Action-specific extrapolation of target motion in human visual system

Neuropsychological studies have indicated two distinct visual pathways in our brain, one dedicated to conscious perception and one to visuomotor control. Some psychophysical results support this idea with normal subjects, but they are still controversial. This study provides new psychophysical evidence for the dissociation by showing action-specific extrapolation of the visual target trajectory. When a moving target disappears, the perceived final position is liable to be shifted forward (representational momentum). In experiment 1, larger and more robust forward shifts were found when the position was directly touched without seeing the screen (open-loop pointing) than when the position was judged perceptually. The most striking dissociation was that fixation did not affect the forward shift in open-loop pointing while it almost abolished the shifts in perceptual judgements. In experiment 2, this action-specific result was found to disappear after a response delay of 4000 ms. Experiments 3 and 4 confirmed that the results were not affected by the external reference frames. The specific forward shifts found in open-loop pointing suggest that the visuomotor system compensates for the neural delays by extrapolating the target motion. The results, together with earlier findings, lead to a psychophysical double dissociation of the two visual pathways.     

Neuronal latencies and the position of moving objects

Neuronal latencies delay the registration of the visual signal from a moving object. By the time the visual input reaches brain structures that encode its position, the object has already moved on. Do we perceive the position of a moving object with a delay because of neuronal latencies? Or is there a brain mechanism that compensates for latencies such that we perceive the true position of a moving object in real time? This question has been intensely debated in the context of the flash-lag illusion: a moving object and an object flashed in alignment with it appear to occupy different positions. The moving object is seen ahead of the flash. Does this show that the visual system extrapolates the position of moving objects into the future to compensate for neuronal latencies? Alternative accounts propose that it simply shows that moving and flashed objects are processed with different delays, or that it reflects temporal integration in brain areas that encode position and motion. The flash-lag illusion and the hypotheses put forward to explain it lead to interesting questions about the encoding of position in the brain. Where is the 'where' pathway and how does it work?     

How does non-random spontaneous activity contribute to brain development?

Highly non-random forms of spontaneous activity are proposed to play an instrumental role in the early development of the visual system. However, both the fundamental properties of spontaneous activity required to drive map formation, as well as the exact role of this information remain largely unknown. Here, a realistic computational model of spontaneous retinal waves is employed to demonstrate that both the amplitude and frequency of waves may play determining roles in retinocollicular map formation. Furthermore, results obtained with different learning rules show that spike precision in the order of milliseconds may be instrumental to neural development: a rule based on precise spike interactions (spike-timing-dependent plasticity) reduced the density of aberrant projections to the SC to a markedly greater extent than a rule based on interactions at much broader time-scale (correlation-based plasticity). Taken together, these results argue for an important role of spontaneous yet highly non-random activity, along with temporally precise learning rules, in the formation of neural circuits.     

Object recognition by artificial cortical maps.

Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.     

How feedback inhibition shapes spike-timing-dependent plasticity and its implications for recent Schizophrenia models

It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here, we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing-dependent plasticity and show analytically that the feedback inhibition shortens the time window for LTD during spike-timing-dependent plasticity but not for LTP. We then use a realistic GENESIS model to test two hypothesis about interneuron hypofunction and conclude that a reduction in GAD67 is the most likely candidate as the cause for hypofrontality as observed in Schizophrenia.     

Putative roles for homeostatic plasticity in epileptogenesis

Homeostatic plasticity allows neural circuits to maintain an average activity level while preserving the ability to learn new associations and efficiently transmit information. This dynamic process usually protects the brain from excessive activity, like seizures. However, in certain contexts, homeostatic plasticity might produce seizures, either in response to an acute provocation or more chronically as a driver of epileptogenesis. Here, we review three seizure conditions in which homeostatic plasticity likely plays an important role: acute drug withdrawal seizures, posttraumatic or disconnection epilepsy, and cyclic seizures. Identifying the homeostatic mechanisms active at different stages of development and in different circuits could allow better targeting of therapies, including determining when neuromodulation might be most effective, proposing ways to prevent epileptogenesis, and determining how to disrupt the cycle of recurring seizure clusters.     

