Brain–machine interface for eye movements

A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain–machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients.Brain–machine interfaces (BMIs) have been successfully used to predict reaches and arm movements (1–7). However, little effort has been concentrated on building a BMI based on eye movements. This gap is surprising because the motor and neuronal mechanisms of eye movements are very well understood and arguably simpler than those of arm movements. Specifically, eye movements are rapid and ballistic. The lateral intraparietal cortex (LIP) is ideally suited to be the site for a BMI based on eye movements (8). LIP neurons are known to encode eye movement plans, among other signals such as eye position (9–16). We recently showed that eye movement plans can be accurately predicted from the responses of populations of LIP neurons using Bayesian inference (16). The aim of the present study was twofold. First, a BMI was used with small neuronal ensembles of LIP neurons to predict, in real time, eye movement plans without the animals actually making eye movements. Second, the BMI application induced learning-related changes in the saccade system. Learning can produce changes in reach areas, but how learning-related changes occur at the level of LIP neuronal ensembles is still unclear (17, 18).Here, we show that the intended eye movement activity can be used to accurately position a cursor on a computer screen. These results suggest that an eye movement BMI can be used as a prosthetic to assist locked-in patients who cannot produce eye movements. Moreover, such an eye movement BMI can also be used to assist tetraplegic persons to decode intended limb movements by providing an extra channel of target position information (19). Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. The population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, which here is the prediction accuracy of the BMI. Such learning adds additional support for the utility of an eye movement BMI based on LIP activity.     

Epileptic baboons have lower numbers of neurons in specific areas of cortex

Epilepsy is characterized by recurrent seizure activity that can induce pathological reorganization and alter normal function in neocortical networks. In the present study, we determined the numbers of cells and neurons across the complete extent of the cortex for two epileptic baboons with naturally occurring seizures and two baboons without epilepsy. Overall, the two epileptic baboons had a 37% average reduction in the number of cortical neurons compared with the two nonepileptic baboons. The loss of neurons was variable across cortical areas, with the most pronounced loss in the primary motor cortex, especially in lateral primary motor cortex, representing the hand and face. Less-pronounced reductions of neurons were found in other parts of the frontal cortex and in somatosensory cortex, but no reduction was apparent in the primary visual cortex and little in other visual areas. The results provide clear evidence that epilepsy in the baboon is associated with considerable reduction in the numbers of cortical neurons, especially in frontal areas of the cortex related to motor functions. Whether or not the reduction of neurons is a cause or an effect of seizures needs further investigation.Epilepsy is associated with structural changes in the cerebral cortex (e.g., refs. 1–6), and partial epilepsies (i.e., seizures originating from a brain region) may lead to loss of neurons (7) and altered connectivity (8). The cerebral cortex is a heterogeneous structure comprised of multiple sensory and motor information-processing systems (e.g., refs. 9 and 10) that vary according to their processing demands, connectivity (e.g., refs. 11 and 12), and intrinsic numbers of cells and neurons (13–16). Chronic seizures have been associated with progressive changes in the region of the epileptic focus and in remote but functionally connected cortical or subcortical structures (3, 17). Because areas of the cortex are functionally and structurally different, they may also differ in susceptibility to pathological changes resulting from epilepsy.The relationship between seizure activity and neuron damage can be difficult to study in humans. Seizure-induced neuronal damage can be convincingly demonstrated in animals using electrically or chemically induced status epilepticus (one continuous seizure episode longer than 5 min) to reveal morphometric (e.g., refs. 18 and 19) or histological changes (e.g., refs. 20 and 21). Subcortical brain regions are often studied for vulnerability to seizure-induced injury (21–27); however, a recent study by Karbowski et al. (28) observed reduction of neurons in cortical layers 5 and 6 in the frontal lobes of rats with seizures. Seizure-induced neuronal damage in the cortex has also been previously demonstrated in baboons with convulsive status epilepticus (29).The goal of the present study was to determine if there is a specific pattern of cell or neuron reduction across the functionally divided areas of the neocortex in baboons with epilepsy. Selected strains of baboons have been studied as a natural primate model of generalized epilepsy (30–36) that is analogous to juvenile myoclonic epilepsy in humans. The baboons demonstrate generalized myoclonic and tonic-clonic seizures, and they have generalized interictal and ictal epileptic discharges on scalp EEG. Because of their phylogenetic proximity to humans, baboons and other Old World monkeys share many cortical areas and other features of cortical organization with humans (e.g., refs. 9 and 10). Cortical cell and neuron numbers were determined using the flow fractionator method (37, 38) in epileptic baboon tissue obtained from the Texas Biomedical Research Institute, where a number of individuals develop generalized epilepsy within a pedigreed baboon colony (31–36). Our results reveal a regionally specific neuron reduction in the cortex of baboons with naturally occurring, generalized seizures.     

Behavioral response inhibition and maturation of goal representation in prefrontal cortex after puberty

Executive functions including behavioral response inhibition mature after puberty, in tandem with structural changes in the prefrontal cortex. Little is known about how activity of prefrontal neurons relates to this profound cognitive development. To examine this, we tracked neuronal responses of the prefrontal cortex in monkeys as they transitioned from puberty into adulthood and compared activity at different developmental stages. Performance of the antisaccade task greatly improved in this period. Among neural mechanisms that could facilitate it, reduction of stimulus-driven activity, increased saccadic activity, or enhanced representation of the opposing goal location, only the latter was evident in adulthood. Greatly accentuated in adults, this neural correlate of vector inversion may be a prerequisite to the formation of a motor plan to look away from the stimulus. Our results suggest that the prefrontal mechanisms that underlie mature performance on the antisaccade task are more strongly associated with forming an alternative plan of action than with suppressing the neural impact of the prepotent stimulus.Behavioral response inhibition, and cognitive task performance more generally, improves substantially between the time of puberty and adulthood (1–4). Risky decision-making peaks in adolescence, the time period between puberty and adulthood that is most closely linked to delinquent behavior in humans (5–7). Performance in tasks that assay response inhibition, such as the antisaccade task, improves into adulthood, reflecting the progressive development of behavioral control (3). This period of cognitive enhancement parallels the maturation of the prefrontal cortex (8–11). Anatomical changes in the prefrontal cortex continue during adolescence, involving gray and white matter volumes and myelination of axon fibers within the prefrontal cortex and between the prefrontal cortex and other areas (8–15). Changes in prefrontal activation, including increases (12, 16–20) and decreases (21, 22), have been documented in imaging studies for tasks that require inhibition of prepotent behavioral responses and filtering of distractors.Much less is known about how the physiological properties of prefrontal neurons develop after puberty. Similar to the human pattern of development, the monkey prefrontal cortex undergoes anatomical maturation in adolescence and early adulthood (23, 24). Male monkeys enter puberty at ∼3.5 y of age and reach full sexual maturity at 5 y, approximately equivalent to the human ages of 11 y and 16 y, respectively (25, 26). By some accounts, biochemical and anatomical changes characteristic of adolescence in humans occur at an earlier, prepubertal age in the monkey prefrontal cortex (27, 28), so it is not known if cognitive maturation or neurophysiological changes occur in monkeys after puberty. The contribution of prefrontal cortex to antisaccade performance has also been a matter of debate, with contrasting views favoring mechanisms of inhibiting movement toward the visual stimulus or enhancing movement away from it (29–31). Potential maturation of behavioral response inhibition may therefore be associated with a more efficient suppression of the stimulus representation in neural activity (weaker visual responses to stimuli inside the receptive field), stronger motor responses (higher activity to saccades), or enhancement of the appropriate goal representation (stronger activity for planning a saccade away from the stimulus). To examine the mechanisms that facilitate the mature ability to resist generating a response toward a salient stimulus, we used developmental markers to track transition from puberty to adulthood in monkeys and sought to identify neural correlates of changes in antisaccade performance within the visual and saccade-related activations of prefrontal neurons.     

From the Cover: PNAS Plus: Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro

Human cell reprogramming technologies offer access to live human neurons from patients and provide a new alternative for modeling neurological disorders in vitro. Neural electrical activity is the essence of nervous system function in vivo. Therefore, we examined neuronal activity in media widely used to culture neurons. We found that classic basal media, as well as serum, impair action potential generation and synaptic communication. To overcome this problem, we designed a new neuronal medium (BrainPhys basal + serum-free supplements) in which we adjusted the concentrations of inorganic salts, neuroactive amino acids, and energetic substrates. We then tested that this medium adequately supports neuronal activity and survival of human neurons in culture. Long-term exposure to this physiological medium also improved the proportion of neurons that were synaptically active. The medium was designed to culture human neurons but also proved adequate for rodent neurons. The improvement in BrainPhys basal medium to support neurophysiological activity is an important step toward reducing the gap between brain physiological conditions in vivo and neuronal models in vitro.Induced pluripotent stem cell (iPSC) technology is currently being used to model human diseases in vitro and may contribute to the discovery and validation of new pharmacological treatments (1–3). In particular, neuroscientists have seized the opportunity to culture neurons from patients with neurological and psychiatric disorders and have demonstrated that phenotypes associated with particular disorders can be recapitulated in the dish (4–7). However, the basic culture conditions for growing neurons in vitro have not been updated to reflect fundamental principles of brain physiology. Currently, most human neuronal cultures are grown in media based on DMEM/F12 (4, 5, 7–24), Neurobasal (25–30), or a mixture of DMEM and Neurobasal (DN) (31–34). To promote neuronal differentiation and survival, a variety of supplements, such as serum, growth factors, hormones, proteins, and antioxidants, are typically added to these basal media. Although these media were designed and optimized to promote neuronal survival in vitro, they were not tested for their ability to support fundamental neuronal functions. Using several electrophysiological techniques such as patch clamping, calcium imaging, and multielectrode arrays, we found that widely used tissue culture media (e.g., DMEM basal, Neurobasal, serum) actually impaired neurophysiological functions.We identified several neuroactive components in these media that acutely interfered with neuronal function. To solve these issues, we designed a chemically defined basal medium: BrainPhys basal. We used human neurons in vitro to demonstrate in a series of experiments that this new medium, combined with the appropriate supplements, better supports important neuronal functions while sustaining cell survival in vitro. Notably, BrainPhys-based medium better mimics the environment present in healthy living brains, unlike previous media based on DMEM, Neurobasal or serum. Although BrainPhys basal was specifically designed for the culturing of mature human neurons, our studies also showed that BrainPhys provided a functional environment for ex vivo brain slices and for culturing rodent primary neurons.     

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.     

Gating of steering signals through phasic modulation of reticulospinal neurons during locomotion

The neural control of movements in vertebrates is based on a set of modules, like the central pattern generator networks (CPGs) in the spinal cord coordinating locomotion. Sensory feedback is not required for the CPGs to generate the appropriate motor pattern and neither a detailed control from higher brain centers. Reticulospinal neurons in the brainstem activate the locomotor network, and the same neurons also convey signals from higher brain regions, such as turning/steering commands from the optic tectum (superior colliculus). A tonic increase in the background excitatory drive of the reticulospinal neurons would be sufficient to produce coordinated locomotor activity. However, in both vertebrates and invertebrates, descending systems are in addition phasically modulated because of feedback from the ongoing CPG activity. We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem–spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on- or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem–spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. The simulations also predict that, in contrast to reticulospinal neurons, tectal steering/turning command neurons should have minimal frequency adaptive properties, which has been confirmed experimentally.In many vertebrate and invertebrate motor systems, a phasic modulation occurs in the descending control system determining the level of activity (1–3) during rhythmic movements. The physiological role of this modulation has remained enigmatic, because it has been shown that tonic activity is sufficient to effectively drive motor activity like locomotion. One example is the reticulospinal neurons in the brainstem that serve as the major interface between higher level commands and the networks in the spinal cord in all vertebrates from lamprey to primates (4–6). In this study, we investigate the motor system of the lamprey, belonging to the most ancient group of vertebrates that has been investigated in considerable detail not only at the brainstem–spinal cord level but also with regard to the forebrain systems underlying the control of action (2, 7, 8). A bilateral symmetric activation of reticulospinal neurons will activate the locomotor networks in the spinal cord, resulting in coordinated swimming movements (2, 9–11). The reticulospinal neurons act on the excitatory and inhibitory network interneurons in the spinal cord through NMDA and AMPA receptors (12). Most reticulospinal neurons can be involved in several motor patterns (13). Whereas a bilaterally symmetric activation leads to locomotion, a unilateral addition of excitation to one side will enhance motor activity on this side and result in turning. This is the basis of steering during locomotion (14).The phasic modulation of reticulospinal neurons is most pronounced in the fastest-conducting group involved in steering (15). It results from feedback from the network neurons in the rostral segments of the spinal cord during locomotor movements, so that the reticulospinal neurons become active in phase with those segments, and during the inactive period they are instead inhibited (15–19). This feedback is conveyed to reticulospinal neurons via ascending spinobulbar neurons (15–20) that provide an “efference copy” regarding the cycle-to-cycle activity in the locomotor network. Spinobulbar neurons (21) provide the excitatory and inhibitory drive to reticulospinal neurons, resulting in modulation of their activity in phase with the ipsilateral rostral parts of the spinal cord (21, 22) and this forms a closed spino-reticulo-spinal loop (17).Visuomotor coordination (23) and steering results from activation of tectal output neurons that monosynaptically activate reticulospinal neurons (24, 25), which represent the interface between tectum and the spinal cord networks. Here, we focus on the role of the phasic modulation of the reticulospinal neurons. We show that the phasic modulation of the reticulospinal cells is advantageous in that the steering commands from tectum become gated and thereby arrive in the correct phase of the locomotor cycle. The tectal commands, therefore, need not be timed very precisely in relation to the locomotor cycle. The reticulospinal modulation ascertains that the command signal will be accurately timed provided that the tectal command signal itself remains constant and thus has a limited spike-frequency adaptation, which indeed applies to the tectal output neurons as shown here experimentally (25). We explore the effect of steering signals through a combined simulation-experimental approach. Biologically detailed lamprey spinal cell models (26) are used in large-scale simulations of the locomotor network (7) to replicate electric activity in command centers and the spinal cord. A mechanical model of swimming (27, 28) is used for a quantitative evaluation of the locomotor response.     

