Analysis of an Attractor Neural Network's Response to Conflicting External Inputs.

Journal of Mathematical Neuroscience
Kathryn R Hedrick, Kechen Zhang

Abstract

The theory of attractor neural networks has been influential in our understanding of the neural processes underlying spatial, declarative, and episodic memory. Many theoretical studies focus on the inherent properties of an attractor, such as its structure and capacity. Relatively little is known about how an attractor neural network responds to external inputs, which often carry conflicting information about a stimulus. In this paper we analyze the behavior of an attractor neural network driven by two conflicting external inputs. Our focus is on analyzing the emergent properties of the megamap model, a quasi-continuous attractor network in which place cells are flexibly recombined to represent a large spatial environment. In this model, the system shows a sharp transition from the winner-take-all mode, which is characteristic of standard continuous attractor neural networks, to a combinatorial mode in which the equilibrium activity pattern combines embedded attractor states in response to conflicting external inputs. We derive a numerical test for determining the operational mode of the system a priori. We then derive a linear transformation from the full megamap model with thousands of neurons to a reduced 2-unit model that h...Continue Reading

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Citations

Jan 9, 2019·Journal of Mathematical Neuroscience·Zachary P KilpatrickRobert Rosenbaum
Sep 28, 2021·PLoS Computational Biology·Yijia YanAndrej Bicanski

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Software Mentioned

megamap

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