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An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

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Persistent storage capability impairs decision making in a biophysical network model.

Dominic Standage1, Martin Paré

  • 1Canadian Institutes of Health Research Group in Sensory-Motor Integration, Queen's University, 18 Stuart Street, Kingston, Ontario, Canada. standage@biomed.queensu.ca

Neural Networks : the Official Journal of the International Neural Network Society
|June 11, 2011
PubMed
Summary
This summary is machine-generated.

Neuroscience research suggests common mechanisms for decision-making and memory storage. However, our model shows these processes may conflict at the local circuit level under realistic parameters.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Decision-making and short-term memory storage are fundamental cognitive functions.
  • NMDA receptors at intrinsic cortical synapses are implicated in both decision-making and memory.
  • A long NMDA time constant is hypothesized to support persistent neural activity for memory and slow integration for decisions.

Purpose of the Study:

  • To test the hypothesis that local circuit mechanisms for decision-making also support persistent information storage.
  • To investigate the compatibility of processing requirements for persistent storage and decision-making within a local cortical circuit model.

Main Methods:

  • Utilized a computational model of spiking neurons in a local cortical circuit.
  • Manipulated NMDA receptor conductance strength to control intrinsic neuronal drive.
  • Simulated network dynamics to assess persistent activity and decision-making performance.

Main Results:

  • Simulation results support the idea that common mechanisms underlie persistent storage and decision-making.
  • Under biophysically realistic parameters, the model revealed potential incompatibilities between the processing demands of storage and decision-making.
  • Parameters favoring persistent storage exhibited strong dynamics, conflicting with slow integration needed for decisions.
  • Weaker network dynamics, while supporting decision-making speed-accuracy trade-offs, were less conducive to persistent storage.

Conclusions:

  • Persistent information storage and decision-making likely share underlying neural mechanisms.
  • Biophysical constraints at the local circuit level may create a trade-off between optimal conditions for memory persistence and decision processing.
  • The dynamics required for robust memory maintenance might interfere with the integration processes essential for accurate decision-making.