Cross-modal and scale-free action representations through enaction

Embodied action representation and action understanding are the first steps to understand what it means to communicate. We present a biologically plausible mechanism to the representation and the recognition of actions in a neural network with spiking neurons based on the learning mechanism of spike-timing-dependent plasticity (STDP). We show how grasping is represented through the multi-modal integration between the vision and tactile maps across multiple temporal scales. The network evolves into a small-world organization with scale-free dynamics promoting efficient inter-modal binding of the neural assemblies with accurate timing. Finally, it acquires the qualitative properties of the mirror neuron system to trigger an observed action performed by someone else.     

Inability of Rhesus Monkey Area V1 to Discriminate Between Self-induced and Externally Induced Retinal Image Slip

Retinal image slip can result from an eye movement across a stationary object or alternatively from motion of the object while the eyes are stationary. The ability to discriminate between these two kinds of retinal image slip is necessary for the perception of a stable visual world. In order to determine if this ability is already present in monkey visual area V1, we asked if single V1 units are able to differentiate between externally and self-induced retinal image slip. Externally induced retinal image slip was realized in the‘object motion’condition (OMC) by moving a behaviourally irrelevant visual stimulus (‘object’: a bar or a large random dot pattern) across the receptive field while the monkey fixated a small, stationary target. Conversely, self-induced retinal image slip of comparable size was evoked in the‘ego motion’condition (EMC) by asking the monkey to pursue the target, moving at the speed of the object in the OMC, while the object was kept stationary. We recorded 221 units from visual area V1, 51 (23%) of them directionally selective, and compared their responses to self-induced and externally induced retinal image slip. Many of them seemed to give some preference to externally induced retinal image slip. However, on closer examination it became clear that this seeming preference could be attributed to the fact that oculomotor performance was less precise in the EMC than in the OMC, causing a larger deviation from the optimal retinal image trajectory in the EMC. We show that the impact of eye position errors can be eliminated by the use of a position-invariant stimulus, such as large-field random dot patterns. We then show that the impact of both eye position errors and deviation of eye velocity from target velocity in the EMC can be eliminated by moving the stimulus in a given OMC trial according to an inverted replica of the eye movement trajectory in the preceding EMC trial, guaranteeing identical retinal stimulation in the OMC and the EMC. If identical retinal stimulation was ensured, none of the V1 units tested was able to differentiate between externally and self-induced retinal image slip. We conclude that V1 does not contribute to the perception of a world which is stable despite eye movements.     

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain.     

Does the fusion pore contribute to synaptic plasticity?

How synapses change their strength in response to impinging neural activity is a fundamental issue for understanding how the brain molds its circuitry in response to behavioral experience. Although a growing number of studies reveal the involvement of postsynaptic changes contributing to synaptic plasticity in brain circuits, the involvement of presynaptic factors has been implied by several studies. Most recently, several works point to the mechanism of vesicle fusion as a new possible locus for the modification of presynaptic synaptic strength. However, it is not yet clear to what extent such changes affect the simplest form of information transfer in the brain--the transmission of a single action potential.     

Enhanced long-term synaptic depression in an animal model of depression.

BACKGROUND: A growing body of evidence suggests a disturbance of brain plasticity in major depression. In contrast to hippocampal neurogenesis, much less is known about the role of synaptic plasticity. Long-term potentiation (LTP) and long-term depression (LTD) regulate the strength of synaptic transmission and the formation of new synapses in many neural networks. Therefore, we examined the modulation of synaptic plasticity in the chronic mild stress animal model of depression. METHODS: Adult rats were exposed to mild and unpredictable stressors for 3 weeks. Thereafter, long-term synaptic plasticity was examined in the hippocampal CA1 region by whole-cell patch clamp measurements in brain slices. Neurogenesis was assessed by doublecortin immunostaining. RESULTS: Exposure to chronic mild stress facilitated LTD and had no effect on LTP. Chronic application of the antidepressant fluvoxamine during the stress protocol prevented the facilitation of LTD and increased the extent of LTP induction. Neurogenesis in the dentate gyrus was impaired after chronic stress. CONCLUSIONS: In addition to neurogenesis, long-term synaptic plasticity is an important and ubiquitous form of brain plasticity that is disturbed in an animal model of depression. Facilitated depression of synaptic transmission might impair function and structure of brain circuits involved in the pathophysiology of major depression. Antidepressants might counteract these alterations.     

