HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Amit, D. J. Articles by Mongillo, G. Search for Related Content PubMed PubMed Citation Articles by Amit, D. J. Articles by Mongillo, G.

Spike-Driven Synaptic Dynamics Generating Working Memory States

daniel.amit{at}roma1.infn.it, Dipartimento di Fisica, Universita’ di Roma, La Sapienza, 00185 Rome, Italy, and Racah Institute of Physics, Hebrew University, Jerusalem, Israel

Gianluigi Mongillo

mongillo{at}titanus.roma1.infn.it, Dipartimento di Fisica, Universita’ di Roma, La Sapienza, 00185 Rome, Italy, and Racah Institute of Physics, Hebrew University, Jerusalem, Israel

The collective behavior of a network, modeling a cortical module of spiking neurons connected by plastic synapses is studied. A detailed spike-driven synaptic dynamics is simulated in a large network of spiking neurons, implementing the full double dynamics of neurons and synapses. The repeated presentation of a set of external stimuli is shown to structure the network to the point of sustaining working memory (selective delay activity). When the synaptic dynamics is analyzed as a function of pre- and postsynaptic spike rates in functionally defined populations, it reveals a novel variation of the Hebbian plasticity paradigm: in any functional set of synapses between pairs of neurons (e.g., stimulated–stimulated, stimulated–delay, stimulated–spontaneous), there is a finite probability of potentiation as well as of depression. This leads to a saturation of potentiation or depression at the level of the ratio of the two probabilities. When one of the two probabilities is very high relative to the other, the familiar Hebbian mechanism is recovered. But where correlated working memory is formed, it prevents overlearning. Constraints relevant to the stability of the acquired synaptic structure and the regimes of global activity allowing for structuring are expressed in terms of the parameters describing the single-synapse dynamics. The synaptic dynamics is discussed in the light of experiments observing precise spike timing effects and related issues of biological plausibility.

This article has been cited by other articles:

				S. Romani, D. J. Amit, and Y. Amit Optimizing One-Shot Learning with Binary Synapses Neural Comput., August 1, 2008; 20(8): 1928 - 1950. [Abstract] [Full Text] [PDF]

				V. Yakovlev, D. J. Amit, S. Romani, and S. Hochstein Universal Memory Mechanism for Familiarity Recognition and Identification J. Neurosci., January 2, 2008; 28(1): 239 - 248. [Abstract] [Full Text] [PDF]

				J. M. Brader, W. Senn, and S. Fusi Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics Neural Comput., November 1, 2007; 19(11): 2881 - 2912. [Abstract] [Full Text] [PDF]

				C. Molter, U. Salihoglu, and H. Bersini The Road to Chaos by Time-Asymmetric Hebbian Learning in Recurrent Neural Networks Neural Comput., January 1, 2006; 19(1): 80 - 110. [Abstract] [Full Text] [PDF]

				W. Senn and S. Fusi Learning Only When Necessary: Better Memories of Correlated Patterns in Networks with Bounded Synapses Neural Comput., October 1, 2005; 17(10): 2106 - 2138. [Abstract] [Full Text] [PDF]

				E. Curti, G. Mongillo, G. La Camera, and D. J. Amit Mean Field and Capacity in Realistic Networks of Spiking Neurons Storing Sparsely Coded Random Memories Neural Comput., December 1, 2004; 16(12): 2597 - 2637. [Abstract] [Full Text] [PDF]

				A. N. Burkitt, H. Meffin, and D. B. Grayden Spike-Timing-Dependent Plasticity: The Relationship to Rate-Based Learning for Models with Weight Dynamics Determined by a Stable Fixed Point Neural Comput., May 1, 2004; 16(5): 885 - 940. [Abstract] [Full Text] [PDF]