Short-term potentiation effect on pattern recall in sparsely coded neural network

Stroffek, Julius; Maršálek, Petr

doi:10.1016/j.neucom.2011.08.021

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…k x q n denotes the activities of individual neurons within iteration q of the cycle in pattern k. The learning rule itself is the same as in the Willshaw model except that it is rewritten in a form that include neural activities within cycles. Further details can be found in [39,40]. Synaptic facilitation and depression can additionally enlarge the network capacity, as demonstrated in [41,42,43].…”

Section: Recurrent Networkmentioning

confidence: 95%

Biological context of Hebb learning in artificial neural networks, a review

et al. 2015

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Section: Recurrent Networkmentioning

confidence: 95%

Biological context of Hebb learning in artificial neural networks, a review

et al. 2015

Self Cite

View full text Add to dashboard Cite

Binding and Segmentation via a Neural Mass Model Trained With Hebbian and Anti-Hebbian Mechanisms

Cona

Zavaglia

Ursino

2012

Int. J. Neur. Syst.

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Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

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Short-term potentiation effect on pattern recall in sparsely coded neural network

Cited by 2 publications

References 23 publications

Biological context of Hebb learning in artificial neural networks, a review

Biological context of Hebb learning in artificial neural networks, a review

Binding and Segmentation via a Neural Mass Model Trained With Hebbian and Anti-Hebbian Mechanisms

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