Local generation of multineuronal spike sequences in the hippocampal CA1 region

Sequential activity of multineuronal spiking can be observed during theta and high-frequency ripple oscillations in the hippocampal CA1 region and is linked to experience, but the mechanisms underlying such sequences are unknown. We compared multineuronal spiking during theta oscillations, spontaneous ripples, and focal optically induced high-frequency oscillations (“synthetic” ripples) in freely moving mice. Firing rates and rate modulations of individual neurons, and multineuronal sequences of pyramidal cell and interneuron spiking, were correlated during theta oscillations, spontaneous ripples, and synthetic ripples. Interneuron spiking was crucial for sequence consistency. These results suggest that participation of single neurons and their sequential order in population events are not strictly determined by extrinsic inputs but also influenced by local-circuit properties, including synapses between local neurons and single-neuron biophysics.A hypothesized hallmark of cognition is self-organized sequential activation of neuronal assemblies (1). Self-organized neuronal sequences have been observed in several cortical structures (2–5) and neuronal models (6–7). In the hippocampus, sequential activity of place cells (8) may be induced by external landmarks perceived by the animal during spatial navigation (9) and conveyed to CA1 by the upstream CA3 region or layer 3 of the entorhinal cortex (10). Internally generated sequences have been also described in CA1 during theta oscillations in memory tasks (4, 11), raising the possibility that a given neuronal substrate is responsible for generating sequences at multiple time scales. The extensive recurrent excitatory collateral system of the CA3 region has been postulated to be critical in this process (4, 7, 12, 13).The sequential activity of place cells is “replayed” during sharp waves (SPW) in a temporally compressed form compared with rate modulation of place cells (14–20) and may arise from the CA3 recurrent excitatory networks during immobility and slow wave sleep. The SPW-related convergent depolarization of CA1 neurons gives rise to a local, fast oscillatory event in the CA1 region (“ripple,” 140–180 Hz; refs. 8 and 21). Selective elimination of ripples during or after learning impairs memory performance (22–24), suggesting that SPW ripple-related replay assists memory consolidation (12, 13). Although the local origin of the ripple oscillations is well demonstrated (25, 26), it has been tacitly assumed that the ripple-associated, sequentially ordered firing of CA1 neurons is synaptically driven by the upstream CA3 cell assemblies (12, 15), largely because excitatory recurrent collaterals in the CA1 region are sparse (27). However, sequential activity may also emerge by local mechanisms, patterned by the different biophysical properties of CA1 pyramidal cells and their interactions with local interneurons, which discharge at different times during a ripple (28–30). A putative function of the rich variety of interneurons is temporal organization of principal cell spiking (29–32). We tested the “local-circuit” hypothesis by comparing the probability of participation and sequential firing of CA1 neurons during theta oscillations, natural spontaneous ripple events, and “synthetic” ripples induced by local optogenetic activation of pyramidal neurons.     

The hypothalamic link between arousal and sleep homeostasis in mice

Sleep and wakefulness are not simple, homogenous all-or-none states but represent a spectrum of substates, distinguished by behavior, levels of arousal, and brain activity at the local and global levels. Until now, the role of the hypothalamic circuitry in sleep–wake control was studied primarily with respect to its contribution to rapid state transitions. In contrast, whether the hypothalamus modulates within-state dynamics (state “quality”) and the functional significance thereof remains unexplored. Here, we show that photoactivation of inhibitory neurons in the lateral preoptic area (LPO) of the hypothalamus of adult male and female laboratory mice does not merely trigger awakening from sleep, but the resulting awake state is also characterized by an activated electroencephalogram (EEG) pattern, suggesting increased levels of arousal. This was associated with a faster build-up of sleep pressure, as reflected in higher EEG slow-wave activity (SWA) during subsequent sleep. In contrast, photoinhibition of inhibitory LPO neurons did not result in changes in vigilance states but was associated with persistently increased EEG SWA during spontaneous sleep. These findings suggest a role of the LPO in regulating arousal levels, which we propose as a key variable shaping the daily architecture of sleep–wake states.

Interspecies variation in the daily amount of sleep is strongly influenced by genetic factors (1). However, individuals also possess a striking ability to adapt the timing and duration of wakefulness and sleep in response to a variety of intrinsic and extrinsic factors (2). Among the key regulators of “adaptive sleep architecture” are 1) homeostatic sleep need, 2) the endogenous circadian clock, and 3) the necessity to satisfy other physiological and behavioral needs such as feeding or the avoidance of danger (3–5). It is unknown how and in what form these numerous signals are integrated within the neural circuitry that generates the rapid and stable transitions between sleep and wake states.Brain state switching has been the main focus of circuit-oriented sleep research for decades. Early studies identified the preoptic hypothalamus as a primary candidate for a hypothesized “key sleep center” (6–8), and subsequent studies have confirmed the existence of sleep-active neurons in the ventrolateral and median preoptic areas (VLPO and MPO) of the hypothalamus (9–11). Combined with the findings that orexin/hypocretin neurons are necessary to maintain wakefulness (12, 13), a model was proposed in which the sleep/wake-promoting circuitries function as a flip-flop switch (14). This model was able to account for rapid and complete transitions between sleep and wakefulness and preventing state instability (15) or the occurrence of mixed hybrid states of vigilance (16). Over the last decade, our knowledge of subcortical brain nuclei that control sleep has expanded steadily, leading to the identification of functional specialization within the sleep–wake controlling network and, in parallel, highlighting a previously underappreciated complexity (17–35).A key question to emerge is how signals regulating sleep–wake architecture are represented and integrated in hypothalamic state-switching circuitries to ultimately maximize ecological fitness (36). Although sleep homeostasis has been considered an important factor influencing sleep/wake transitions (37–40), relatively few studies have addressed whether and how sleep–wake controlling brain areas overlap with those involved in homeostatic sleep regulation (26) or the underlying neurophysiological mechanisms (41–44). One recent study pointed to an important role of galanin neurons in the lateral preoptic hypothalamus, as was demonstrated through their selective ablation, which abolished the rebound of electroencephalogram (EEG) slow-wave activity (SWA; EEG power density between 0.5 to 4 Hz) after sleep deprivation (26). Other studies suggest that while homeostatic sleep pressure, reflected in SWA, builds up as a function of global wake duration, it is also locally regulated by specific activities during wakefulness (45, 46). The property of sleep and wake as brain states with flexible intensities on a global and local level suggests an additional complexity, which is difficult to reconcile with the existence of a single center solely responsible for complete sleep–wake switching (47). For example, there is evidence to suggest that wake “intensity” contributes to the build-up of global homeostatic sleep need (48–51), and the balance between intrinsic and extrinsic arousal-promoting and sleep-promoting signals ultimately determines the probability and degree of state switching (3, 52).Here, we investigate the role of the hypothalamus in the bidirectional interactions between sleep–wake switching, arousal, and sleep homeostasis. Firstly, we applied optogenetic stimulation of glutamate decarboxylase 2 (GAD2) neurons in the lateral preoptic area (LPO) of mice (17) and found that photoactivation of the LPO during sleep led to rapid wake induction, but this effect was also observed when structures surrounding the LPO were stimulated. Unexpectedly, GAD2^LPO neuronal stimulation did not merely trigger wakefulness, but the awake state produced by this stimulation was characterized by increased EEG theta activity—the established measure of arousal (53, 54). In turn, subsequent sleep was associated with increased levels of EEG SWA, indicative of higher homeostatic sleep pressure (45). In contrast, unilateral inhibition of GAD2^LPO neurons decreased the drive for arousal, as was reflected in a persistent increase in nonrapid eye movement (NREM) EEG SWA across the day. In summary, our experiments demonstrate an important role of GAD2^LPO neurons not only in the control of state transitions but also in linking arousal to sleep homeostasis. We find that the kinetics of the response to photoactivation and photoinhibition were different, and so they may arise from distinct mechanisms while converging on the dynamic modulation of arousal levels, ultimately shaping the daily architecture of sleep–wake states.     

