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Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex

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  • Yuichi Katori
  • Kazuhiro Sakamoto
  • Naohiro Saito
  • Jun Tanji
  • Hajime Mushiake
  • Kazuyuki Aihara

Abstract

The prefrontal cortex (PFC) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory. The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors, each attractor of which corresponds to an active state of a cell assembly, representing a fragment of information. Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context. This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear. Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity, or short-term plasticity. We construct a network model based on a spiking neuron model with dynamical synapses, which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task. The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies. Furthermore, we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies' configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system. This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC. Author Summary: The prefrontal cortex plays a highly flexible role in various cognitive tasks e.g., decision making and action planning. Neurons in the prefrontal cortex exhibit flexible representation or selectivity for task relevant information and are involved in working memory with sustained activity, which can be modeled as attractor dynamics. Moreover, recent experiments revealed that prefrontal neurons not only represent parametric or discrete sets of information but also switch the representation and transform a set of information to another set in order to match the context of the required task. However, underlying mechanisms of this flexible representational switching are unknown. Here we propose a dynamically reorganizable attractor network model in which short-term modulation of the synaptic connections reconfigures the structure of neural attractors by assembly and disassembly of a network of cells to produce flexible attractor dynamics. On the basis of computer simulation as well as theoretical analysis, we showed that this model reproduced experimentally demonstrated representational switching, and that switching on certain characteristic axes defining neural dynamics well describes the essence of the representational switching. This model has the potential to provide unique insights about the flexible information representations and processing in the cortical network.

Suggested Citation

  • Yuichi Katori & Kazuhiro Sakamoto & Naohiro Saito & Jun Tanji & Hajime Mushiake & Kazuyuki Aihara, 2011. "Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-17, November.
  • Handle: RePEc:plo:pcbi00:1002266
    DOI: 10.1371/journal.pcbi.1002266
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    References listed on IDEAS

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    1. Kazuhiro Sakamoto & Yuichi Katori & Naohiro Saito & Shun Yoshida & Kazuyuki Aihara & Hajime Mushiake, 2013. "Increased Firing Irregularity as an Emergent Property of Neural-State Transition in Monkey Prefrontal Cortex," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-13, December.

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