In vivo imaging of immediate early gene expression reveals layer-specific memory traces in the mammalian brain

The dynamic processes of formatting long-term memory traces in the cortex are poorly understood. The investigation of these processes requires measurements of task-evoked neuronal activities from large numbers of neurons over many days. Here, we present a two-photon imaging-based system to track event–related neuronal activity in thousands of neurons through the quantitative measurement of EGFP proteins expressed under the control of the EGR1 gene promoter. A recognition algorithm was developed to detect GFP-positive neurons in multiple cortical volumes and thereby to allow the reproducible tracking of 4,000 neurons in each volume for 2 mo. The analysis revealed a context-specific response in sparse layer II neurons. The context-evoked response gradually increased during several days of training and was maintained 1 mo later. The formed traces were specifically activated by the training context and were linearly correlated with the behavioral response. Neuronal assemblies that responded to specific contexts were largely separated, indicating the sparse coding of memory-related traces in the layer II cortical circuit.In the mammalian brain, memory traces in cortical areas are poorly understood. In contrast to the medial temporal lobe, particularly the hippocampus, which is involved in the temporary storage of declarative memories (1, 2), the neocortex is believed to store remote memories (3–6). However, remarkably little knowledge regarding the sites and dynamics of remote memory storage has been revealed at the cellular level owing to the complexity of the connections and the large number of neurons within the cortical circuit.In vivo electrophysiological recording of neuronal firing revolutionized neurobiology by linking neuronal activity with animal behavior. The small number of neurons recorded by the electrodes, however, was a limitation, as information coding and decoding may use an army of neurons forming neuronal assemblies (7, 8). Efforts to record the activity of larger populations of neurons in cortical volumes have been actively pursued by either increasing the number of electrode probes (7, 9–11) or using calcium indicator–based imaging (12–15) and immediate early gene (IEG)-based reporters (16–18). The expression of IEGs is correlated with the averaged neuronal activation on external stimuli (19, 20), implying that the marked neurons are involved in behavior (1, 21–25). Studies using in vivo imaging of IEGs have revealed cortical coding in the visual cortex and in other cortical areas, reflecting electrical activation in individual neurons (16, 17). Among IEGs, the expression of early growth response protein 1 (EGR1, also known as zif268) is associated with high-frequency stimulation and the induction of long-term plasticity during learning (26, 27). To measure neuronal activation in cortical circuits during a behavioral task, we used an EGR1 expression reporter mouse line in which the expression of the EGFP protein is under the control of the Egr1 gene promoter. We designed offline recording strategies to monitor task-associated neuronal activity by quantifying changes in cellular EGFP signals in the mouse cortex. Patterns of activated neuronal assemblies during different tasks were visualized in the entire cortical volume. Furthermore, through computer recognition-based reconstruction, we were able to track the activity-related cellular EGFP signals from multiple cortical areas for 2 mo to reveal memory-related changes in the cortical circuit.     

Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation

A long-standing hypothesis termed “Hebbian plasticity” suggests that memories are formed through strengthening of synaptic connections between neurons with correlated activity. In contrast, other theories propose that coactivation of Hebbian and neuromodulatory processes produce the synaptic strengthening that underlies memory formation. Using optogenetics we directly tested whether Hebbian plasticity alone is both necessary and sufficient to produce physiological changes mediating actual memory formation in behaving animals. Our previous work with this method suggested that Hebbian mechanisms are sufficient to produce aversive associative learning under artificial conditions involving strong, iterative training. Here we systematically tested whether Hebbian mechanisms are necessary and sufficient to produce associative learning under more moderate training conditions that are similar to those that occur in daily life. We measured neural plasticity in the lateral amygdala, a brain region important for associative memory storage about danger. Our findings provide evidence that Hebbian mechanisms are necessary to produce neural plasticity in the lateral amygdala and behavioral memory formation. However, under these conditions Hebbian mechanisms alone were not sufficient to produce these physiological and behavioral effects unless neuromodulatory systems were coactivated. These results provide insight into how aversive experiences trigger memories and suggest that combined Hebbian and neuromodulatory processes interact to engage associative aversive learning.Hebbian plasticity refers to the strengthening of a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive (1). This was originally proposed as a mechanism for memory formation. Findings from in vitro and in vivo physiological studies suggest that Hebbian processes control synaptic strengthening (2–10). However, other results and theories suggest that Hebbian mechanisms alone are not normally sufficient for producing synaptic plasticity and that synaptic strengthening mediating memory formation involves interactions between Hebbian and neuromodulatory mechanisms (3, 4, 7, 11–19). Although molecules that may mediate Hebbian processes in memory formation have been identified (3, 11, 16, 17, 20–22), it has been difficult to directly test whether Hebbian plasticity alone or in combination with neuromodulation is necessary and sufficient to produce neural plasticity and memories in behaving animals (especially in mammals). This is because of technical limitations in controlling correlated activity between pre- and postsynaptic neurons involved in memory storage in a temporally/spatially precise manner while measuring behavioral memory formation and neural plasticity.To overcome these problems, we used optogenetic techniques to directly manipulate Hebbian mechanisms in pyramidal neurons in the lateral nucleus of the amygdala (LA), a cell population important for storing aversive memories. Pavlovian auditory threat (fear) conditioning (23, 24) is a form of associative learning during which a neutral auditory conditioned stimulus (CS) is temporally paired with an aversive unconditioned stimulus (US), often a mild electric shock (17, 20, 21, 25–27). Following training, the auditory CS comes to elicit behavioral defense responses (such as freezing) and supporting physiological changes controlled by the autonomic nervous and endocrine systems. These conditioned responses can be used to measure the associative memory created by CS–US pairing.This form of aversive Pavlovian conditioning is a particularly useful model for testing the Hebbian hypothesis because a critical site of associative plasticity underlying the learning has been identified in the LA (17, 22, 28). LA neurons receive convergent input from the auditory system and from aversive nociceptive circuits (29, 30). Auditory inputs to LA neurons are potentiated during threat conditioning (31–34), possibly as a result of auditory-evoked presynaptic activity occurring convergently and contemporaneously with strong activation of postsynaptic LA pyramidal neurons by the aversive shock US (i.e., a Hebbian mechanism). If these neural and behavioral changes are the result of Hebbian plasticity, then activity in LA pyramidal neurons specifically during the aversive US period (when both presynaptic inputs and postsynaptic neurons may be active) should be necessary for aversive memory formation and learning-related plasticity of auditory input synapses in the LA to occur. Reducing activity in LA neurons should disrupt the correlation between presynaptic activity induced by the auditory CS and postsynaptic activity induced by the aversive US. In addition, pairing the auditory CS with direct depolarization of LA pyramidal neurons in place of a shock US should be sufficient to produce aversive memories and plasticity of auditory inputs to the LA. This is because direct stimulation of postsynaptic LA neurons as an US would artificially produce coactivity with concurrently active auditory inputs. Previously, we found that this type of training procedure did produce behavioral learning when many training trials were used (35). However, the behavioral memory acquired under these conditions was somewhat weak, suggesting that other factors, such as neuromodulatory receptor activation, might function in a cooperative way to enhance Hebbian neural plasticity in the LA to possibly regulate the gain of aversive memory formation. Here we optogenetically manipulated correlated activity between auditory inputs and LA postsynaptic pyramidal neurons to directly test whether Hebbian mechanisms are both necessary and sufficient to produce changes in auditory processing in the LA and fear memories.     