Theta activity paradoxically boosts gamma and ripple frequency sensitivity in prefrontal interneurons

Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input’s statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons’ sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling.

Collective rhythmic activity is implicated in brain functions from sensory information processing to memory consolidation, often with higher-frequency activity bouts locked onto lower frequencies (1–3). While the mechanism behind this cross-frequency coupling is unclear (3), the initiation and maintenance of gamma band (30 to 150 Hz) oscillations are closely associated with fast-spiking (FS) parvalbumin-positive interneurons (4, 5). When driven with frequency chirps, and as a result of intrinsic membrane properties, FS neurons fire more robustly at higher input frequencies than spike-frequency adapting (AD) somatostatin-positive interneurons, which are most responsive to lower frequencies (6). Nevertheless, recent studies strongly suggest that, under certain conditions, somatostatin-positive interneurons are crucial for gamma oscillations (7–9). Could the spectral sensitivity of different interneuron populations perhaps be itself state dependent? Here, we characterized cortical GABAergic interneurons at different in vivo–like working points by measuring their dynamic gain (10–14).Dynamic gain quantifies how input in different frequency bands modulates population firing under in vivo–like conditions of fluctuating background input. To probe the potential impact of different brain states on spectral sensitivity, we used different types of background inputs that mimic the strength and timescales of correlations in background input across brain states (15). We find that both FS and AD interneuron populations can have remarkably wide bandwidths (up to about 500 Hz), making them capable of tracking fast input frequencies well into the range of sharp wave ripples.Moreover, our results uncover unanticipated flexibility in AD neurons, which can massively shift their frequency preference, specifically engaging or disengaging with high-frequency rhythms, such as gamma and sharp wave ripples. The presence or absence of slowly correlated input drives this sensitivity shift, which can occur within 50 ms, in phase with an ongoing slow rhythm. This observation offers a mechanistic explanation for theta-gamma cross-frequency coupling.     

Orexin/hypocretin system modulates amygdala-dependent threat learning through the locus coeruleus

Survival in a dangerous environment requires learning about stimuli that predict harm. Although recent work has focused on the amygdala as the locus of aversive memory formation, the hypothalamus has long been implicated in emotional regulation, and the hypothalamic neuropeptide orexin (hypocretin) is involved in anxiety states and arousal. Nevertheless, little is known about the role of orexin in aversive memory formation. Using a combination of behavioral pharmacology, slice physiology, and optogenetic techniques, we show that orexin acts upstream of the amygdala via the noradrenergic locus coeruleus to enable threat (fear) learning, specifically during the aversive event. Our results are consistent with clinical studies linking orexin levels to aversive learning and anxiety in humans and dysregulation of the orexin system may contribute to the etiology of fear and anxiety disorders.Hess and Akert demonstrated that electrical stimulation of the perifornical (PFH) region of the hypothalamus elicits defensive or aggressive responses in cats (1). Others showed that hypothalamic stimulation can serve as the aversive unconditioned stimulus (US) (2), indicating that the hypothalamus processes threat information important for aversive learning. One possibility is that orexin neurons, which populate these hypothalamic areas, may mediate these observed responses, as these neurons project to and modulate brain areas critical for threat processing, reward, and memory.Orexins are neuropeptides produced in the PFH and lateral regions of the hypothalamus (LH) (3, 4). Two orexin peptides (Orexin-A and Orexin-B) are processed from one peptide precursor (prepro-orexin) and bind two distinct G protein–coupled receptors (OrxR1 and OrxR2) in the brain (3, 4). Activation of either receptor commonly increases excitability in target neurons by reducing potassium channel conductance, enhancing presynaptic glutamate release, or increasing postsynaptic NMDA receptor (NMDAR) conductance (5, 6). Orexin receptors are differentially distributed in the brain and may serve differing roles in stress, arousal, vigilance, feeding, reward processing, and drug addiction (7–10). Evidence suggests that, in general, OrxR2 is involved in maintenance of arousal or wakefulness (11, 12), whereas OrxR1 mediates responses to environmental stimuli (13, 14).Recent reports point to a role for the orexin system in emotional regulation. Overactivity in orexin neurons can exacerbate panic-like episodes and lead to an anxiety-like phenotype in rats (15, 16). Conversely, administration of the dual orexin receptor antagonist almorexant blunts autonomic and behavioral responses affiliated with heightened stress levels (17, 18). Although orexin system activity is linked to general states of hyperarousal, the precise role of orexin in these and other aversive states remains unknown.Hypothalamic orexin neurons send a dense output to the locus coeruelus (LC) and depolarize neurons in vitro and in vivo (19–21). In line with their connectivity, LC neurons respond to phasic stimuli in a manner comparable to orexin neurons (22), suggesting that orexin neurons modulate LC responses to salient sensory events. Interestingly, orexin and LC neurons are both activated by aversive stimuli such as shock (23, 24). Thus, orexin could contribute to aversive learning by way of LC, given the importance of norepinephrine to aversive memory processes in amygdala (25–27).Pavlovian threat (fear) conditioning is a well-established behavioral paradigm to assess the formation, storage, and expression of aversive memories (28). During training, animals learn to associate an aversive US, such as a footshock, with a neutral conditioned stimulus (CS), such as a tone, when both occur in close temporal proximity. Here, we tested the hypothesis that orexin neurons phasically activate locus coeruleus neurons during an aversive event to enable threat learning. Using a combination of behavioral pharmacology, electrophysiology, and optogenetic approaches, we show that orexin neurons, via activation of OrxR1 in the LC, facilitate the acquisition of amygdala-dependent threat memory.     

Local dendritic balance enables learning of efficient representations in networks of spiking neurons

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory–inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

Many neural systems have to encode high-dimensional and complex input signals in their activity. It has long been hypothesized that these encodings are highly efficient; that is, neural activity faithfully represents the input while also obeying energy and information constraints (1–3). For populations of spiking neurons, such an efficient code requires two central features: First, neural activity in the population has to be coordinated, such that no spike is fired superfluously (4); second, individual neural activity should represent reoccurring patterns in the input signal, which reflect the statistics of the sensory stimuli (2, 3). How can this coordination and these efficient representations emerge through local plasticity rules?To coordinate neural spiking, choosing the right recurrent connections between coding neurons is crucial. In particular, recurrent connections have to ensure that neurons do not spike redundantly to encode the same input. An early result was that in unstructured networks the redundancy of spiking is minimized when excitatory and inhibitory currents cancel on average in the network (5–7), which is also termed loose global excitatory–inhibitory (E-I) balance (8). To reach this state, recurrent connections can be chosen randomly with the correct average magnitude, leading to asynchronous and irregular neural activity (5) as observed in vivo (4, 9). More recently, it became clear that recurrent connections can ensure a much more efficient encoding when E-I currents cancel not only on average, but also on fast timescales and in individual neurons (4), which is also termed tight detailed E-I balance (8). Here, recurrent connections have to be finely tuned to ensure that the network response to inputs is precisely distributed over the population. To achieve this intricate recurrent connectivity, different local plasticity rules have been proposed, which robustly converge to a tight balance and thereby ensure that networks spike efficiently in response to input signals (10, 11).To efficiently encode high-dimensional input signals, it is additionally important that neural representations are adapted to the statistics of the input. Often, high-dimensional signals contain redundancies in the form of reoccurring spatiotemporal patterns, and coding neurons can reduce activity by representing these patterns in their activity. For example, in an efficient code of natural images, the activity of neurons should represent the presence of edges, which are ubiquitous in these images (3). Early studies of recurrent networks showed that such efficient representations can be found through Hebbian-like learning of feedforward weights (12, 13). With Hebbian learning the repeated occurrence of patterns in the input is associated with postsynaptic activity, causing neurons to become detectors of these patterns. Recurrent connections indirectly guide this learning process by forcing neurons to fire for distinct patterns in the input. Recent efforts rigorously formalized this idea for models of spiking neurons in balanced networks (11) and spiking neuron sampling from generative models (14–17). The great strength of these approaches is that the learning rules can be derived from first principles and turn out to be similar to spike-timing–dependent plasticity (STDP) curves that have been measured experimentally.However, to enable the learning of efficient representations, these models have strict requirements on network dynamics. Most crucially, recurrent inhibition has to ensure that neural responses are sufficiently decorrelated. In the neural sampling approaches, learning therefore relies on strong winner-take-all dynamics (14–17). In the framework of balanced networks, transmission of inhibition has to be nearly instantaneous to ensure strong decorrelation (18). These requirements are likely not met in realistic situations, where neural activity often shows positive correlations (19–22).We here derive a learning scheme that overcomes these limitations. First, we show that when neural activity is correlated, learning of feedforward connections has to incorporate nonlocal information about the activity of other neurons. Second, we show that recurrent connections can provide this nonlocal information by learning to locally balance specific feedforward inputs on the dendrites. In simulations of spiking neural networks we demonstrate the benefits of learning with dendritic balance over Hebbian-like learning for the efficient encoding of high-dimensional signals. Hence, we extend the idea that tightly balancing inhibition provides information about the population code locally and show that it can be used not only to distribute neural responses over a population, but also for an improved learning of feedforward weights.     