Stable learning in stochastic network states

The mammalian cerebral cortex is characterized in vivo by irregular spontaneous activity, but how this ongoing dynamics affects signal processing and learning remains unknown. The associative plasticity rules demonstrated in vitro, mostly in silent networks, are based on the detection of correlations between presynaptic and postsynaptic activity and hence are sensitive to spontaneous activity and spurious correlations. Therefore, they cannot operate in realistic network states. Here, we present a new class of spike-timing-dependent plasticity learning rules with local floating plasticity thresholds, the slow dynamics of which account for metaplasticity. This novel algorithm is shown to both correctly predict homeostasis in synaptic weights and solve the problem of asymptotic stable learning in noisy states. It is shown to naturally encompass many other known types of learning rule, unifying them into a single coherent framework. The mixed presynaptic and postsynaptic dependency of the floating plasticity threshold is justified by a cascade of known molecular pathways, which leads to experimentally testable predictions.     

Sources of Uncertainty in Intuitive Physics

Recent work suggests that people predict how objects interact in a manner consistent with Newtonian physics, but with additional uncertainty. However, the sources of uncertainty have not been examined. In this study, we measure perceptual noise in initial conditions and stochasticity in the physical model used to make predictions. Participants predicted the trajectory of a moving object through occluded motion and bounces, and we compared their behavior to an ideal observer model. We found that human judgments cannot be captured by simple heuristics and must incorporate noisy dynamics. Moreover, these judgments are biased consistently with a prior expectation on object destinations, suggesting that people use simple expectations about outcomes to compensate for uncertainty about their physical models.     

Passive immunotherapy rapidly increases structural plasticity in a mouse model of Alzheimer disease

Senile plaque-associated changes in neuronal connectivity such as altered neurite trajectory, dystrophic swellings, and synapse and dendritic spine loss are thought to contribute to cognitive dysfunction in Alzheimer's disease and mouse models. Immunotherapy to remove amyloid beta is a promising therapy that causes recovery of neurite trajectory and dystrophic neurites over a period of days. The acute effects of immunotherapy on neurite morphology at a time point when soluble amyloid has been cleared but dense plaques are not yet affected are unknown. To examine whether removal of soluble amyloid β (Aβ) has a therapeutic effect on dendritic spines, we explored spine dynamics within 1 h of applying a neutralizing anti Aβ antibody. This acute treatment caused a small but significant increase in dendritic spine formation in PDAPP brain far from plaques, without affecting spine plasticity near plaques or average dendritic spine density. These data support the hypothesis that removing toxic soluble forms of amyloid-beta rapidly increases structural plasticity possibly allowing functional recovery of neural circuits.     

Transcranial magnetic stimulation as a method for investigating the plasticity of the brain in Parkinson's disease and dystonia

It is now possible to probe the plasticity of some neural circuits in the human motor cortex using transcranial magnetic stimulation (TMS). This article illustrates how changes in the plasticity of these circuits is linked to the expression of dyskinesias in Parkinson's disease, and may even underlie the tendency of some individuals to develop focal dystonia. Indeed, gradual normalisation of this excessive plasticity occurs after initiating deep brain stimulation of the internal globus pallidus in patients with generalised dystonia. It may therefore relate to the slow onset of clinical improvement that occurs over the first 6 weeks or so of treatment.     

Addiction-induced plasticity in underlying neural circuits

Neurological Sciences - Synaptic plasticity, the substrate for learning, has been established in neural reward circuits and might involve in the learning of addictive behaviors. Long-term exposure...     

A model of STDP based on spatially and temporally local information: derivation and combination with gated decay.

Temporal relationships between neuronal firing and plasticity have received significant attention in recent decades. Neurophysiological studies have shown the phenomenon of spike-timing-dependent plasticity (STDP). Various models were suggested to implement an STDP-like learning rule in artificial networks based on spiking neuronal representations. The rule presented here was developed under three constraints. First, it only depends on the information that is available at the synapse at the time of synaptic modification. Second, it naturally follows from neurophysiological and psychological research starting with Hebb's postulate [D. Hebb. (1949). The organization of behavior. Wiley, New York]. Third, it is simple, computationally cheap and its parameters are straightforward to determine. This rule is further extended by addition of four different types of gating derived from conventionally used types of gated decay in learning rules for continuous firing rate neural networks. The results show that the advantages of using these gatings are transferred to the new rule without sacrificing its dependency on spike-timing.     

