A Design for Real-time Neural Modeling on the GPU Incorporating Dendritic Computation

Date

2009-07-05

Author

Garaas, Tyler W.
Marino, Frank
Duzcu, Halil
Pomplun, Marc

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Recent advances in neuroscience have underscored the role of single neurons in information processing. Much of this work has focused on the role of neurons' dendrites to perform complex local computations that form the basis for the global computation of the neuron. Generally, artificial neural networks that are capable of real-time simulation do not take into account the principles underlying single-neuron processing. In this paper we propose a design for a neural model executed on the graphics processing unit (GPU) that is capable of simulating large neural networks that utilize dendritic computation inspired by biological neurons. We subsequently test our design using a neural model of the retinal neurons that contribute to the activation of starburst amacrine cells, which, as in biological retinas, use dendritic computational abilities to produce a neural signal that is directionally selective to stimuli moving centrifugally.

Subject Keywords

Selectivity, Retina, Cortex, Cells

URI

https://hdl.handle.net/11511/67726

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Department of Computer Education and Instructional Technology, Conference / Seminar