Stochastic transitions into silence cause noise correlations in cortical circuits

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.Neuronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally, they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pairwise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings, however, challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4–6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11–13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). These observations suggest that correlations reflect the dynamical state of the circuit more than its hardwired connectivity.Despite substantial progress in understanding the mechanisms giving rise to large individual variability in recurrent networks (9, 16–18), we still lack a canonical model that can generate correlations with the same magnitude and spatiotemporal structure as those observed in cortical circuits. Balanced networks, for instance, a common model that reproduces the large variability of cortical neurons (9, 18, 19), show near-zero averaged correlations (9). Numerous studies have investigated the generation of synchronous firing (20), but whether short bursts of population activity can quantitatively account for the spike count correlations found in the data is unclear. Recurrent networks can also generate fast oscillations in the population activity, but, in a regime of low rates, typical of cortical circuits, average spike count correlations are negligible (21). Network models producing nonzero average correlations are those exhibiting up and down dynamics (22–29). Most of these studies have focused on investigating the mechanisms underlying the slow oscillatory activity observed in cortical slices (30), under anesthesia (31, 32), or during slow-wave sleep (33). Only recently the impact of up and down switching on trial-to-trial response variability (25) and on the probability distribution of multiunit activity (29) across brain states has been investigated. Whether the alternation between up and down phases could quantitatively account for the pairwise correlations observed in different brain states and describe their stimulus-evoked dynamics remains an open question.To investigate the mechanisms producing correlated firing, we recorded the spiking activity of large populations of neurons from the auditory cortex of anesthetized rats. During spontaneous activity, changes in correlation were largely explained by variation of the occurrence rate of periods during which neurons in the circuit stopped firing. Furthermore, the time course of correlation in response to an acoustic stimulus reflected the transient variation of this silence density. A computational rate model with fluctuation-driven transitions between silent and active attractors could explain the experimentally observed time course of correlation and its relation to silence density. Our findings suggest that the dynamics of these transitions play a fundamental role in generating noise correlations among cortical neurons.     

CRYPTOCHROME-mediated phototransduction by modulation of the potassium ion channel β-subunit redox sensor

Blue light activation of the photoreceptor CRYPTOCHROME (CRY) evokes rapid depolarization and increased action potential firing in a subset of circadian and arousal neurons in Drosophila melanogaster. Here we show that acute arousal behavioral responses to blue light significantly differ in mutants lacking CRY, as well as mutants with disrupted opsin-based phototransduction. Light-activated CRY couples to membrane depolarization via a well conserved redox sensor of the voltage-gated potassium (K⁺) channel β-subunit (Kvβ) Hyperkinetic (Hk). The neuronal light response is almost completely absent in hk^−/− mutants, but is functionally rescued by genetically targeted neuronal expression of WT Hk, but not by Hk point mutations that disable Hk redox sensor function. Multiple K⁺ channel α-subunits that coassemble with Hk, including Shaker, Ether-a-go-go, and Ether-a-go-go–related gene, are ion conducting channels for CRY/Hk-coupled light response. Light activation of CRY is transduced to membrane depolarization, increased firing rate, and acute behavioral responses by the Kvβ subunit redox sensor.CRYPTOCHROME (CRY) is a photoreceptor that mediates rapid membrane depolarization and increased spontaneous action potential firing rate in response to blue light in arousal and circadian neurons in Drosophila melanogaster (1, 2). CRY regulates circadian entrainment by targeting circadian clock proteins to proteasomal degradation in response to light (3–6). CRY is expressed in a small subset of central brain circadian, arousal, and photoreceptor neurons in D. melanogaster and other insects, including the large lateral ventral neuron (LNv; l-LNv) subset (1, 2, 7, 8). The l-LNvs are light-activated arousal neurons (1, 2, 9–11), whereas the small lateral ventral neurons (s-LNvs) are critical for circadian function (5, 12). Previous results suggest that light activated arousal is likely attenuated in cry-null mutants. In addition to modulating light-activated firing rate, membrane excitability in the LNv neurons helps maintain circadian rhythms (9, 13, 14), and LNv firing rate is circadian regulated (2, 16).Based on our previous work suggesting that l-LNv electrophysiological light response requires a flavin-specific redox reaction and modulation of membrane K⁺ channels, we investigated the molecular mechanism for CRY phototransduction to determine how light-activated CRY is coupled to rapid membrane electrical changes. Sequence and structural data suggest that the cytoplasmic Kvβs are redox sensors based on a highly conserved aldo-keto-reductase domain (AKR) (17–21). Although no functional role for redox sensing by Kvβ subunits has been established yet in vivo, studies with heterologously expressed WT and mutant Kvβ subunits show that they confer modulatory sensitivity to coexpressed K⁺ channels in response to oxidizing and reducing chemical agents (22–24). Mammals express six Kvβ genes, whereas Drosophila expresses a single Kvβ designated HYPERKINETIC (Hk) (18). We find that the light-activated redox reaction of the flavin adenine dinucleotide (FAD) chromophore in CRY has a distinct phototransduction mechanism that evokes membrane electrical responses via the Kvβ subunit Hk, which we show is a functional redox sensor in vivo.     

Interaction between decision-making and interoceptive representations of bodily arousal in frontal cortex

Decision-making and representations of arousal are intimately linked. Behavioral investigations have classically shown that either too little or too much bodily arousal is detrimental to decision-making, indicating that there is an inverted “U” relationship between bodily arousal and performance. How these processes interact at the level of single neurons as well as the neural circuits involved are unclear. Here we recorded neural activity from orbitofrontal cortex (OFC) and dorsal anterior cingulate cortex (dACC) of macaque monkeys while they made reward-guided decisions. Heart rate (HR) was also recorded and used as a proxy for bodily arousal. Recordings were made both before and after subjects received excitotoxic lesions of the bilateral amygdala. In intact monkeys, higher HR facilitated reaction times (RTs). Concurrently, a set of neurons in OFC and dACC selectively encoded trial-by-trial variations in HR independent of reward value. After amygdala lesions, HR increased, and the relationship between HR and RTs was altered. Concurrent with this change, there was an increase in the proportion of dACC neurons encoding HR. Applying a population-coding analysis, we show that after bilateral amygdala lesions, the balance of encoding in dACC is skewed away from signaling either reward value or choice direction toward HR coding around the time that choices are made. Taken together, the present results provide insight into how bodily arousal and decision-making are signaled in frontal cortex.