Differential roles of delay-period neural activity in the monkey dorsolateral prefrontal cortex in visual–haptic crossmodal working memory

Previous studies have shown that neurons of monkey dorsolateral prefrontal cortex (DLPFC) integrate information across modalities and maintain it throughout the delay period of working-memory (WM) tasks. However, the mechanisms of this temporal integration in the DLPFC are still poorly understood. In the present study, to further elucidate the role of the DLPFC in crossmodal WM, we trained monkeys to perform visuo–haptic (VH) crossmodal and haptic–haptic (HH) unimodal WM tasks. The neuronal activity recorded in the DLPFC in the delay period of both tasks indicates that the early-delay differential activity probably is related to the encoding of sample information with different strengths depending on task modality, that the late-delay differential activity reflects the associated (modality-independent) action component of haptic choice in both tasks (that is, the anticipation of the behavioral choice and/or active recall and maintenance of sample information for subsequent action), and that the sustained whole-delay differential activity likely bridges and integrates the sensory and action components. In addition, the VH late-delay differential activity was significantly diminished when the haptic choice was not required. Taken together, the results show that, in addition to the whole-delay differential activity, DLPFC neurons also show early- and late-delay differential activities. These previously unidentified findings indicate that DLPFC is capable of (i) holding the coded sample information (e.g., visual or tactile information) in the early-delay activity, (ii) retrieving the abstract information (orientations) of the sample (whether the sample has been haptic or visual) and holding it in the late-delay activity, and (iii) preparing for behavioral choice acting on that abstract information.Working memory (WM) is a central concept in cognitive sciences. The prefrontal cortex constitutes the highest stage in the cortical hierarchy of executive memories (1–5), and it seems to be essential for integrating sensory information of different modalities with subsequent action in goal-directed behavior (6–9).Cells involved in WM (“memory cells”) were first recorded in the dorsolateral prefrontal cortex (DLPFC) of monkeys performing delayed-response tasks (10–12) and have also been reported by other subsequent primate studies (13–19). The persistent delay activity recorded in those studies reflects the maintenance of a working-memory representation and therefore underlies the representation of retrospective, current, and prospective information (20). From the results of those studies, it seems that, in addition to persistent delay activity that is sustained throughout the whole delay period in WM tasks, task/set cells, eye movement-related responses, and phasic sensory responses, etc. (14–18, 21), two other general types of prefrontal neurons have also been studied (22, 23). One is the so-called sensory-coupled cue cell, the discharge of which tends to diminish during the delay period of WM tasks. The other is the preparatory-set cell; its discharge tends to increase as the time for an expected behavioral response of a WM task approaches. These two types of cells may participate in two complementary processes: Sensory-coupled cells hold information of stimuli, and preparatory-set cells prepare for action in response to that information. These findings imply that the DLPFC plays a critical role in temporal organization of behavior by integrating action with recent sensory information across time (24).Cells in the DLPFC have been shown to be attuned to stimuli of different modalities in memory tasks, such as colors (25–27), tactile vibrations (19), and tones (28). Functional imaging and event-related potential studies have also shown DLPFC activity in processing information from different modalities (29–33). In addition, monkeys with lesions in banks and depths of the arcuate sulcus (the posterior end of the DLPFC) were impaired in performance of a tactile–visual crossmodal matching task (34).In line with these reports, DLPFC neurons have been revealed to be able to associate a visual stimulus with an auditory stimulus across time (35). In this pioneer study, cells in the DLPFC responded selectively to auditory stimuli, and most of them also responded to visual stimuli according to the task rule (crossmodal associations). A similar type of crossmodal delay activity was also found in the inferior temporal (IT) cortex in auditory–visual and visual–auditory tasks (36).However, the mechanisms of temporal integration of sensory and action processing in crossmodal working memory remain unclear. Specifically, it is still unclear how the sensory component and the action component of crossmodal working memory networks, as well as the component that mediates crosstemporal contingencies throughout the whole delay, are timely and selectively activated in the task. Here, to better understand the role of the DLPFC in the neural processing of crossmodal working memory, we examined differential neural activity (different firing rates in response to different stimuli or task events) (10, 11, 13) during the performance of crossmodal and unimodal WM (delayed matching-to-sample) tasks. Monkeys were trained to perform a visuo–haptic (VH) crossmodal WM task that required memorization of a visual cue for a subsequent haptic choice, and a haptic–haptic (HH) unimodal task, in which the animals had to retain a haptic cue for a subsequent haptic choice. Moreover, we trained a monkey to perform a control task that was identical to the VH task in all respects but without the requirement to memorize the visual cue during the delay period for the subsequent choice. We intended to find answers to two questions: (i) How does the DLPFC represent information of two different associated modalities and (ii) how do cortical networks in the DLPFC integrate the temporally separated components, sensory and choice components of a WM task?     

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.     

Dissecting neural pathways for forgetting in Drosophila olfactory aversive memory

