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NJIT Mathematical Biology Seminar

Tuesday, October 21, 2008, 4:00pm
Cullimore Hall 611
New Jersey Institute of Technology

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A recurrent spiking neuron model of multiple-choice decisions

Moran Furman

Yale University

The ability to make a goal-directed choice among multiple alternatives is an important aspect of adaptive behavior. We present here a line-attractor spiking neuron model for multiple-choice decision making in a motion discrimination task. The network is dominated by recurrent connections that generate competitive interactions and formation of a categorical choice. In difference from previous models of multiple-choice decision making, the model embodies a continuous representation of the sensory input (i.e. direction of motion). Thus, the model can operate independently of the number of choice alternatives, and also opens up the possibility to study varying degrees of similarity between them. The model accounts for a wide range of experimental data, including single neuron activity in the lateral intraparietal area (LIP), behavioral response times and psychometric functions, and provides experimentally testable predictions. Finally, we propose a novel mechanism for flexible control of speed and accuracy during decision making, based on a biologically plausible top-down projection to the network. Taken together, our findings provide further support for the role of attractor neural dynamics as a general mechanism for slow accumulation of sensory information, decision making and storage of a choice in working memory.