Our current bodily state, whether it be thirst or a racing heart, affects ongoing cognitive processes. Bodily arousal is fundamental to representations of our bodily state and can have a marked influence on decision-making (1–3). At moderate levels, bodily arousal can increase the chance of survival by invigorating responding, whereas at higher levels, it promotes defensive behaviors such as freezing when threat of predation is imminent (4, 5). Consequently, altered generation and assessment of bodily arousal is thought to contribute to a host of psychiatric disorders such as anxiety disorders and addiction (6–8).The influence of bodily arousal on behavior can be accounted for by viscerosensory feedback from the body reaching the brain. Central representations of current bodily state including arousal are known as interoception, and these representations are thought to be essential for maintaining homeostasis (9). Several brain areas including the dorsal anterior cingulate cortex (dACC), orbitofrontal cortex (OFC), anterior insular cortex, hypothalamus, and amygdala are implicated in interoception, signaling bodily arousal as well as other aspects of physiological state, such as hydration and temperature (10–13). The network of areas spanning frontal and limbic structures highlighted previously as central to interoception overlaps extensively with the parts of the brain that are essential for reward-guided decision-making, and these shared neural substrates are likely where these two processes interact (14, 15). Notably, lesions or dysfunction within frontal cortex and limbic areas in either humans or monkeys is associated with altered bodily arousal, interoception, and decision-making (16–18). Thus, optimal levels of bodily arousal are essential for appropriate responding to appetitive or aversive stimuli and likely require flexible adjustment of population-level neural representation in frontal and limbic structures (19).Despite the appreciation that limbic and frontal structures are critical to both decision-making and interoception, how these processes interact in the frontal cortex at the level of single neurons is poorly understood. This is because single-neuron investigations of choice behavior have rarely considered or even attempted to measure the influence of bodily arousal on decision-making. Even less certain is how heightened states of bodily arousal affect interoceptive representations at the level of single neurons and subsequently influence choice behavior.To address this, we analyzed a rare dataset: electrocardiogram (ECG) data were recorded simultaneously with single-neuron recordings in OFC and dACC in macaque monkeys performing a reward-guided task both before and after excitotoxic lesions of the amygdala (20). In this previous study, we demonstrated that the decisions of monkeys were guided by the reward size associated with each option. In addition, we found that the reaction times (RTs) to choose rewarded options reflected the expected amount of reward. Correspondingly, the activity of a large proportion of single neurons in OFC and dACC preferentially encoded reward value. Here, our aim was to examine the potential interaction between factors that guide decision-making on a trial-by-trial basis (i.e., reward value and choice direction) and representations of bodily arousal in frontal cortex. For this purpose, we define heart rate (HR) as bodily arousal and its neural representation as interoception. The HR during the fixation period of each trial (baseline HR) was used as a proxy of the current bodily arousal (21). Selective lesions of the amygdala caused a tonic increase in baseline HR, which was seen both during reward-guided behavior (22) and at rest. Here, we report that this increase in HR altered the influence of bodily arousal on decision-making, whereby heightened bodily arousal was associated with slower responding. At the same time, single neuron correlates of baseline HR increased in dACC, but not OFC, after amygdala lesions, altering the balance of coding away from decision-relevant processes and toward representations of bodily arousal. Taken together, this pattern of results suggests that bilateral amygdala lesions caused a state of hyperarousal, which impacts decision-making through adjustments in population coding in dACC.     

Measuring the signal-to-noise ratio of a neuron

The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ² random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are −10 dB to −3 dB for guinea pig auditory cortex neurons, −18 dB to −7 dB for rat thalamic neurons, −28 dB to −14 dB for monkey hippocampal neurons, and −29 dB to −20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron’s spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.The signal-to-noise ratio (SNR), defined as the amplitude squared of a signal or the signal variance divided by the variance of the system noise, is a widely applied measure for quantifying system fidelity and for comparing performance among different systems (1–4). Commonly expressed in decibels as 10log₁₀(SNR), the higher the SNR, the stronger the signal or information in the signal relative to the noise or distortion. Use of the SNR is most appropriate for systems defined as deterministic or stochastic signals plus Gaussian noise (2, 4). For the latter, the SNR can be computed in the time or frequency domain.Use of the SNR to characterize the fidelity of neural systems is appealing because information transmission by neurons is a noisy stochastic process. However, the standard concept of SNR cannot be applied in neuronal analyses because neurons transmit both signal and noise primarily in their action potentials, which are binary electrical discharges also known as spikes (5–8). Defining what is the signal and what is the noise in neural spiking activity is a challenge because the putative signals or stimuli for neurons differ appreciably among brain regions and experiments. For example, neurons in the visual cortex and in the auditory cortex respond respectively to features of light (9) and sound stimuli (10) while neurons in the somatosensory thalamus respond to tactile stimuli (11). In contrast, neurons in the rodent hippocampus respond robustly to the animal’s position in its environment (11, 12), whereas monkey hippocampal neurons respond to the process of task learning (13). As part of responding to a putative stimulus, a neuron’s spiking activity is also modulated by biophysical factors such as its absolute and relative refractory periods, its bursting propensity, and local network and rhythm dynamics (14, 15). Hence, the definition of SNR must account for the extent to which a neuron’s spiking responses are due to the applied stimulus or signal and to these intrinsic biophysical properties.Formulations of the SNR for neural systems have been studied. Rieke et al. (16) adapted information theory measures to define Gaussian upper bounds on the SNR for individual neurons. Coefficients of variation and Fano factors based on spike counts (17–19) have been used as measures of SNR. Similarly, Gaussian approximations have been used to derive upper bounds on neuronal SNR (16). These approaches do not consider the point process nature of neural spiking activity. Moreover, these measures and the Gaussian approximations are less accurate for neurons with low spike rates or when information is contained in precise spike times.Lyamzin et al. (20) developed an SNR measure for neural systems using time-dependent Bernoulli processes to model the neural spiking activity. Their SNR estimates, based on variance formulae, do not consider the biophysical properties of the neuron and are more appropriate for Gaussian systems (16, 21, 22). The Poisson regression model used widely in statistics to relate count observations to covariates provides a framework for studying the SNR for non-Gaussian systems because it provides an analog of the square of the multiple correlation coefficient (R²) used to measure goodness of fit in linear regression analyses (23). The SNR can be expressed in terms of the R² for linear and Poisson regression models. However, this relationship has not been exploited to construct an SNR estimate for neural systems or point process models. Finally, the SNR is a commonly computed statistic in science and engineering. Extending this concept to non-Gaussian systems would be greatly aided by a precise statement of the theoretical quantity that this statistic estimates (24, 25).We show that the SNR estimates a ratio of expected prediction errors (EPEs). Using point process generalized linear models (PP-GLM), we extend the standard definition to one appropriate for single neurons recorded in stimulus−response experiments. In analyses of four neural systems, we show that single-neuron SNRs range from −29 dB to −3 dB and that spiking history is often a more informative predictor of spiking propensity than the signal being represented. Our new SNR definition generalizes to any problem in which the modulatory components of a system’s output can be expressed as separate components of a GLM.     

Direct numerical simulations of aeolian sand ripples

Aeolian sand beds exhibit regular patterns of ripples resulting from the interaction between topography and sediment transport. Their characteristics have been so far related to reptation transport caused by the impacts on the ground of grains entrained by the wind into saltation. By means of direct numerical simulations of grains interacting with a wind flow, we show that the instability turns out to be driven by resonant grain trajectories, whose length is close to a ripple wavelength and whose splash leads to a mass displacement toward the ripple crests. The pattern selection results from a compromise between this destabilizing mechanism and a diffusive downslope transport which stabilizes small wavelengths. The initial wavelength is set by the ratio of the sediment flux and the erosion/deposition rate, a ratio which increases linearly with the wind velocity. We show that this scaling law, in agreement with experiments, originates from an interfacial layer separating the saltation zone from the static sand bed, where momentum transfers are dominated by midair collisions. Finally, we provide quantitative support for the use of the propagation of these ripples as a proxy for remote measurements of sediment transport.Observers have long recognized that wind ripples (1, 2) do not form via the same dynamical mechanism as dunes (3). Current explanations ascribe their emergence to a geometrical effect of solid angle acting on sediment transport. The motion of grains transported in saltation is composed of a series of asymmetric trajectories (4–7) during which they are accelerated by the wind. These grains, in turn, decelerate the airflow inside the transport layer (1, 7–12). On hitting the sand bed, they release a splash-like shower of ejected grains that make small hops from the point of impact (1, 13, 14). This process is called reptation. Previous wind ripple models assume that saltation is insensitive to the sand bed topography and forms a homogeneous rain of grains approaching the bed at a constant oblique angle (15–20). Upwind-sloping portions of the bed would then be submitted to a higher impacting flux than downslopes (1). With a number of ejecta proportional to the number of impacting grains, this effect would produce a screening instability with an emergent wavelength λ determined by the typical distance over which ejected grains are transported (15–17), a few grain diameters d. However, observed sand ripple wavelengths are about 1,000 times larger than d, on Earth. The discrepancy is even more pronounced on Mars, where regular ripples are 20–40 times larger than those on a typical Earth sand dune (21, 22). Moreover, the screening scenario predicts a wavelength independent of the wind shear velocity u_?, in contradiction with field and wind tunnel measurements that exhibit a linear dependence of λ with u_? (23–25).     

