Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation

Mejías, Jorge F.; Torres, Joaquı́n J.

doi:10.1162/neco.2008.02-08-719

Cited by 48 publications

(71 citation statements)

References 39 publications

(95 reference statements)

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“…The dynamics of short-term plasticity (also called dynamic synapses) in model neuronal networks are well reviewed (Barak and Tsodyks 2007;Marinazzo et al 2007;Mejias and Torres 2009) and, in contrast to many other mechanisms, mathematically straightforward to analyze Bressloff 1999;Mejias and Torres 2009). …”

Section: Short-term Plasticitymentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Short-term Plasticitymentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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“…Finally we also report on the effect of synaptic processes coupled with network activity on maximum storage capacity of the network [14] via a phenomenological model of activity-dependent synapses (see [15] for details) which involves a competition between facilitating and depressing synaptic mechanisms. This model can be studied using our general theoretical framework assuming…”

Section: Model and Resultsmentioning

confidence: 99%

Nonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance

Torres

Johnson

Mejías

et al. 2010

Advances in Cognitive Neurodynamics (II)

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Abstract. We present a general theory which allows one to study the effects on emergent, cooperative behavior of a complex interplay between different dynamic processes that occur in actual systems at the neuron, synapse and network levels. We consider synaptic changes at different time scales from less than the millisecond to the scale of learning, and the possibility of finding a fraction of silent neurons. For some limits of interest, the fixed-point solutions or memories then loose stability and the system shows enhancement of its response to changing external stimuli for particular network topologies and dynamical memories. We observe at the edge of chaos that the network activity becomes critical in the sense that the relevant quantities show non-trivial, power-law distributions. We also describe the effect of activity-dependent synaptic processes on the network storage capacity.

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“…where the dynamics of u j (t) has been normalized with respect to [11] for simplicity. Here, U SE is a parameter related with the fraction of neurotransmitters released after the presynaptic neurons fires, and τ rec , τ f ac are, respectively, the time constants for depressing and facilitating mechanisms.…”

Section: The Modelmentioning

confidence: 99%

“…Here, U SE is a parameter related with the fraction of neurotransmitters released after the presynaptic neurons fires, and τ rec , τ f ac are, respectively, the time constants for depressing and facilitating mechanisms. The static-synapses situation (that is, when synapses do not display STD nor STF) may be obtained if one sets x i (t) = u i (t) = 1, ∀ i, t or, alternatively, in the limit in which synaptic time constants τ rec , τ f ac become too small (for more details on this limit, see [11]). The neuron firing thresholds are given by…”

Section: The Modelmentioning

confidence: 99%

Short-term synaptic facilitation improves information retrieval in noisy neural networks

Mejías¹,

Hernandez-Gomez²,

Torres³

2012

EPL

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Short-term synaptic depression and facilitation have been found to greatly influence the performance of autoassociative neural networks. However, only partial results, focused for instance on the computation of the maximum storage capacity at zero temperature, have been obtained to date. In this work, we extended the study of the effect of these synaptic mechanisms on autoassociative neural networks to more realistic and general conditions, including the presence of noise in the system. In particular, we characterized the behavior of the system by means of its phase diagrams, and we concluded that synaptic facilitation significantly enlarges the region of good retrieval performance of the network. We also found that networks with facilitating synapses may have critical temperatures substantially higher than those of standard autoassociative networks, thus allowing neural networks to perform better under high-noise conditions.

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Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation

Cited by 48 publications

References 39 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Nonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance

Short-term synaptic facilitation improves information retrieval in noisy neural networks

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