Fitting of dynamic recurrent neural network models to sensory stimulus-response data

Fitting of dynamic recurrent neural network models to sensory stimulus-response data JBiolPhys https://doi.org/10.1007/s10867-018-9501-z ORIGINAL PAPER Fitting of dynamic recurrent neural network models to sensory stimulus-response data 1 2 R. Ozgur Doruk · Kechen Zhang Received: 9 June 2017 / Accepted: 7 May 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract We present a theoretical study aiming at model fitting for sensory neurons. Con- ventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) that have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using the maxi- mum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynami- cal neuron network models allows us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data are generated by a phased cosine Fourier series having a fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Biological Physics Springer Journals

Fitting of dynamic recurrent neural network models to sensory stimulus-response data

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Publisher
Springer Netherlands
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Physics; Biological and Medical Physics, Biophysics; Biochemistry, general; Complex Systems; Neurosciences; Soft and Granular Matter, Complex Fluids and Microfluidics
ISSN
0092-0606
eISSN
1573-0689
D.O.I.
10.1007/s10867-018-9501-z
Publisher site
See Article on Publisher Site

Abstract

JBiolPhys https://doi.org/10.1007/s10867-018-9501-z ORIGINAL PAPER Fitting of dynamic recurrent neural network models to sensory stimulus-response data 1 2 R. Ozgur Doruk · Kechen Zhang Received: 9 June 2017 / Accepted: 7 May 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract We present a theoretical study aiming at model fitting for sensory neurons. Con- ventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) that have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using the maxi- mum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynami- cal neuron network models allows us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data are generated by a phased cosine Fourier series having a fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus

Journal

Journal of Biological PhysicsSpringer Journals

Published: Jun 2, 2018

References

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