A functional division of Drosophila sweet taste neurons that is value-based and task-specific

Sucrose is an attractive feeding substance and a positive reinforcer for Drosophila. But Drosophila females have been shown to robustly reject a sucrose-containing option for egg-laying when given a choice between a plain and a sucrose-containing option in specific contexts. How the sweet taste system of Drosophila promotes context-dependent devaluation of an egg-laying option that contains sucrose, an otherwise highly appetitive tastant, is unknown. Here, we report that devaluation of sweetness/sucrose for egg-laying is executed by a sensory pathway recruited specifically by the sweet neurons on the legs of Drosophila. First, silencing just the leg sweet neurons caused acceptance of the sucrose option in a sucrose versus plain decision, whereas expressing the channelrhodopsin CsChrimson in them caused rejection of a plain option that was “baited” with light over another that was not. Analogous bidirectional manipulations of other sweet neurons did not produce these effects. Second, circuit tracing revealed that the leg sweet neurons receive different presynaptic neuromodulations compared to some other sweet neurons and were the only ones with postsynaptic partners that projected prominently to the superior lateral protocerebrum (SLP) in the brain. Third, silencing one specific SLP-projecting postsynaptic partner of the leg sweet neurons reduced sucrose rejection, whereas expressing CsChrimson in it promoted rejection of a light-baited option during egg-laying. These results uncover that the Drosophila sweet taste system exhibits a functional division that is value-based and task-specific, challenging the conventional view that the system adheres to a simple labeled-line coding scheme.

The taste systems of many animal species are known to possess a dedicated “channel” for detecting sugars, a class of chemicals that is highly nutritious. For example, mice have been shown to encode gustatory receptors that specifically sense sugars, and the taste neurons that express these sugar receptors on their tongues generally do not express receptors that sense chemicals of another taste modality (e.g., bitterness) (1–3). Furthermore, activation of these sugar-sensing taste neurons by artificial means has been shown to be able to drive appetitive sugar-induced innate responses (e.g., licking) and act as a positive reinforcer for learning (3–5). In some recent studies, these properties of the sweet taste neurons have been found to be present in some of their central nervous system (CNS) targets (e.g., taste-sensitive neurons in the insular cortex), too (6, 7). Thus, one school of thought is that taste coding for sweetness in mice may follow the simple “labeled-line” rule: sweet taste neurons, and potentially some of their central targets, are hardwired to detect sugars specifically and drive sugar-induced reinforcing neural signals and appetitive behaviors (1–7).Drosophila melanogaster also possess sugar-detecting taste neurons. Pioneering early studies have shown that sugar-sensing taste neurons in flies are molecularly, anatomically, and functionally distinct from taste neurons that sense bitterness; sweet-sensing and bitter-sensing taste neurons express different gustatory receptors, project axons to different areas in the brain, and are required to promote different (appetitive versus aversive) behaviors (8–12). Moreover, the activation of sweet neurons by artificial means can drive appetitive behaviors and act as a positive reinforcer for learning (10, 13, 14), while artificial activation of bitter-sensing neurons can induce rejection behaviors and be used as a punishment for learning (10, 13, 15). Interestingly, while these results suggest that Drosophila sweet neurons and their mammalian counterparts have some shared properties, subsequent studies suggest that significant differences exist between them, too. First, the Drosophila genome appears to encode many more sweet receptors than mouse genome does (12, 16–23). Second, Drosophila sweet neurons appear to be able to detect some chemicals that belong to another taste modality [e.g., acetic acid (AA)] (24–27). Third, Drosophila sweet neurons can be found on several body parts (e.g., proboscis and legs) (8, 12, 18, 20, 23, 28–30). Interestingly, sweet neurons on different body parts of Drosophila do not promote identical behavioral outputs (8, 20, 23, 24, 28, 29). For example, labellar sweet neurons and esophageal sweet neurons on the proboscis have been shown to promote proboscis extension reflex (PER) and ingestion, respectively, whereas leg sweet neurons have been shown to promote PER and slowing down of locomotion (8, 12, 28, 29). Collectively, these results suggest that in contrast to the apparent homogeneity of sweet neurons in some mammals, a functional division exists among Drosophila sweet neurons, although the different behavioral responses promoted by different Drosophila sweet neurons generally appear appetitive in nature.In this work, we report yet another striking feature of Drosophila sweet neurons that sets them apart from their mammalian counterparts, namely a functional division that is value-based and task-specific. We discovered this by taking advantage of a context-dependent but highly robust sugar rejection behavior exhibited by egg-laying females (31–34). Previous studies have shown that when selecting for egg-laying site in a small enclosure (dimension ∼16 × 10 × 18 mm), Drosophila readily accept a sucrose-containing agarose for egg-laying when it is the sole option but strongly reject it when a plain option is also available (31, 32). Importantly, silencing their sweet neurons causes the females to no longer reject the sucrose option when choosing between the sucrose versus plain options (31, 32). Thus, in addition to promoting appetitive behaviors and acting as a positive reinforcer, activation of sweet neurons on an egg-laying option can also decrease the value of such an option (thereby causing its rejection over an option that does not activate sweet neurons). These observations not only suggest the existence of an apparent “antiappetitive” role of Drosophila sweet neurons when the task of animals is to select for egg-laying sites but also raise a key question as to whether such counterintuitive, value-decreasing property of sweetness detection during egg-laying may be 1) solely an emergent property of specific neurons in the brain that respond similarly to all peripheral sweet neurons but are sensitive to animals’ behavioral goal and context or 2) carried out by specific sweet neurons at the periphery and then transmitted into the brain via a unique neural pathway activated by these neurons. To disambiguate between these possibilities, we genetically targeted different subsets of sweet neurons to assess their circuit properties as well as their behavioral roles as the animals decided in either a regular or a virtual sweet versus plain decision during egg-laying, taking advantage of a high-throughput closed-loop optogenetic stimulation platform we developed recently. Our collective results support the second scenario and suggest that the value-decreasing property of sweetness/sucrose is conveyed specifically by the sweet neurons on the legs of Drosophila—and not by other sweet neurons—and the unique postsynaptic target(s) of the leg sweet neurons that send long-range projections to the superior lateral protocerebrum (SLP) in the brain. These results reveal a previously unappreciated functional and anatomical division of the Drosophila sweet taste neurons that is both task-specific and value-based, pointing to a level of complexity and sophistication that seems unmatched by their mammalian counterparts so far.     