Neural Development of Speech Sensorimotor Learning

The development of the human brain continues through to early adulthood. It has been suggested that cortical plasticity during this protracted period of development shapes circuits in associative transmodal regions of the brain. Here we considered how cortical plasticity during development might contribute to the coordinated brain activity required for speech motor learning. Specifically, we examined patterns of brain functional connectivity (FC), whose strength covaried with the capacity for speech audio-motor adaptation in children ages 5–12 and in young adults of both sexes. Children and adults showed distinct patterns of the encoding of learning in the brain. Adult performance was associated with connectivity in transmodal regions that integrate auditory and somatosensory information, whereas children rely on basic somatosensory and motor circuits. A progressive reliance on transmodal regions is consistent with human cortical development and suggests that human speech motor adaptation abilities are built on cortical remodeling, which is observable in late childhood and is stabilized in adults.SIGNIFICANCE STATEMENT A protracted period of neuro plasticity during human development is associated with extensive reorganization of associative cortex. We examined how the relationship between FC and speech motor learning capacity are reconfigured in conjunction with this cortical reorganization. Young adults and children aged 5–12 years showed distinctly different patterns. Mature brain networks related to learning included associative cortex, which integrates auditory and somatosensory feedback in speech, whereas the immature networks in children included motor regions of the brain. These patterns are consistent with the cortical reorganization that is initiated in late childhood. The result provides insights into the human biology of speech as well as to the mature neural mechanisms for multisensory integration in motor learning.     

Delayed in vitro development of Up states but normal network plasticity in Fragile X circuits

A broad range of neurophysiological phenotypes have been reported since the generation of the first mouse model of Fragile X syndrome (FXS). However, it remains unclear which phenotypes are causally related to the cognitive deficits associated with FXS. Indeed, because many of these phenotypes are known to be modulated by experience, a confounding factor in the interpretation of many studies is whether some phenotypes are an indirect consequence of abnormal development and experience. To help diminish this confound we first conducted an in vitro developmental study of spontaneous neural dynamics in cortical organotypic cultures. A significant developmental increase in network activity and Up states was observed in both wild‐type and Fmr1^?/y circuits, along with a specific developmental delay in the emergence of Up states in knockout circuits. To determine whether Up state regulation is generally impaired in FXS circuits, we examined Up state plasticity using chronic optogenetic stimulation. Wild‐type and Fmr1^?/y stimulated circuits exhibited a significant decrease in overall spontaneous activity including Up state frequency; however, no significant effect of genotype was observed. These results demonstrate that developmental delays characteristic of FXS are recapitulated during in vitro development, and that Up state abnormalities are probably a direct consequence of the disease, and not an indirect consequence of abnormal experience. However, the fact that Fmr1^?/y circuits exhibited normal homeostatic modulation of Up states suggests that these plasticity mechanisms are largely intact, and that some of the previously reported plasticity deficits could reflect abnormal experience or the engagement of compensatory mechanisms.     

Spiking neurons, dopamine, and plasticity: timing is everything, but concentration also matters

While both dopamine (DA) fluctuations and spike-timing-dependent plasticity (STDP) are known to influence long-term corticostriatal plasticity, little attention has been devoted to the interaction between these two fundamental mechanisms. Here, a theoretical framework is proposed to account for experimental results specifying the role of presynaptic activation, postsynaptic activation, and concentrations of extracellular DA in synaptic plasticity. Our starting point was an explicitly-implemented multiplicative rule linking STDP to Michaelis-Menton equations that models the dynamics of extracellular DA fluctuations. This rule captures a wide range of results on conditions leading to long-term potentiation and depression in simulations that manipulate the frequency of induced corticostriatal stimulation and DA release. A well-documented biphasic function relating DA concentrations to synaptic plasticity emerges naturally from simulations involving a multiplicative rule linking DA and neural activity. This biphasic function is found consistently across different neural coding schemes employed (voltage-based vs. spike-based models). By comparison, an additive rule fails to capture these results. The proposed framework is the first to generate testable predictions on the dual influence of DA concentrations and STDP on long-term plasticity, suggesting a way in which the biphasic influence of DA concentrations can modulate the direction and magnitude of change induced by STDP, and raising the possibility that DA concentrations may inverse the LTP/LTD components of the STDP rule.