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Evidence accumulation in dynamic environments

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Models of evidence accumulation are of interest in disciplines ranging from neuroscience to robotics to psychology. In a constantly changing world, agents must account for environmental volatility and appropriately discount old information when making decisions based on such accumulated evidence.

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We introduce Bayesian inference models of decision making, and derive an ideal observer model for inferring the present state of the environment along with its rate of change. Moment closure then allows us to obtain a low-dimensional system that performs comparable inference. These computations can be implemented by a neural network model whose connections are updated according to an activity-dependent plasticity rule. Our work therefore builds a bridge between statistical decision making in volatile environments and stochastic nonlinear dynamics of neural systems.

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This work is in collaboration with Kresimir Josic, Adrian Radillo, and Alan Veliz-Cuba.