Optogenetic stimulation of glutamatergic neurons in the cuneiform nucleus controls locomotion in a mouse model of Parkinson’s disease

In Parkinson’s disease (PD), the loss of midbrain dopaminergic cells results in severe locomotor deficits, such as gait freezing and akinesia. Growing evidence indicates that these deficits can be attributed to the decreased activity in the mesencephalic locomotor region (MLR), a brainstem region controlling locomotion. Clinicians are exploring the deep brain stimulation of the MLR as a treatment option to improve locomotor function. The results are variable, from modest to promising. However, within the MLR, clinicians have targeted the pedunculopontine nucleus exclusively, while leaving the cuneiform nucleus unexplored. To our knowledge, the effects of cuneiform nucleus stimulation have never been determined in parkinsonian conditions in any animal model. Here, we addressed this issue in a mouse model of PD, based on the bilateral striatal injection of 6-hydroxydopamine, which damaged the nigrostriatal pathway and decreased locomotor activity. We show that selective optogenetic stimulation of glutamatergic neurons in the cuneiform nucleus in mice expressing channelrhodopsin in a Cre-dependent manner in Vglut2-positive neurons (Vglut2-ChR2-EYFP mice) increased the number of locomotor initiations, increased the time spent in locomotion, and controlled locomotor speed. Using deep learning-based movement analysis, we found that the limb kinematics of optogenetic-evoked locomotion in pathological conditions were largely similar to those recorded in intact animals. Our work identifies the glutamatergic neurons of the cuneiform nucleus as a potentially clinically relevant target to improve locomotor activity in parkinsonian conditions. Our study should open avenues to develop the targeted stimulation of these neurons using deep brain stimulation, pharmacotherapy, or optogenetics.

In Parkinson’s disease (PD), midbrain dopaminergic (DA) cells are lost, resulting in motor dysfunction, including severe locomotor deficits (e.g., gait freezing, akinesia, and falls) (1). Growing evidence indicates that part of these deficits can be attributed to changes in the mesencephalic locomotor region (MLR) (refs. 2–7; for review, see ref. 8). This brainstem region plays a key role in locomotor control by sending projections to reticulospinal neurons that carry the locomotor drive to the spinal cord in vertebrates (lamprey: refs. 9 and 10; salamander: refs. 11 and 12; and mouse: refs. 13 to 19; for review, see ref. 20). The DA neurons of the substantia nigra pars compacta (SNc) indirectly control MLR activity through the basal ganglia (15, 21, 22). In parallel, the MLR receives direct DA projections from the SNc (23–26) and from the zona incerta (27). The DA innervation of the MLR degenerates in a monkey model of PD (28). Therefore, the loss of DA cells in PD has major effects on MLR activity. In PD, locomotor deficits are associated with MLR cell loss, abnormal neural activity, altered connectivity, and metabolic deficits, likely resulting in a loss of amplification of the locomotor commands (for review, see ref. 8). Accordingly, motor arrests and gait freezing are associated with a decrease in MLR activity in PD (ref. 29; for review, see ref. 8).One approach to improve locomotor function in PD would be to increase MLR activity. L-DOPA, the gold standard drug used to improve motor symptoms in PD, increases MLR activity and this likely contributes to the locomotor benefits (30). The MLR has been proposed to contribute to the locomotor benefits of deep brain stimulation (DBS) of the subthalamic nucleus (31–33), which has direct and indirect projections to the MLR (refs. 34, 35; for review, see ref. 20). However, the benefits of L-DOPA and subthalamic DBS on locomotor deficits may wane over time, highlighting the need to find new therapeutic approaches (36, 37). Since 2005, the MLR has been explored as a DBS target (38). The results vary, from modest to promising (39, 40). However, the best target in the MLR in PD conditions is not yet identified. The MLR is a heterogeneous structure, with the cuneiform nucleus (CnF) controlling the largest range of locomotor speeds, and the pedunculopontine nucleus (PPN) controlling slow speeds, posture and in some cases locomotor arrests (refs. 15, 17–19; for review, see ref. 20). Human DBS protocols targeted the PPN, but left the CnF unexplored, despite its major importance in locomotor control in animal research (19, 41, 42). In humans, a recent anatomical analysis of DBS electrode position relative to the pontomesencephalic junction suggests that some of the beneficial effects attributed to the PPN could be due to CnF activation (43).To add further complexity, three main cell types are present in the MLR: glutamatergic, GABAergic, and cholinergic cells. It is still unknown which cell type is the best target to improve locomotor function in PD conditions. Optogenetic studies uncovered that glutamatergic cells in the CnF play a key role in generating the locomotor drive for a wide range of speeds (15, 17–19). Glutamatergic cells of the PPN control slower speeds (17, 18) and, in some cases, evoke locomotor arrests (18, 44, 45). The GABAergic cells in the CnF and PPN stop locomotion likely by inhibiting glutamatergic cells (15, 17). The role of the PPN cholinergic cells is not resolved, as their activation can increase or decrease locomotion (refs. 15, 17, 18; for review, see ref. 20). Clinically, DBS likely stimulates all cells around the electrode, including the GABAergic cells that stop locomotion, and this could contribute to the variability of outcomes.Here, we aimed at identifying a relevant target in the MLR to improve the locomotor function in parkinsonian conditions. We hypothesized that the selective activation of CnF glutamatergic neurons should improve locomotor function in a mouse model of PD. We induced parkinsonian conditions in mice by bilaterally injecting into the striatum the neurotoxin 6-hydroxydopamine (6-OHDA), which is well known to damage the nigrostriatal DA pathway and to induce a dramatic decrease in locomotor activity (e.g., refs. 22 and 46). Using in vivo optogenetics in mice expressing channelrhodopsin in a Cre-dependent manner in Vglut2-positive neurons (Vglut2-ChR2-EYFP mice), we show that the photostimulation of glutamatergic neurons in the CnF robustly initiated locomotion, reduced immobility, increased the time spent in locomotion, and precisely controlled locomotor speed. Our results should help in defining therapeutic strategies aimed at specifically activating CnF glutamatergic neurons to improve locomotor function in PD using optimized DBS protocols, pharmacotherapy, or future optogenetic tools for human use.     

Electrical synapses connect a network of gonadotropin releasing hormone neurons in a cichlid fish

Initiating and regulating vertebrate reproduction requires pulsatile release of gonadotropin-releasing hormone (GnRH1) from the hypothalamus. Coordinated GnRH1 release, not simply elevated absolute levels, effects the release of pituitary gonadotropins that drive steroid production in the gonads. However, the mechanisms underlying synchronization of GnRH1 neurons are unknown. Control of synchronicity by gap junctions between GnRH1 neurons has been proposed but not previously found. We recorded simultaneously from pairs of transgenically labeled GnRH1 neurons in adult male Astatotilapia burtoni cichlid fish. We report that GnRH1 neurons are strongly and uniformly interconnected by electrical synapses that can drive spiking in connected cells and can be reversibly blocked by meclofenamic acid. Our results suggest that electrical synapses could promote coordinated spike firing in a cellular assemblage of GnRH1 neurons to produce the pulsatile output necessary for activation of the pituitary and reproduction.Development and function of the reproductive system in vertebrates depends on the timing and levels of signaling by gonadal sex steroids (1, 2). Production of these steroids is controlled by neurons expressing gonadotropin-releasing hormone (GnRH1), which comprise the final output of the brain to the hypothalamic-pituitary-gonadal axis. During vertebrate development, GnRH1 neurons originate outside the central nervous system in the olfactory placode and migrate into the basal forebrain (3–6). These neurons signal to the pituitary via the decapeptide GnRH1 to effect the release of the gonadotropins, follicle stimulating hormone and luteinizing hormone, which in turn stimulate steroid production by the gonads. It has long been known that this release depends on coordinated, pulsatile GnRH1 release, not simply elevated levels (7, 8), requiring some level of synchronization in the output of these neurons. Episodic activation of the pituitary gonadotropes has been observed in multiple vertebrate taxa, including mammals and fish (9–12), however, mechanisms that underlie this required coordinated activity of GnRH1 neurons are unknown. Synchrony could in principle derive from coincident input from a “pacemaker” neural population, from direct coupling of GnRH1 neurons, or from a combination of mechanisms. Gap junction-mediated coupling has been suspected to play a role, as synchronous firing can be observed in neurons mechanically isolated from brain slices and in cultures of embryonic mouse and primate neurons, and immortalized mouse GnRH1 neurons express the connexin proteins that constitute gap junctions (13–15). However, no evidence for gap junctions among adult GnRH1 cells in vivo has been found (16, 17).To search for the origin of synchrony among these neurons, we used a unique model system for analysis of GnRH1 neurons, Astatotilapia burtoni, a cichlid fish. GnRH1 neurons in males of this species exhibit dynamic morphological plasticity caused by changes in their social status (18–21). Here we use transgenic dominant male A. burtoni to perform paired recordings from GnRH1 neurons, and report that they are reciprocally connected by electrical synapses. These findings suggest that gap junctions contribute to the coordinated firing of these neurons necessary for reproductive function.     

