title: Spike-driven synaptic plasticity: theory, simulation, VLSI implementation.
abstract: We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite (stimulation) interval fluctuate, even at fixed pre- and postsynaptic spike rates. The dynamics of the single synapse is studied analytically by extending the solution to a classic problem in queuing theory (Takacs process). The model of the synapse is implemented in aVLSI and consists of only 18 transistors. It is also directly simulated. The simulations indicate that LTP/LTD probabilities versus rates are robust to fluctuations of the electronic parameters in a wide range of rates. The solutions for these probabilities are in very good agreement with both the simulations and measurements. Moreover, the probabilities are readily manipulable by variations of the chip's parameters, even in ranges where they are very small. The tests of the electronic device cover the range from spontaneous activity (3-4 Hz) to stimulus-driven rates (50 Hz). Low transition probabilities can be maintained in all ranges, even though the intrinsic time constants of the device are short (approximately 100 ms). Synaptic transitions are triggered by elevated presynaptic rates: for low presynaptic rates, there are essentially no transitions. The synaptic device can preserve its memory for years in the absence of stimulation. Stochasticity of learning is a result of the variability of interspike intervals; noise is a feature of the distributed dynamics of the network. The fact that the synapse is binary on long timescales solves the stability problem of synaptic efficacies in the absence of stimulation. Yet stochastic learning theory ensures that it does not affect the collective behavior of the network, if the transition probabilities are low and LTP is balanced against LTD.
authors.affiliation: INFN Sezione RM1, University of Rome La Sapienza, Roma, Italy.
authors.forename: S
authors.initials: S
authors.lastname: Fusi
authors.forename: M
authors.initials: M
authors.lastname: Annunziato
authors.forename: D
authors.initials: D
authors.lastname: Badoni
authors.forename: A
authors.initials: A
authors.lastname: Salamon
authors.forename: D J
authors.initials: DJ
authors.lastname: Amit
mesh_terms: D000200:Action Potentials / Q000502:physiology; D001185:Artificial Intelligence; D003198:Computer Simulation; D003201:Computers; D004553:Electric Conductivity; D017774:Long-Term Potentiation / Q000502:physiology; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D011336:Probability; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology