NIPS*99 Spike Timing Workshop Schedule

NIPS*99 Spike Timing Workshop Schedule and Abstracts

7:30AM Larry Abbott and Paul Munro
Welcome and introduction

8:15AM William B. Levy
The Time Span of Associative Synaptic Modification is Substantially Amplified by Neural Interactions
Abstract:
The asymmetric time span of associative modification is useful for storing information that creates predictions into the future. Unfortunately, biophysical limitations preclude a sufficiently long time span for many behaviorally-relevant predictions. Neuronal circuitry and network dynamics can produce a 10 to 100-fold increase in this time span.

8:40AM Mu-Ming Poo
Correlated Activity and Long-term Synaptic Modifications - Hebb's Postulate Revisited
Abstract:
In search of a cellular mechanism for associative learning, Hebb (1949) suggested that correlated excitation of pre- and postsynaptic neurons may lead to strengthening of the synaptic connection between them. Rigorous examination of the role of temporal patterns of pre- and postsynaptic spiking activity in synaptic modifications has only recently been carried out. I will summarize our efforts in studying the role of spike timing in activity-induced long-term potentiation (LTP) and long-term depression (LTD) in artificial networks of cultured rat hippocampal neurons and in the developing frog tectum. In both systems, we found that for repetitive spiking activities to induce LTP and LTD, they must appear in the pre- and postsynaptic cells within a critical time window. Synaptic inputs that are repetitively activated within 20 ms before the spiking of the postsynaptic neuron become potentiated, while inputs activated within 20 ms after the postsynaptic spiking become depressed. Both potentiation and depression depend on activation of NMDA-subtype of glutamate receptors and can be readily reversed by subsequent spiking activity of appropriate patterns. Thus correlated pre- and postsynaptic spiking are required for both strengthening and weakening of the synapse, but with an opposite requirement in the temporal sequence of spiking.

Input specificity is another inherent concept in the Hebb's postulate: Only synapses along the activated pathway become modified. I will also summarize our recent studies on the issue of synapse specificity in activity-induced synaptic modifications in defined networks of cultured hippocampal neurons. Our results showed that induction of LTP and LTD by repetitive stimulation of one connection in a defined network leads to a "propagation" of potentiation and depression selectively to other connections within the networks. Furthermore, we found evidence for induction of LTP and LTD at selective "remote" synaptic sites distant from the neurons that are repetitively stimulated. These remote modifications can be accounted for by synaptic modification due to positively- or negatively-correlated spiking at remote synaptic sites where pre- and postsynaptic cells are activated through separate pathways with transmission delays that happen to match interpulse-intervals associated with the repetitive stimuli. Through such a "delay-line" mechanism, temporal information coded in the timing of individual spikes can be converted and stored into spatially distributed patterns of persistent synaptic modifications in a neural network. Taken together, our results argue that specific rules of input specificity must be considered for activity-dependent synaptic modifications at the network level.

9:05AM

BREAK!

9:20AM Richard Kempter
Intrinsic Rate Normalization by Hebbian Rules: Spike-Based vs. Rate-Based Learning
Abstract:
Over a broad parameter regime, Hebbian learning leads to an intrinsic normalization of the mean firing rate of the postsynaptic neuron. Subtractive normalization of the weights is guaranteed if, in addition, the mean input rates are identical at all synapses. In a rate description, intrinsic rate normalization is most easily achieved with a negative factor in front of the correlation term in the learning rule, often called `anti-Hebbian' learning. It is shown that, for spike based learning, a distinction between Hebbian and `anti-Hebbian' rules is no longer possible. Learning is driven by correlations on the time scale of the learning window which may be positive even though the integral over the learning window is negative.

9:45AM Dan Feldman
Long-term plasticity induced by action potential-EPSP timing at layer II/III pyramidal cells in rat somatosensory cortex.
Abstract:
Long-term potentiation and depression (LTP and LTD) induced by the relative timing of EPSPs and postsynaptic spikes may contribute to experience-dependent plasticity of neuronal response properties in vivo. In the rat's primary somatosensory cortex (S1), plucking one whisker but sparing its neighbor causes neuronal responses to the deprived whisker in layer II/III to become rapidly depressed. This depression appears to be driven in part by activation of the spared whisker and can be readily explained by a form of timing-based, associative long-term plasticity recently observed in these cells in vitro. An unusual feature of this plasticity is that the range of spike-EPSP delays that induces LTD is much longer than the range of delays that induces LTP. This observation predicts that synapses that elicit subthreshold EPSPs in a manner uncorrelated with postsynaptic spiking will, over time, become depressed. In vivo, spontaneous spiking of inputs representing plucked whiskers will therefore drive depression of these same inputs. This mechanism may contribute to experience-dependent depression of sensory responses in other cortical areas as well.

