Limits...
Persistent activity in neural networks with dynamic synapses.

Barak O, Tsodyks M - PLoS Comput. Biol. (2007)

Bottom Line: One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections.Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks.This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, The Weizmann Institute of Science, Rehovot, Israel.

ABSTRACT
Persistent activity states (attractors), observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

Show MeSH

Related in: MedlinePlus

Summary of Analysis for a Single SubpopulationFive regions in parameter space with qualitatively different network behavior are illustrated. The traces shown in blue were obtained with the following parameter sets (clockwise, beginning from Transient Response): A with J = 0.8Jlow, A, A with J = 1.1J*, A with J = 1.1Jhigh, D (see Methods, Table 2).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC1808024&req=5

pcbi-0030035-g006: Summary of Analysis for a Single SubpopulationFive regions in parameter space with qualitatively different network behavior are illustrated. The traces shown in blue were obtained with the following parameter sets (clockwise, beginning from Transient Response): A with J = 0.8Jlow, A, A with J = 1.1J*, A with J = 1.1Jhigh, D (see Methods, Table 2).

Mentions: Critical Values of Parameters Separating Regimes of Different Qualitative Behavior (See Text and Figure 6)


Persistent activity in neural networks with dynamic synapses.

Barak O, Tsodyks M - PLoS Comput. Biol. (2007)

Summary of Analysis for a Single SubpopulationFive regions in parameter space with qualitatively different network behavior are illustrated. The traces shown in blue were obtained with the following parameter sets (clockwise, beginning from Transient Response): A with J = 0.8Jlow, A, A with J = 1.1J*, A with J = 1.1Jhigh, D (see Methods, Table 2).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1808024&req=5

pcbi-0030035-g006: Summary of Analysis for a Single SubpopulationFive regions in parameter space with qualitatively different network behavior are illustrated. The traces shown in blue were obtained with the following parameter sets (clockwise, beginning from Transient Response): A with J = 0.8Jlow, A, A with J = 1.1J*, A with J = 1.1Jhigh, D (see Methods, Table 2).
Mentions: Critical Values of Parameters Separating Regimes of Different Qualitative Behavior (See Text and Figure 6)

Bottom Line: One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections.Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks.This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, The Weizmann Institute of Science, Rehovot, Israel.

ABSTRACT
Persistent activity states (attractors), observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

Show MeSH
Related in: MedlinePlus