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(Neural Computation. 2003;15:331-348.)
© 2003 The MIT Press

Letter

Modeling Short-Term Synaptic Depression in Silicon

Malte Boegerhausen

malte{at}ini.phys.ethz.ch, Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

Pascal Suter

psu{at}ini.phys.ethz.ch, Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

Shih-Chii Liu

shih{at}ini.phys.ethz.ch, Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses of this synaptic model are compared to the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons and different types of short-term dynamic synapses. We also show experimental data demonstrating the possible computational roles of depression. One possible role of a depressing synapse is that the input can quickly bring the neuron up to threshold when the membrane potential is close to the resting potential.




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C. Bartolozzi and G. Indiveri
Synaptic Dynamics in Analog VLSI
Neural Comput., October 1, 2007; 19(10): 2581 - 2603.
[Abstract] [Full Text] [PDF]


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