10:10AM Sen Song
Spike-Timing Dependent Synaptic Plasticity Enables Groups of Correlated Neurons to Supervise Network Learning
Abstract:
Recent experiments have shown that the timing of pre- and post-synaptic action potentials plays a key role in the modification of synaptic efficacy. One of the striking features of such a form of plasticity is the asymmetry between pre- and post-synaptic neurons, namely the sharp transition from potentiation to depression depending on the order of spiking. In a recurrent network, this leads to the strengthening of the synapses formed by a highly correlated cluster of neurons onto other cells. These synapses can carry an instructional signal. A computational model is presented to illustrate this phenomenon. A layer of recurrently connected neurons receives inputs from a layer of neurons with Gaussian tuning curves. We present stimuli at random positions for periods chosen from an exponential distribution. This leads to the development of receptive fields at different random locations if the recurrents are turned off. We next seed a small group of neurons in the recurrent layer with feedforward connections tuned to the same location. These neurons became correlated and form strong connections to other untuned cells in the network. As a consequence of the recurrent connections, the other cells also become responsive to this particular location, forming a column-like structure. Once the proper feedforward connections are formed, i.e. the instructor has done its job, the recurrent connections weaken. Thus, in this example, the recurrent connections served as the vehicle for a small group of "teacher" neurons to supervise the development of other neurons in the network. The asymmetry in the learning is crucial in insuring the one-way information flow from the "teacher" to the "student".

10:10AM Kevin Franks
Simulated Dendritic Ca Influx through Ligand- and Voltage-Gated Channels
Abstract:
Calcium is an important intracellular second messenger in many biological systems and is, seemingly paradoxically, necessary for the induction of both LTP and LTD. We have used MCell, a Monte Carlo simulation package to simulate the influx of Ca²⁺ into a postsynaptic dendrite following pre- and postsynaptic activity and to track the binding of Ca²⁺ to endogenous proteins and fluorescent sensors. The order and spacing of the EPSP and back-propagating action potential has a large effect on the influx of Ca²⁺, and thus on intracellular Ca²⁺ concentration. Calcium binding proteins can both transduce Ca²⁺-dependent signaling cascades and buffer intracellular Ca²⁺ so as to dampen the initiation of such signals. Here we show how a specific, spatially restricted binding protein, calmodulin, can serve as a differentiator of [Ca²⁺], by virtue of its kinetics. Such a differentiator might serve to transduce a signal to either potentiate or depress a synapse depending on the spatio-temporal profile of the Ca²⁺ signal.

10:35AM DISCUSSION

11:00AM LUNCH & FREE TIME

4:30PM Mayank R. Mehta and Matthew A. Wilson
Effect of LTP on Spatio-Temporal Structure of Receptive Fields: Hippocampal Lessons for V1
Abstract:
Recent works have investigated the effect of patterns of neuronal activity and synaptic plasticity on the size and specificity of receptive fields. In this work we have investigate the effect of plasticity on the receptive field shape. Here we show that within a few traversals of a familiar environment a large fraction of hippocampal place fields become asymmetric such that, the firing rate of a place cell rises slowly as a rat enters a place field, but the firing rate drops off abruptly at the end of the place field. A computational model that can explain the results, based on NMDA dependent LTP, is presented. The model provides a mechanism underlying phase precession in the hippocampal neurons and the development of inseparable spatio-temporal and directionally selective receptive fields in the striate cortex. Such asymmetric receptive fields can allow an animal to predict an upcoming event, such as the location of a visual stimulus or the animal's location, based on previously experienced patterns of neuronal activity.

4:55PM Misha Tsodyks
An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Post-Synaptic Spike Timing
Abstract:
The precise times of pre- and post-synaptic action potentials play a key role in the modification of the synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm which reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and post-synaptic neurons. The primary feature of this algorithm is an asymmetry with respect to the direction of synaptic modification depending on whether the presynaptic spikes precede or follow the postsynaptic spike. In the case where neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of discharge converges towards a characteristic value which is determined by the pre- and post-synaptic firing rates. On the other hand, if the mean rates of the Poisson spike trains slowly change with time, our algorithm predicts modifications in the probability of release which generalize Hebbian and BCM rules.

5:20PM Rajesh Rao
Predictive Learning of Direction Selectivity in Recurrent Cortical Circuits
(with Margaret Livingstone and Terry Sejnowski)
Abstract:
When a spike is initiated near the soma of a cortical pyramidal neuron, it may backpropagate up dendrites toward distal synapses, where strong depolarization can trigger temporally asymmetric Hebbian plasticity at recently activated synapses. We first show that these mechanisms can implement a temporal-difference algorithm for sequence learning. We then show using biophysical simulations that a population of recurrently connected neurons with this form of synaptic plasticity can learn to predict spatiotemporal input patterns. In particular, we demonstrate that a network of cortical neurons, starting from a weak bias, can develop direction selectivity similar to that observed in complex cells in alert monkey visual cortex as a consequence of learning to predict moving stimuli.

5:45PM David Horn
Distributed Synchrony in a Hebbian Cell Assembly of Spiking Neurons
Abstract:
We investigate the formation of a Hebbian cell assembly of spiking neurons, using a temporal synaptic learning curve that is based on recent experimental findings. It includes potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occurring in the reverse order. The coupling between the dynamics of synaptic learning and that of neuronal activation leads to interesting results. One possible mode of activity is distributed synchrony, implying spontaneous division of the Hebbian cell assembly into groups, or subassemblies, of cells that fire in a cyclic manner. The behavior of distributed synchrony is investigated both by simulations and by analytic calculations of the resulting synaptic distributions.

6:25PM Break, Discussion, and Conclusion