Recent studies have identified molecular pathways driving forgetting and supported the notion that forgetting is a biologically active process. The circuit mechanisms of forgetting, however, remain largely unknown. Here we report two sets of Drosophila neurons that account for the rapid forgetting of early olfactory aversive memory. We show that inactivating these neurons inhibits memory decay without altering learning, whereas activating them promotes forgetting. These neurons, including a cluster of dopaminergic neurons (PAM-β′1) and a pair of glutamatergic neurons (MBON-γ4>γ1γ2), terminate in distinct subdomains in the mushroom body and represent parallel neural pathways for regulating forgetting. Interestingly, although activity of these neurons is required for memory decay over time, they are not required for acute forgetting during reversal learning. Our results thus not only establish the presence of multiple neural pathways for forgetting in Drosophila but also suggest the existence of diverse circuit mechanisms of forgetting in different contexts.Although forgetting commonly has a negative connotation, it is a functional process that shapes memory and cognition (1–4). Recent studies, including work in relatively simple invertebrate models, have started to reveal basic biological mechanisms underlying forgetting (5–15). In Drosophila, single-session Pavlovian conditioning by pairing an odor (conditioned stimulus, CS) with electric shock (unconditioned stimulus, US) induces aversive memories that are short-lasting (16). The memory performance of fruit flies is observed to drop to a negligible level within 24 h, decaying rapidly early after training and slowing down thereafter (17). Memory decay or forgetting requires the activation of the small G protein Rac, a signaling protein involved in actin remodeling, in the mushroom body (MB) intrinsic neurons (6). These so-called Kenyon cells (KCs) are the neurons that integrate CS–US information (18, 19) and support aversive memory formation and retrieval (20–22). In addition to Rac, forgetting also requires the DAMB dopamine receptor (7), which has highly enriched expression in the MB (23). Evidence suggests that the dopamine-mediated forgetting signal is conveyed to the MB by dopamine neurons (DANs) in the protocerebral posterior lateral 1 (PPL1) cluster (7, 24). Therefore, forgetting of olfactory aversive memory in Drosophila depends on a particular set of intracellular molecular pathways within KCs, involving Rac, DAMB, and possibly others (25), and also receives modulation from extrinsic neurons. Although important cellular evidence supporting the hypothesis that memory traces are erased under these circumstances is still lacking, these findings lend support to the notion that forgetting is an active, biologically regulated process (17, 26).Although existing studies point to the MB circuit as essential for forgetting, several questions remain to be answered. First, whereas the molecular pathways for learning and forgetting of olfactory aversive memory are distinct and separable (6, 7), the neural circuits seem to overlap. Rac-mediated forgetting has been localized to a large population of KCs (6), including the γ-subset, which is also critical for initial memory formation (21, 27). The site of action of DAMB for forgetting has yet to be established; however, the subgroups of PPL1-DANs implicated in forgetting are the same as those that signal aversive reinforcement and are required for learning (28–30). It leaves open the question of whether the brain circuitry underlying forgetting and learning is dissociable, or whether forgetting and learning share the same circuit but are driven by distinct activity patterns and molecular machinery (26). Second, shock reinforcement elicits multiple memory traces through at least three dopamine pathways to different subdomains in the MB lobes (28, 29). Functional imaging studies have also revealed Ca²⁺-based memory traces in different KC populations (31). It is poorly understood how forgetting of these memory traces differs, and it remains unknown whether there are multiple regulatory neural pathways. Notably, when PPL1-DANs are inactivated, forgetting still occurs, albeit at a lower rate (7). This incomplete block suggests the existence of an additional pathway(s) that conveys forgetting signals to the MB. Third, other than memory decay over time, forgetting is also observed through interference (32, 33), when new learning or reversal learning is introduced after training (6, 34, 35). Time-based and interference-based forgetting shares a similar dependence on Rac and DAMB (6, 7). However, it is not known whether distinct circuits underlie forgetting in these different contexts.In the current study, we focus on the diverse set of MB extrinsic neurons (MBENs) that interconnect the MB lobes with other brain regions, which include 34 MB output neurons (MBONs) of 21 types and ∼130 dopaminergic neurons of 20 types in the PPL1 and protocerebral anterior medial (PAM) clusters (36, 37). These neurons have been intensively studied in olfactory memory formation, consolidation, and retrieval in recent years (e.g., 24, 28–30, 38–48); however, their roles in forgetting have not been characterized except for the aforementioned PPL1-DANs. In a functional screen, we unexpectedly found that several Gal4 driver lines of MBENs showed significantly better 3-h memory retention when the Gal4-expressing cells were inactivated. The screen has thus led us to identify two types of MBENs that are not involved in initial learning but play important and additive roles in mediating memory decay. Furthermore, neither of these MBEN types is required for reversal learning, supporting the notion that there is a diversity of neural circuits that drive different forms of forgetting.     

Cortical oscillations arise from contextual interactions that regulate sparse coding

Precise spike times carry information and are important for synaptic plasticity. Synchronizing oscillations such as gamma bursts could coordinate spike times, thus regulating information transmission in the cortex. Oscillations are driven by inhibitory neurons and are modulated by sensory stimuli and behavioral states. How their power and frequency are regulated is an open question. Using a model cortical circuit, we propose a regulatory mechanism that depends on the activity balance of monosynaptic and disynaptic pathways to inhibitory neurons: Monosynaptic input causes more powerful oscillations whereas disynaptic input increases the frequency of oscillations. The balance of stimulation to the two pathways modulates the overall distribution of spikes, with stronger disynaptic stimulation (e.g., preferred stimuli inside visual receptive fields) producing high firing rates and weak oscillations; in contrast, stronger monosynaptic stimulation (e.g., suppressive contextual stimulation from outside visual receptive fields) generates low firing rates and strong oscillatory regulation of spike timing, as observed in alert cortex processing complex natural stimuli. By accounting for otherwise paradoxical experimental findings, our results demonstrate how the frequency and power of oscillations, and hence spike times, can be modulated by both sensory input and behavioral context, with powerful oscillations signifying a cortical state under inhibitory control in which spikes are sparse and spike timing is precise.Individual neurons can precisely time their spikes when driven by temporally fluctuating synaptic inputs (1). Narrowband oscillations mediated by inhibitory neurons are thought to be a key source of coordinated fluctuating discharges from input neurons, and they vary in power and frequency during wakeful behavior and sleep. Oscillations in the gamma range (30–80 Hz), thought to be mediated by fast-spiking inhibitory neurons expressing the calcium-binding protein parvalbumin (2, 3), are modulated by the sensory environment (4–6), attention (7), and volition (8), as well as by specific memory tasks, causing changes in sensory responses (2) and information transfer (3) in the cortex. The modulation is observed both in the oscillation power, which we define as the peak of a distinct “bump” in the power spectrum of the local field potential (LFP), as well as the oscillation frequency, which is the frequency at this peak in the power spectrum (5, 6). In current models of oscillations in neuronal networks, oscillations are regulated by stimulation of inhibitory neurons such that increasing stimulation mainly increases their frequency (9–11) or power (12). In the visual cortex, both the contrast and size of visual stimuli increase the stimulation to local inhibitory neurons (13, 14), but the former increases the frequency of gamma-range oscillations (6), and the latter decreases it (5). The power of gamma oscillations increases in the somatosensory, medial temporal (15), motor (8), olfactory (16), and primary visual cortex (5) with increased stimulation to local inhibitory neurons. However, the peak power of oscillations decreases with increased stimulation of inhibitory neurons with attention (17) in some cortical areas (7). In a third scenario, whereas the broadband power in the LFP signal increases with increasing visual contrast (6, 18), peak narrowband power shows no significant trend in response to increasing contrast (8), which is thought to increase the stimulation to the local inhibitory neurons (13).We show that these diverse experimental observations can be explained by the following hypothesis: The balance of two distinct pathways that activate local inhibitory neurons mediates bidirectional regulation of oscillations (Fig. 1A). We classify these pathways as monosynaptic (MS), those that make direct excitatory synaptic connections to the inhibitory neurons, and disynaptic (DS), those that act through the local excitatory neurons.Fig. 1.Relative strength of MS and DS stimulation to inhibitory neurons determines the power and frequency of oscillations in spiking activity. (A) Schematic of local network and the monosynaptic (solid black) and disynaptic (dotted black) pathways for stimulating ...     

Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2)

Visual processing depends critically on the receptive field (RF) properties of visual neurons. However, comprehensive characterization of RFs beyond the primary visual cortex (V1) remains a challenge. Here we report fine RF structures in secondary visual cortex (V2) of awake macaque monkeys, identified through a projection pursuit regression analysis of neuronal responses to natural images. We found that V2 RFs could be broadly classified as V1-like (typical Gabor-shaped subunits), ultralong (subunits with high aspect ratios), or complex-shaped (subunits with multiple oriented components). Furthermore, single-unit recordings from functional domains identified by intrinsic optical imaging showed that neurons with ultralong RFs were primarily localized within pale stripes, whereas neurons with complex-shaped RFs were more concentrated in thin stripes. Thus, by combining single-unit recording with optical imaging and a computational approach, we identified RF subunits underlying spatial feature selectivity of V2 neurons and demonstrated the functional organization of these RF properties.Visual perception depends on processing of the input signals through multiple stages of the visual pathway. At each stage of processing, neuronal representation of the input is governed by the receptive field (RF) properties of individual cells. Characterization of neuronal RFs is a crucial step for understanding the mechanism of visual processing.Compared with the primary visual cortex (V1), neuronal RFs in the secondary visual cortex (V2) are much less understood. Previous studies have shown that in addition to orientation and direction selectivity (1, 2), similar to that found in V1, neurons in V2 also exhibit selectivity for more complex spatial features such as angle (3–5), illusory contour (6), complex shapes (7), texture (8), and segmentation of the scene (9, 10). Given the large number of potentially relevant visual features, traditional methods using stimulus sets with parametric variation of particular visual features are not efficient for comprehensive RF characterization.An alternative approach is to fit the stimulus–response relationship of each neuron by a parametric model. The relationship is ideally probed with large ensembles of visual stimuli, and the resulting model can be used to predict the neuronal responses to other arbitrary stimuli (11, 12). Natural stimuli are well suited for this purpose, because the visual system has evolved to process natural scenes, which contain rich spatial features that are more effective than random stimuli in eliciting cortical responses (13). Such an approach imposes no prior assumption about which stimulus features are relevant to the cell and is thus well suited for unbiased RF characterization.In the present study, we used large ensembles of natural images to probe the neuronal responses in awake macaque monkeys and a linear–nonlinear model (14, 15) to represent the RF of each V2 neuron. The subunits of the RF models were identified by a method adapted from projection pursuit regression (PPR) (16–18), which does not require stimuli with specific statistical properties and is thus well suited for analyzing the neuronal responses to natural stimuli. Compared with other optimization methods, a distinct feature of PPR is to optimize one subunit of the RF model at a time to reduce the dimensionality of the problem. Using this method, we revealed the spatial RF structures of many V2 neurons. Furthermore, we identified cytochrome oxidase stripes of V2 by optical imaging of intrinsic signals. Data from single-unit recording were used in combination with the latter information to determine the spatial organization of cells with different RF properties with respect to the stripes of V2.     

Progressive maturation of silent synapses governs the duration of a critical period

During critical periods, all cortical neural circuits are refined to optimize their functional properties. The prevailing notion is that the balance between excitation and inhibition determines the onset and closure of critical periods. In contrast, we show that maturation of silent glutamatergic synapses onto principal neurons was sufficient to govern the duration of the critical period for ocular dominance plasticity in the visual cortex of mice. Specifically, postsynaptic density protein-95 (PSD-95) was absolutely required for experience-dependent maturation of silent synapses, and its absence before the onset of critical periods resulted in lifelong juvenile ocular dominance plasticity. Loss of PSD-95 in the visual cortex after the closure of the critical period reinstated silent synapses, resulting in reopening of juvenile-like ocular dominance plasticity. Additionally, silent synapse-based ocular dominance plasticity was largely independent of the inhibitory tone, whose developmental maturation was independent of PSD-95. Moreover, glutamatergic synaptic transmission onto parvalbumin-positive interneurons was unaltered in PSD-95 KO mice. These findings reveal not only that PSD-95–dependent silent synapse maturation in visual cortical principal neurons terminates the critical period for ocular dominance plasticity but also indicate that, in general, once silent synapses are consolidated in any neural circuit, initial experience-dependent functional optimization and critical periods end.Immature cortical neural networks, which are formed primarily under genetic control (1), require experience and training to shape and optimize their functional properties. This experience-dependent refinement is considered to be a general developmental process for all functional cortical domains and typically peaks during their respective critical periods (CPs) (2, 3). Known examples for CPs span functional domains as diverse as filial imprinting and courtship song learning in birds (4, 5); cognitive functions, such as linguistic or musical skills in humans (6, 7); and likely best studied, the different features of sensory modalities (3). CPs are characterized by the absolute requirement for experience in a restricted time window for neural network optimization. Lack of visual experience during the CP for visual cortex refinements can, for example, cause irreversible visual impairment (8). Refinements during the CP play an essential role (9). Although some functions can be substantially ameliorated after the CP, they are rarely optimally restored.It is believed that the neural network refinement is based on synapse stabilization and elimination (10–12) and includes forms of long-term synaptic plasticity to remodel excitatory synapses of principal neurons (13, 14). Although long-term plasticity at these excitatory synapses is instructive for shaping neural networks for functional output and their expression coincides with CPs, it is not known whether the remodeling itself governs the duration of CPs. In contrast, only permissive mechanisms have been shown to terminate CPs. Among these, the developmental increase of local inhibition appears to be the dominating mechanism to regulate cortical plasticity and CPs (15–17). Additionally, extracellular matrix remodeling is involved, as well as receptors of immune signaling, such as paired Ig-like receptor B (PirB), or axon pathfinding, such as Nogo (18–21). However, a specific function to directly regulate synapse remodeling during initial neural network optimization is not known and a potential instructive function of PirB was described for adult cortical plasticity but not plasticity of the initial synapse remodeling during CPs (22).AMPA receptor-silent synapses have been proposed to be efficient plasticity substrates during early cortical network refinements (13, 23, 24). Silent synapses are thought to be immature, still-developing excitatory synapses containing only NMDA receptors (NMDARs) but lacking AMPA receptors (AMPARs) (23, 24). They are functionally dormant but can evolve into fully transmitting synapses by experience-dependent insertion of AMPARs, a plasticity process thought to occur frequently in developing cortices (10). Although they appear as the ideal synaptic substrate for CP plasticity and their maturation correlates with sensory experience (10, 25), it has not been experimentally tested whether maturation of silent synapses indeed causes the termination of critical periods. This conceptual model contrasts with the current view that increased local inhibition and the expression of plasticity brakes ends critical periods (18–20, 26). We hypothesize that experience-dependent unsilencing of silent synapses, which results in strengthening and maturation of excitatory synapses, governs network stabilization and refinement during critical periods, and that the progressive decrease of silent synapses leads to the closure of critical periods.Experience-dependent cortical plasticity is classically tested with ocular dominance (OD) plasticity (ODP) in the primary visual cortex (V1), induced by monocular deprivation (MD). In the binocular region of mouse V1, neurons respond to sensory inputs from both eyes, but activity is dominated by afferents from the contralateral eye. During the critical period, a brief MD induces an OD shift of visually evoked responses in V1 toward the open eye (27–29). This juvenile ODP is mediated by a reduction of deprived eye responses in V1 and is temporally confined to a critical period (30, 31).A molecular candidate regulating the cellular basis of critical period plasticity is postsynaptic density protein-95 (PSD-95), whose expression in the visual cortex increases on eye opening and thus the onset of visual experience (32). PSD-95 promotes the maturation of AMPA receptor-silent excitatory synapses in hippocampal neurons and is required for activity-driven synapse stabilization (33–35). In juvenile PSD-95 KO mice, ODP displays the same features as in WT mice (36). However, as adult PSD-95 KO mice have not yet been analyzed, it is unknown whether PSD-95 is essential for the closure of critical periods. Thus, PSD-95 appeared to be the ideal molecular candidate to test our conceptual model that progressive silent synapse maturation marks the closure of critical periods.     