Collapse of complexity of brain and body activity due to excessive inhibition and MeCP2 disruption

Complex body movements require complex dynamics and coordination among neurons in motor cortex. Conversely, a long-standing theoretical notion supposes that if many neurons in motor cortex become excessively synchronized, they may lack the necessary complexity for healthy motor coding. However, direct experimental support for this idea is rare and underlying mechanisms are unclear. Here we recorded three-dimensional body movements and spiking activity of many single neurons in motor cortex of rats with enhanced synaptic inhibition and a transgenic rat model of Rett syndrome (RTT). For both cases, we found a collapse of complexity in the motor system. Reduced complexity was apparent in lower-dimensional, stereotyped brain–body interactions, neural synchrony, and simpler behavior. Our results demonstrate how imbalanced inhibition can cause excessive synchrony among movement-related neurons and, consequently, a stereotyped motor code. Excessive inhibition and synchrony may underlie abnormal motor function in RTT.

A diverse and complex repertoire of body movements requires diverse and complex neural activity among cortical neurons. Moreover, interactions between movement-related neurons and the body must be sufficiently high dimensional to carry these movement signals with high fidelity. The complexity of movement-related neural activity and neuron–body interactions can be compromised if synchrony among neurons is excessive. Indeed, it is well understood theoretically that excessive correlations can limit the information capacity of any neural code (1–3)—if all neurons are perfectly synchronized, then different neurons cannot encode different motor signals. Synchrony is also known to play a role in pathophysiology of movement-related disorders, like Parkinson’s disease (4–6). However, synchrony and correlations also contribute to healthy function in the motor system (7–14). For instance, particular groups of synchronized neurons seem to send control signals to particular muscle groups (7, 8) and propagation of correlated firing contributes to motor planning (10). Synchrony can also play a role in motor learning (12–14). These findings suggest that correlated activity among specific subsets of neurons encodes specific motor functions. Thus, it stands to reason that if this synchrony became less selective and more stereotyped across neurons, then the motor code would become less complex and lose specificity, resulting in compromised motor function.Here we explored this possibility in rats, in the caudal part of motor cortex where neurons associated with hindlimb, forelimb, and trunk body movement are located (15–17). We focused on two conditions. First, we studied a transgenic rat model of Rett syndrome (RTT), which has disrupted expression of the MeCP2 gene. Second, we studied normal rats with acutely altered inhibitory neural interactions. Both of these cases are associated with abnormal motor behavior and, possibly, abnormal synchrony. Abnormal synchrony is a possibility, because both of these cases are linked to an imbalance between excitatory (E) and inhibitory (I) neural interactions, which in turn is likely to result in abnormal synchrony. For instance, many computational models suggest that synchrony is strongly dependent on E/I interactions (18–21). Likewise, in experiments, pharmacological manipulation of E/I causes changes in synchrony (19, 22, 23) and the excessive synchrony that occurs during epileptic seizures is often attributed to an E/I imbalance (24, 25). Similarly, the majority of people with RTT suffer from seizures (26) and many previous studies establish E/I imbalance as a common problem in RTT (27). MeCP2 dysfunction, which is known to cause RTT, seems to be particularly important in inhibitory neurons (28). For instance, two studies have shown that disrupting MeCP2 only in specific inhibitory neuron types can recapitulate the effects of brain-wide disruption of MeCP2 (29, 30). However, whether the E/I imbalance favors E or I at the population level seems to vary across different brain regions in RTT. Studies of visual cortex (29) and hippocampus (31) suggest that the balance tips toward too much excitation (perhaps explaining the prevalence of seizures), while studies of somatosensory cortex (32, 33) and a brain-wide study of Fos expression (34) suggest that frontal areas, including motor cortex, are tipped toward excessive inhibition. These facts motivated our choice to study pharmacological disruption of inhibition here. While it is clear that E/I imbalance is important in RTT, it is much less clear how it manifests at the level of dynamics and complexity of neural activity that is responsible for coordinating body movements. Thus, in addition to pursuing the general questions about synchrony and complexity in the motor system discussed above, a second goal of our work was to improve understanding of motor dysfunction due to MeCP2 disruption.Taken together, these facts led us to the following questions: How does MeCP2 disruption impact the complexity of body movements, movement-related neural activity, and motor coding? Are abnormalities in the MeCP2-disrupted motor system consistent with excessive inhibition in motor cortex? We hypothesized that both MeCP2 disruption and excessive inhibition lead to reduced complexity of interactions between cortical neurons and body movements, excessive cortical synchrony, and reduced complexity of body movements. Our findings confirmed this hypothesis and suggest that RTT-related motor dysfunction may be due, in part, to excessive synchrony and inhibition in motor cortex.     

Interactions among diameter,myelination, and the Na/K pump affect axonal resilience to high-frequency spiking

Axons reliably conduct action potentials between neurons and/or other targets. Axons have widely variable diameters and can be myelinated or unmyelinated. Although the effect of these factors on propagation speed is well studied, how they constrain axonal resilience to high-frequency spiking is incompletely understood. Maximal firing frequencies range from ∼1 Hz to >300 Hz across neurons, but the process by which Na/K pumps counteract Na⁺ influx is slow, and the extent to which slow Na⁺ removal is compatible with high-frequency spiking is unclear. Modeling the process of Na⁺ removal shows that large-diameter axons are more resilient to high-frequency spikes than are small-diameter axons, because of their slow Na⁺ accumulation. In myelinated axons, the myelinated compartments between nodes of Ranvier act as a “reservoir” to slow Na⁺ accumulation and increase the reliability of axonal propagation. We now find that slowing the activation of K⁺ current can increase the Na⁺ influx rate, and the effect of minimizing the overlap between Na⁺ and K⁺ currents on spike propagation resilience depends on complex interactions among diameter, myelination, and the Na/K pump density. Our results suggest that, in neurons with different channel gating kinetic parameters, different strategies may be required to improve the reliability of axonal propagation.

Axons usually reliably conduct information (spikes) to other neurons, muscles, and glands. The largest-diameter axons are found in some invertebrates, where the squid giant axon and arthropod giant fibers are parts of rapid escape systems. In the mammalian nervous system, axon diameters can differ by a factor of >100. Some are covered with myelin sheaths but others are not (1). Both myelination and large axon diameter increase spike propagation speed (2, 3), and the latter factor can also increase axonal resilience to noise perturbation (4).Neuronal firing frequency is not static, but variations in firing rates are used to encode a wide range of signals such as inputs from sensory organs and command signals from motor cortex. The maximal firing frequency, critical for neuronal functional capacity, ranges from ∼1 Hz to >300 Hz (5–8). However, it remains unclear how axonal physical properties constrain axonal resilience to high-frequency firing. During action potentials, Na⁺ flows into axons and then adenosine 5′-triphosphate (ATP) is used by Na/K pumps to pump out the excess Na⁺. Given the limited energy supply in the brain, evolutionary strategies including shortening spike propagation distance, sparse coding, and reducing Na⁺- and K⁺-current overlap, and so on, have helped minimize energy expenditure (9–13). However, the observation that high-frequency spiking tends to occur in large-diameter axons (5) is confusing, because fast spiking in large-diameter axons causes more Na⁺ influx and exerts a heavy burden on the energy supply in the brain (14, 15). Additionally, the process by which Na/K pumps remove Na⁺ is slow (16–22). In neurons, it takes seconds or even minutes to remove the excess Na⁺ after [Na⁺] elevation (16–21). It remains unknown how these directly correlated processes, fast spiking and slow Na⁺ removal, interact in the control of reliable spike generation and propagation. Furthermore, it is unclear whether reducing Na⁺- and K⁺-current overlap can consistently decrease the rate of Na⁺ influx and accordingly enhance axonal reliability to propagate high-frequency spikes. Consequently, by modeling the process of Na⁺ removal in unmyelinated and myelinated axons, we systematically explored the effects of diameter, myelination, and Na/K pump density on spike propagation reliability.     

