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Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells.

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Title: Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells.
Authors: Voelker, Aaron R., Eliasmith, Chris
Source: Neural Computation. Mar2018, Vol. 30 Issue 3, p569-609. 41p. 1 Color Photograph, 3 Diagrams, 6 Graphs.
Subjects: Computational neuroscience, Artificial neural networks, Action potentials, Computational biology, Analog computer simulation, Digital computer simulation
Abstract: Researchers building spiking neural networks face the challenge of improving the biological plausibility of their model networks while maintaining the ability to quantitatively characterize network behavior. In this work, we extend the theory behind the neural engineering framework (NEF), a method of building spiking dynamical networks, to permit the use of a broad class of synapse models while maintaining prescribed dynamics up to a given order. This theory improves our understanding of how low-level synaptic properties alter the accuracy of high-level computations in spiking dynamical networks. For completeness, we provide characterizations for both continuous-time (i.e., analog) and discrete-time (i.e., digital) simulations. We demonstrate the utility of these extensions by mapping an optimal delay line onto various spiking dynamical networks using higher-order models of the synapse. We show that these networks nonlinearly encode rolling windows of input history, using a scale invariant representation, with accuracy depending on the frequency content of the input signal. Finally, we reveal that these methods provide a novel explanation of time cell responses during a delay task, which have been observed throughout hippocampus, striatum, and cortex. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells.
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Mar2018, Vol. 30 Issue 3, p569-609. 41p. 1 Color Photograph, 3 Diagrams, 6 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Action+potentials%22">Action potentials</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+biology%22">Computational biology</searchLink><br /><searchLink fieldCode="DE" term="%22Analog+computer+simulation%22">Analog computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+computer+simulation%22">Digital computer simulation</searchLink>
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  Data: Researchers building spiking neural networks face the challenge of improving the biological plausibility of their model networks while maintaining the ability to quantitatively characterize network behavior. In this work, we extend the theory behind the neural engineering framework (NEF), a method of building spiking dynamical networks, to permit the use of a broad class of synapse models while maintaining prescribed dynamics up to a given order. This theory improves our understanding of how low-level synaptic properties alter the accuracy of high-level computations in spiking dynamical networks. For completeness, we provide characterizations for both continuous-time (i.e., analog) and discrete-time (i.e., digital) simulations. We demonstrate the utility of these extensions by mapping an optimal delay line onto various spiking dynamical networks using higher-order models of the synapse. We show that these networks nonlinearly encode rolling windows of input history, using a scale invariant representation, with accuracy depending on the frequency content of the input signal. Finally, we reveal that these methods provide a novel explanation of time cell responses during a delay task, which have been observed throughout hippocampus, striatum, and cortex. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1162/neco_a_01046
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        Text: English
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      – SubjectFull: Computational neuroscience
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Action potentials
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      – SubjectFull: Analog computer simulation
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      – SubjectFull: Digital computer simulation
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              Text: Mar2018
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