From the Cover: Neurons selective to the number of visual items in the corvid songbird endbrain

It is unknown whether anatomical specializations in the endbrains of different vertebrates determine the neuronal code to represent numerical quantity. Therefore, we recorded single-neuron activity from the endbrain of crows trained to judge the number of items in displays. Many neurons were tuned for numerosities irrespective of the physical appearance of the items, and their activity correlated with performance outcome. Comparison of both behavioral and neuronal representations of numerosity revealed that the data are best described by a logarithmically compressed scaling of numerical information, as postulated by the Weber–Fechner law. The behavioral and neuronal numerosity representations in the crow reflect surprisingly well those found in the primate association cortex. This finding suggests that distantly related vertebrates with independently developed endbrains adopted similar neuronal solutions to process quantity.Birds show elaborate quantification skills (1–3) that are of adaptive value in naturalistic situations like nest parasitism (4), food caching (5), or communication (6). The neuronal correlates of numerosity representations have only been explored in humans (7–9) and primates (10–18), and they have been found to reside in the prefrontal and posterior parietal neocortices. In contrast to primates, birds lack a six-layered neocortex. The birds’ lineage diverged from mammals 300 Mya (19), at a time when the neocortex had not yet developed from the pallium of the endbrain. Instead, birds developed different pallial parts as dominant endbrain structures (20, 21) based on convergent evolution, with the nidopallium caudolaterale (NCL) as a high-level association area (22–26). Where and how numerosity is encoded in vertebrates lacking a neocortex is unknown. Here, we show that neurons in the telencephalic NCL of corvid songbirds respond to numerosity and show a specific code for numerical information.     

From the Cover: PNAS Plus: Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans

The ability to acquire large-scale recordings of neuronal activity in awake and unrestrained animals is needed to provide new insights into how populations of neurons generate animal behavior. We present an instrument capable of recording intracellular calcium transients from the majority of neurons in the head of a freely behaving Caenorhabditis elegans with cellular resolution while simultaneously recording the animal’s position, posture, and locomotion. This instrument provides whole-brain imaging with cellular resolution in an unrestrained and behaving animal. We use spinning-disk confocal microscopy to capture 3D volumetric fluorescent images of neurons expressing the calcium indicator GCaMP6s at 6 head-volumes/s. A suite of three cameras monitor neuronal fluorescence and the animal’s position and orientation. Custom software tracks the 3D position of the animal’s head in real time and two feedback loops adjust a motorized stage and objective to keep the animal’s head within the field of view as the animal roams freely. We observe calcium transients from up to 77 neurons for over 4 min and correlate this activity with the animal’s behavior. We characterize noise in the system due to animal motion and show that, across worms, multiple neurons show significant correlations with modes of behavior corresponding to forward, backward, and turning locomotion.How do patterns of neural activity generate an animal’s behavior? To answer this question, it is important to develop new methods for recording from large populations of neurons in animals as they move and behave freely. The collective activity of many individual neurons appears to be critical for generating behaviors including arm reach in primates (1), song production in zebrafinch (2), the choice between swimming or crawling in leech (3), and decision-making in mice during navigation (4). New methods for recording from larger populations of neurons in unrestrained animals are needed to better understand neural coding of these behaviors and neural control of behavior more generally.Calcium imaging has emerged as a promising technique for recording dynamics from populations of neurons. Calcium-sensitive proteins are used to visualize changes in intracellular calcium levels in neurons in vivo which serve as a proxy for neural activity (5). To resolve the often weak fluorescent signal of an individual neuron in a dense forest of other labeled cells requires a high magnification objective with a large numerical aperture that, consequently, can image only a small field of view. Calcium imaging has traditionally been performed on animals that are stationary from anesthetization or immobilization to avoid imaging artifacts induced by animal motion. As a result, calcium imaging studies have historically focused on small brain regions in immobile animals that exhibit little or no behavior (6).No previous neurophysiological study has attained whole-brain imaging with cellular resolution in a freely behaving unrestrained animal. Previous whole-brain cellular resolution calcium imaging studies of populations of neurons that involve behavior investigate either fictive locomotion (3, 7), or behaviors that can be performed in restrained animals, such as eye movements (8) or navigation of a virtual environment (9). One exception has been the development of fluorescence endoscopy, which allows recording from rodents during unrestrained behavior, although imaging is restricted to even smaller subbrain regions (10).Investigating neural activity in small transparent organisms allows one to move beyond studying subbrain regions to record dynamics from entire brains with cellular resolution. Whole-brain imaging was performed first in larval zebrafish using two-photon microscopy (7). More recently, whole-brain imaging was performed in Caenorhabditis elegans using two-photon (11) and light-field microscopy (12). Animals in these studies were immobilized, anesthetized, or both and thus exhibited no gross behavior.C. elegans’ compact nervous system of only 302 neurons and small size of only 1 mm make it ideally suited for the development of new whole-brain imaging techniques for studying behavior. There is a long and rich history of studying and quantifying the behavior of freely moving C. elegans dating back to the mid-1970s (13, 14). Many of these works involved quantifying animal body posture as the worm moved, for example as in ref. 15. To facilitate higher-throughput recordings of behavior, a number of tracking microscopes (16–18) or multiworm imagers were developed (19, 20) along with sophisticated behavioral analysis software and analytical tools (21, 22). Motivated in part to understand these behaviors, calcium imaging systems were also developed that could probe neural activity in at first partially immobilized (23) and then freely moving animals, beginning with ref. 24 and and then developing rapidly (17, 18, 25–29). One limitation of these freely moving calcium imaging systems is that they are limited to imaging only a very small subset of neurons and lack the ability to distinguish neurons that lie atop one another in the axial direction of the microscope. Despite this limitation, these studies, combined with laser-ablation experiments, have identified a number of neurons that correlate or affect changes in particular behaviors including the AVB neuron pair and VB-type motor neurons for forward locomotion; the AVA, AIB, and AVE neuron pairs and VA-type motor neurons for backward locomotion; and the RIV, RIB, and SMD neurons and the DD-type motor neurons for turning behaviors (17, 18, 25, 26, 28, 30, 31). To move beyond these largely single-cell studies, we sought to record simultaneously from the entire brain of C. elegans with cellular resolution and record its behavior as it moved about unrestrained.     