Optogenetic activation of septal cholinergic neurons suppresses sharp wave ripples and enhances theta oscillations in the hippocampus

Theta oscillations in the limbic system depend on the integrity of the medial septum. The different populations of medial septal neurons (cholinergic and GABAergic) are assumed to affect different aspects of theta oscillations. Using optogenetic stimulation of cholinergic neurons in ChAT-Cre mice, we investigated their effects on hippocampal local field potentials in both anesthetized and behaving mice. Cholinergic stimulation completely blocked sharp wave ripples and strongly suppressed the power of both slow oscillations (0.5–2 Hz in anesthetized, 0.5–4 Hz in behaving animals) and supratheta (6–10 Hz in anesthetized, 10–25 Hz in behaving animals) bands. The same stimulation robustly increased both the power and coherence of theta oscillations (2–6 Hz) in urethane-anesthetized mice. In behaving mice, cholinergic stimulation was less effective in the theta (4–10 Hz) band yet it also increased the ratio of theta/slow oscillation and theta coherence. The effects on gamma oscillations largely mirrored those of theta. These findings show that medial septal cholinergic activation can both enhance theta rhythm and suppress peri-theta frequency bands, allowing theta oscillations to dominate.Subcortical neuromodulators play a critical role in shifting states of the brain (1, 2). State changes can occur both during sleep and in the waking animal and are instrumental in affecting local circuit computation that supports various functions, including attention, learning, memory, and action (3–5). The septo-hippocampal cholinergic system has been hypothesized to play a critical role in setting network states in the limbic system (4, 6). ACh can affect both short- and long-term plasticity of synaptic connections and provide favorable conditions for encoding information (7–9). These plastic states are associated with hippocampal theta oscillations (10). High theta states are characterized by increased release of ACh that varies in a task-dependent manner on the time scale of seconds (11–13). In contrast, reduced cholinergic activity allows effective spread of excitation in the recurrent CA3 network, giving rise to synchronous sharp wave ripples (SPW-R) (14–16).Inactivation of the medial septum (MS)/diagonal band of Broca abolishes theta oscillations in the hippocampus and entorhinal cortex (17) and results in severe learning deficit (18, 19). Similarly, selective toxin lesion of septal cholinergic neurons produces a several-fold decrease of theta power but not its frequency (20). The phase of the local field potentials (LFP) theta oscillations shifts from the septal to the temporal pole and in the CA3–CA1 axis by ∼180° (21, 22). Thus, at each point in time neurons residing at different locations of the three-dimensional structure of the hippocampus spike at different theta phases yet are bound together by the global theta signal. These numerous sources of theta generators are believed to be coordinated by the reciprocal connections between the septum and hippocampus (23), but the nature of this spatial–temporal coordination is not well understood (24). Both cholinergic and GABAergic neurons, and a small fraction of VGlut2 immunoreactive neurons (25), are believed to play a critical role in such global coordination (26, 27). Although GABAergic neurons of the MS were demonstrated to be entrained at theta frequency, identified cholinergic neurons did not show theta-related discharge pattern (28, 29). Additionally, both GABAergic and cholinergic neurons are affected by the feedback long-range hippocampo-septal inhibitory connections (30).Early studies, performed in anesthetized animals, already suggested a critical role for the cholinergic septo-hippocampal projection in the generation of theta oscillations (6). Indeed, the low-frequency theta present under urethane anesthesia can be fully abolished by antimuscarinic drugs (31). In contrast, atropine or scopolamine fail to abolish theta oscillations during waking exploration (31, 32), although they affect the theta waveform and its amplitude-phase depth profile in the hippocampus (33). Although these previous works are compatible with the hypothesis that the role of septal cholinergic projections is mainly permissive and affects theta power without modulating its frequency (20, 26, 28), direct evidence is missing. The role of septal cholinergic neurons on gamma oscillation and SPW-R is even less understood (14). To address these issues, we used optogenetic activation of septal cholinergic input and examined its impact on hippocampal theta, peri-theta bands, gamma, and ripple oscillations in both anesthetized and freely moving mice.     

Cumulative latency advance underlies fast visual processing in desynchronized brain state

Fast sensory processing is vital for the animal to efficiently respond to the changing environment. This is usually achieved when the animal is vigilant, as reflected by cortical desynchronization. However, the neural substrate for such fast processing remains unclear. Here, we report that neurons in rat primary visual cortex (V1) exhibited shorter response latency in the desynchronized state than in the synchronized state. In vivo whole-cell recording from the same V1 neurons undergoing the two states showed that both the resting and visually evoked conductances were higher in the desynchronized state. Such conductance increases of single V1 neurons shorten the response latency by elevating the membrane potential closer to the firing threshold and reducing the membrane time constant, but the effects only account for a small fraction of the observed latency advance. Simultaneous recordings in lateral geniculate nucleus (LGN) and V1 revealed that LGN neurons also exhibited latency advance, with a degree smaller than that of V1 neurons. Furthermore, latency advance in V1 increased across successive cortical layers. Thus, latency advance accumulates along various stages of the visual pathway, likely due to a global increase of membrane conductance in the desynchronized state. This cumulative effect may lead to a dramatic shortening of response latency for neurons in higher visual cortex and play a critical role in fast processing for vigilant animals.Fast reaction is essential for the survival of animals, such as when detecting and fleeing a predator. Humans or animals react rapidly in a vigilant state (1–3). The vigilance level of animals (also humans) varies with brain state, which can be characterized by the patterns of population activities measured by electroencephalogram (EEG) and local field potential (LFP) (4, 5). Although brain state exhibits diverse activity patterns, it can be broadly classified into a desynchronized state dominated by small-amplitude, high-frequency activities, and a synchronized state dominated by large-amplitude, low-frequency fluctuations (4, 5). The brain operates in the desynchronized state when the animal is alert or vigilant, whereas it operates in the synchronized state when the animal is quiescent or drowsy (4, 5). To understand the neural substrate for fast processing in the vigilant state, it is important to compare response latencies in the two brain states for sensory cortical neurons, which are at the initial stage along the sensorimotor pathway.Brain state has a dominant impact on both resting properties (6, 7) and sensory evoked responses (4, 8, 9) of cortical neurons. For the visual system, although brain state or behavioral state can modulate response amplitude, spatial receptive field, and temporal frequency tuning of the neurons in the early visual pathway (10–13), it is not clear whether brain state can modulate response latency of primary visual cortex (V1) neurons. Furthermore, the cellular and network mechanisms for brain state modulation of visual response remain largely unknown. It is suggested that background synaptic bombardments regulate membrane potential and input conductance of cortical neurons, and thus significantly influence the temporal properties of synaptic integration by the neurons (14, 15). In this study, we raised and tested a hypothesis that brain state can modulate response latency of V1 neurons by changing background synaptic inputs.We found in this study that the response latency of V1 neurons was shorter in the desynchronized state than in the synchronized state, in both awake and anesthetized rats. In vivo whole-cell recording from the same V1 neurons undergoing the two states showed that both the resting and visually evoked conductances were higher in the desynchronized state, but such conductance increases of single neurons only partly contributed to the observed latency advance. Simultaneous recording from lateral geniculate nucleus (LGN) and V1 neurons using multisite silicon probes revealed that the latency advance increased from LGN to V1 and across successive V1 layers. Thus, the shorter latency of V1 neurons in the desynchronized state can be accounted for by an accumulation of latency advance along various stages in the early visual pathway.