Associative Memory with Dynamic Synapses

Pantic, Lovorka; Torres, Joaquı́n J.; Kappen, Hilbert J.; Gielen, Stan

doi:10.1162/089976602760805331

Cited by 98 publications

(124 citation statements)

References 29 publications

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“…Here, on the other hand, the adverse effect of STD on associative memory is shown for the more general and realistic case of stochastic (T > 0) neural networks, a result which corroborates those of recent studies [22]. We find that the reduction of the memory area is due to the temporary weakening of synaptic connections due to STD, which decreases the stability of the fixed points of the dynamics [9]. The effects of STF in the retrieval properties of stochastic neural networks may be investigated as well.…”

Section: Resultssupporting

confidence: 91%

“…Quite often, to obtain such information one has to assume restricted conditions. For instance, to investigate the role of STD and STF on network dynamics, many studies have focused on networks with a finite number of stored patterns P (so that the network load, defined as α ≡ P/N with N being the number of neurons in the network, tends to zero in the thermodynamic limit N → ∞) [9,16]. Other studies consider, for instance, systems where the temperature parameter T remains low (or even zero, which corresponds to deterministic dynamics) in order to evaluate maximal retrieval abilities of neural networks [8,10,11].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

Mejías¹,

Hernandez-Gomez²,

Torres³

2012

EPL

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“…Kappen for very useful comments, and financial support from FEDER-MEC project FIS2005-00791, JA project P06-FQM-01505, and EPSRC-COLAMN project EP/CO 10841/1. (Tsodyks et al, 1998;Pantic et al, 2002), under partial updating in the oscillatory regime. Panels show, from top to bottom, the cases ρ = 1, 0.7, 0.3, 0.1.…”

Section: Discussionmentioning

confidence: 99%

Instabilities in attractor networks with fast synaptic fluctuations and partial updating of the neurons activity

et al. 2008

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We present and study a probabilistic neural automaton in which the fraction of simultaneously-updated neurons is a parameter, ρ ∈ (0, 1) . For small ρ, there is relaxation towards one of the attractors and a great sensibility to external stimuli and, for ρ ≥ ρ c , itinerancy among attractors. Tuning ρ in this regime, oscillations may abruptly change from regular to chaotic and vice versa, which allows one to control the efficiency of the searching process. We argue on the similarity of the model behavior with recent observations and on the possible role of chaos in neurobiology.

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“…An interesting particular situation is to assume activitydependent synaptic noise consistent with short-term synaptic depression and/or facilitation [6,9]. That is, let us assume that P st (Z|S) = j P (z j |S) with…”

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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Associative Memory with Dynamic Synapses

Cited by 98 publications

References 29 publications

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

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

Instabilities in attractor networks with fast synaptic fluctuations and partial updating of the neurons activity

Nonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance

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