Drosophila seminal protein ovulin mediates ovulation through female octopamine neuronal signaling

Across animal taxa, seminal proteins are important regulators of female reproductive physiology and behavior. However, little is understood about the physiological or molecular mechanisms by which seminal proteins effect these changes. To investigate this topic, we studied the increase in Drosophila melanogaster ovulation behavior induced by mating. Ovulation requires octopamine (OA) signaling from the central nervous system to coordinate an egg’s release from the ovary and its passage into the oviduct. The seminal protein ovulin increases ovulation rates after mating. We tested whether ovulin acts through OA to increase ovulation behavior. Increasing OA neuronal excitability compensated for a lack of ovulin received during mating. Moreover, we identified a mating-dependent relaxation of oviduct musculature, for which ovulin is a necessary and sufficient male contribution. We report further that oviduct muscle relaxation can be induced by activating OA neurons, requires normal metabolic production of OA, and reflects ovulin’s increasing of OA neuronal signaling. Finally, we showed that as a result of ovulin exposure, there is subsequent growth of OA synaptic sites at the oviduct, demonstrating that seminal proteins can contribute to synaptic plasticity. Together, these results demonstrate that ovulin increases ovulation through OA neuronal signaling and, by extension, that seminal proteins can alter reproductive physiology by modulating known female pathways regulating reproduction.Throughout internally fertilizing animals, seminal proteins play important roles in regulating female fertility by altering female physiology and, in some cases, behavior after mating (reviewed in refs. 1–3). Despite this, little is understood about the physiological mechanisms by which seminal proteins induce postmating changes and how their actions are linked with known networks regulating female reproductive physiology.In Drosophila melanogaster, the suite of seminal proteins has been identified, as have many seminal protein-dependent postmating responses, including changes in egg production and laying, remating behavior, locomotion, feeding, and in ovulation rate (reviewed in refs. 2 and 3). For example, the Drosophila seminal protein ovulin elevates ovulation rate to maximal levels during the 24 h following mating (4, 5), and the seminal protein sex peptide (SP) suppresses female mating receptivity and increases egg-laying behavior for several days after mating (6–10). However, although a receptor for SP has been identified (11), along with elements of the neural circuit in which it is required (12–14), SP’s mechanism of action has not yet been linked to regulatory networks known to control postmating behaviors. Thus, a crucial question remains: how do male-derived seminal proteins interact with regulatory networks in females to trigger postmating responses?We addressed this question by examining the stimulation of Drosophila ovulation by the seminal protein ovulin. In insects, ovulation, defined here as the release of an egg from the ovary to the uterus, is among the best understood reproductive processes in terms of its physiology and neurogenetics (15–27). In D. melanogaster, ovulation requires input from neurons in the abdominal ganglia that release the catecholaminergic neuromodulators octopamine (OA) and tyramine (17, 18, 28). Drosophila ovulation also requires an OA receptor, OA receptor in mushroom bodies (OAMB) (19, 20). Moreover, it has been proposed that OA may integrate extrinsic factors to regulate ovulation rates (17). Noradrenaline, the vertebrate structural and functional equivalent to OA (29, 30), is important for mammalian ovulation, and its dysregulation has been associated with ovulation disorders (31–38). In this paper we investigate the role of neurons that release OA and tyramine in ovulin’s action. For simplicity, we refer to these neurons as “OA neurons” to reflect the well-established role of OA in ovulation behavior (16–20, 22).We investigated how action of the seminal protein ovulin relates to the conserved canonical neuromodulatory pathway that regulates ovulation physiology (39–41). We found that ovulin increases ovulation and egg laying through OA neuronal signaling. We also found that ovulin relaxes oviduct muscle tonus, a postmating process that is also mediated by OA neuronal signaling. Finally, subsequent to these effects we detected an ovulin-dependent increase in synaptic sites between OA motor neurons and oviduct muscle, suggesting that ovulin’s stimulation of OA neurons could have increased their synaptic activity. These results suggest that ovulin affects ovulation by manipulating the gain of a neuromodulatory pathway regulating ovulation physiology.     

Distinct dopamine neurons mediate reward signals for short- and long-term memories

Drosophila melanogaster can acquire a stable appetitive olfactory memory when the presentation of a sugar reward and an odor are paired. However, the neuronal mechanisms by which a single training induces long-term memory are poorly understood. Here we show that two distinct subsets of dopamine neurons in the fly brain signal reward for short-term (STM) and long-term memories (LTM). One subset induces memory that decays within several hours, whereas the other induces memory that gradually develops after training. They convey reward signals to spatially segregated synaptic domains of the mushroom body (MB), a potential site for convergence. Furthermore, we identified a single type of dopamine neuron that conveys the reward signal to restricted subdomains of the mushroom body lobes and induces long-term memory. Constant appetitive memory retention after a single training session thus comprises two memory components triggered by distinct dopamine neurons.Memory of a momentous event persists for a long time. Whereas some forms of long-term memory (LTM) require repetitive training (1–3), a highly relevant stimulus such as food or poison is sufficient to induce LTM in a single training session (4–7). Recent studies have revealed aspects of the molecular and cellular mechanisms of LTM formation induced by repetitive training (8–11), but how a single training induces a stable LTM is poorly understood (12).Appetitive olfactory learning in fruit flies is suited to address the question, as a presentation of a sugar reward paired with odor induces robust short-term memory (STM) and LTM (6, 7). Odor is represented by a sparse ensemble of the 2,000 intrinsic neurons, the Kenyon cells (13). A current working model suggests that concomitant reward signals from sugar ingestion cause associative plasticity in Kenyon cells that might underlie memory formation (14–20). A single activation session of a specific cluster of dopamine neurons (PAM neurons) by sugar ingestion can induce appetitive memory that is stable over 24 h (19), underscoring the importance of sugar reward to the fly.The mushroom body (MB) is composed of the three different cell types, α/β, α′/β′, and γ, which have distinct roles in different phases of appetitive memories (11, 21–25). Similar to midbrain dopamine neurons in mammals (26, 27), the structure and function of PAM cluster neurons are heterogeneous, and distinct dopamine neurons intersect unique segments of the MB lobes (19, 28–34). Further circuit dissection is thus crucial to identify candidate synapses that undergo associative modulation.By activating distinct subsets of PAM neurons for reward signaling, we found that short- and long-term memories are independently formed by two complementary subsets of PAM cluster dopamine neurons. Conditioning flies with nutritious and nonnutritious sugars revealed that the two subsets could represent different reinforcing properties: sweet taste and nutritional value of sugar. Constant appetitive memory retention after a single training session thus comprises two memory components triggered by distinct reward signals.