title: Modeling short-term synaptic depression in silicon.
abstract: 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.
authors.affiliation: Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. malte@ini.phys.ethz.ch
authors.forename: Malte
authors.initials: M
authors.lastname: Boegerhausen
authors.forename: Pascal
authors.initials: P
authors.lastname: Suter
authors.forename: Shih-Chii
authors.initials: SC
authors.lastname: Liu
mesh_terms: D036881:Long-Term Synaptic Depression / Q000502:physiology*; D008564:Membrane Potentials / Q000502:physiology; D016571:Neural Networks, Computer; D013569:Synapses / Q000502:physiology*