Jeffrey P. Gavornik

Spontaneous and Evoked Release Are Independently Regulated at Individual Active Zones

Neurotransmitter release from synaptic vesicle fusion is the fundamental mechanism for neuronal communication at synapses. Evoked release following an action potential has been well characterized for its function in activating the postsynaptic cell, but the significance of spontaneous release is less clear. Using transgenic tools to image single synaptic vesicle fusion events at individual release sites (active zones) in Drosophila, we characterized the spatial and temporal dynamics of exocytotic events that occur spontaneously or in response to an action potential. We also analyzed the relationship between these two modes of fusion at single release sites. A majority of active zones participate in both modes of fusion, although release probability is not correlated between the two modes of release and is highly variable across the population. A subset of active zones is specifically dedicated to spontaneous release, indicating a population of postsynaptic receptors is uniquely activated by this mode of vesicle fusion. Imaging synaptic transmission at individual release sites also revealed general rules for spontaneous and evoked release, and indicate that active zones with similar release probability can cluster spatially within individual synaptic boutons. These findings suggest neuronal connections contain two information channels that can be spatially segregated and independently regulated to transmit evoked or spontaneous fusion signals.

Rapid Structural Remodeling of Thalamocortical Synapses Parallels Experience-Dependent Functional Plasticity in Mouse Primary Visual Cortex

Monocular lid closure (MC) causes a profound shift in the ocular dominance (OD) of neurons in primary visual cortex (V1). Anatomical studies in both cat and mouse V1 suggest that large-scale structural rearrangements of eye-specific thalamocortical (TC) axons in response to MC occur much more slowly than the shift in OD. Consequently, there has been considerable debate as to whether the plasticity of TC synapses, which transmit competing visual information from each eye to V1, contributes to the early functional consequences of MC or is simply a feature of long-term deprivation. Here, we used quantitative immuno-electron microscopy to examine the possibility that alterations of TC synapses occur rapidly enough to impact OD after brief MC. The effect of short-term deprivation on TC synaptic structure was examined in male C57BL/6 mice that underwent 3 and 7 d of MC or monocular retinal inactivation (MI) with tetrodotoxin. The data show that 3 d of MC is sufficient to induce substantial remodeling of TC synapses. In contrast, 3 d of MI, which alters TC activity but does not shift OD, does not significantly affect the structure of TC synapses. Our results support the hypothesis that the rapid plasticity of TC synapses is a key step in the sequence of events that shift OD in visual cortex.

Learning reward timing in cortex through reward dependent expression of synaptic plasticity

The ability to represent time is an essential component of cognition but its neural basis is unknown. Although extensively studied both behaviorally and electrophysiologically, a general theoretical framework describing the elementary neural mechanisms used by the brain to learn temporal representations is lacking. It is commonly believed that the underlying cellular mechanisms reside in high order cortical regions but recent studies show sustained neural activity in primary sensory cortices that can represent the timing of expected reward. Here, we show that local cortical networks can learn temporal representations through a simple framework predicated on reward dependent expression of synaptic plasticity. We assert that temporal representations are stored in the lateral synaptic connections between neurons and demonstrate that reward-modulated plasticity is sufficient to learn these representations. We implement our model numerically to explain reward-time learning in the primary visual cortex (V1), demonstrate experimental support, and suggest additional experimentally verifiable predictions.

A network of spiking neurons that can represent interval timing: mean field analysis

Despite the vital importance of our ability to accurately process and encode temporal information, the underlying neural mechanisms are largely unknown. We have previously described a theoretical framework that explains how temporal representations, similar to those reported in the visual cortex, can form in locally recurrent cortical networks as a function of reward modulated synaptic plasticity. This framework allows networks of both linear and spiking neurons to learn the temporal interval between a stimulus and paired reward signal presented during training. Here we use a mean field approach to analyze the dynamics of non-linear stochastic spiking neurons in a network trained to encode specific time intervals. This analysis explains how recurrent excitatory feedback allows a network structure to encode temporal representations.

A single spiking neuron that can represent interval timing: analysis, plasticity and multi-stability

The ability to represent interval timing is crucial for many common behaviors, such as knowing whether to stop when the light turns from green to yellow. Neural representations of interval timing have been reported in the rat primary visual cortex and we have previously presented a computational framework describing how they can be learned by a network of neurons. Recent experimental and theoretical results in entorhinal cortex have shown that single neurons can exhibit persistent activity, previously thought to be generated by a network of neurons. Motivated by these single neuron results, we propose a single spiking neuron model that can learn to compute and represent interval timing. We show that a simple model, reduced analytically to a single dynamical equation, captures the average behavior of the complete high dimensional spiking model very well. Variants of this model can be used to produce bi-stable or multi-stable persistent activity. We also propose a plasticity rule by which this model can learn to represent different intervals and different levels of persistent activity.

What does scalar timing tell us about neural dynamics

The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold.

A network model that can learn reward timing using reinforced expression of synaptic plasticity

Recent experimental results indicate that cells within theprimary visual cortex can learn to predict the time ofrewards associated with visual cues [1]. In this work, dif-ferent visual cues were paired with rewards at specific tem-poral offsets. Before training, neurons in visual cortexwere active only during the duration of the visual cue.After sufficient training neurons developed persistentactivity for a time period correlated with the timing ofreward.Recurrent connections in a neural network can be con-structed to set a desired network time constant that is dif-ferent from the time constants of the constituent neurons.However, it is not known how such a network can learnthe appropriate recurrent weights. A plasticity model thatis able to accomplish this must be sensitive to the timingof reward events that, at least initially, occur seconds afterthe activity in the network returns to its basal level. Inorder to learn the appropriate dynamics, this networkneeds to solve a temporal credit assignment problem. Inour model plasticity is an ongoing process changing therecurrent synaptic weights as a function of their activity; inthe absence of a reward signal this plasticity rapidlydecays. External reward signals allow permanent expres-sion of preceding plasticity events, reinforcing only thosewhich predict the reward. As a result, the network dynam-ics are altered and it develops time constants correlatedwith the timing of different rewards. As in other reinforce-ment learning models the reward signal is inhibited by thenetwork activity to produce a stable activity pattern.We have implemented these ideas in both abstract passiveintegrator networks and in more realistic integrate and firenetworks and obtained results that are qualitatively simi-lar to the experimental results. Further, we examine theimplications of different possible biophysical mecha-nisms and propose experiments to test which specificmechanism are involved.Support: NSF CRCNS grant number 0515285.