Analog VLSI Circuits for Short-Term Dynamic Synapses
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Analog VLSI Circuits for Short-Term Dynamic Synapses Shih-Chii Liu Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland Email: [email protected] Received 14 May 2002 and in revised form 25 September 2002 Short-term dynamical synapses increase the computational power of neuronal networks. These synapses act as additional filters to the inputs of a neuron before the subsequent integration of these signals at its cell body. In this work, we describe a model of depressing and facilitating synapses derived from a hardware circuit implementation. This model is equivalent to theoretical models of short-term synaptic dynamics in network simulations. These circuits have been added to a network of leaky integrateand-fire neurons. A cortical model of direction-selectivity that uses short-term dynamic synapses has been implemented with this network. Keywords and phrases: short-term synaptic dynamics, depression, facilitation, silicon synapse, cortical models.
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INTRODUCTION
Cortical neurons show a wide variety of neuronal and synaptic responses to their input signals. Networks with simplified models of spiking neurons and synapses and consisting of one or two time constants already exhibit a large number of possible operating regimes [1, 2]. Simulations of these spiking networks can take a long time on a serial computer. In most network simulations, synapses are assumed to be static. Recent physiological data, however, show that synapses frequently show activity-dependent plasticity which vary on a time scale of milliseconds to seconds. In particular, shortterm dynamical synapses [3, 4, 5, 6, 7] with time constants of hundreds of milliseconds are seen in many parts of the visual cortex. When these synapses are stimulated with a train of input spikes, the amplitude of the membrane potential of the neuron or the excitatory postsynaptic potential (EPSP) decreases (depressing synapse) or increases (facilitating synapse) with each subsequent spike. The recovery time of the maximum synaptic amplitude is in the order of hundreds of milliseconds. These synapses encode the history of their inputs and can be treated as time-invariant filters with fading memory [8]. These activity-dependent synapses, when added to the network, allow for different forms of dynamical networks that can process time-varying patterns [9, 10]. Examples of how these synapses could contribute to visual cortical responses include direction selectivity [11] and automatic gain control [12]. The simulation time of spiking networks with different types of activity-dependent synapses consisting of different time constants will increase significantly. This simulation time can be shortened by using a hardware implemen-
tation of a network with spiking neurons and these activitydependent synapses. Here, we describe a circuit model of short-term synaptic dynamics based on the silicon implementation of synaptic depression and facilitation in [13]. The dynamics of this circuit model is qualitatively com
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