Real-Time Decoding of an Integrate and Fire Encoder
Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal. Decoding the former becomes intractable, while the latter does not adequately capture the nonlinearities...
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| Format: | Article |
| Language: | en_US |
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Neural Information Processing Systems Foundation
2015
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| Online Access: | http://hdl.handle.net/1721.1/99951 https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0001-5617-1202 |
| _version_ | 1826201738016718848 |
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| author | Saxena, Shreya Dahleh, Munther A. |
| author2 | MIT Institute for Data, Systems, and Society |
| author_facet | MIT Institute for Data, Systems, and Society Saxena, Shreya Dahleh, Munther A. |
| author_sort | Saxena, Shreya |
| collection | MIT |
| description | Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal. Decoding the former becomes intractable, while the latter does not adequately capture the nonlinearities present in the neuronal encoding system. For use in practical applications, we wish to record the output of neurons, namely spikes, and decode this signal fast in order to act on this signal, for example to drive a prosthetic device. Here, we introduce a causal, real-time decoder of the biophysically-based Integrate and Fire encoding neuron model. We show that the upper bound of the real-time reconstruction error decreases polynomially in time, and that the L[subscript 2] norm of the error is bounded by a constant that depends on the density of the spikes, as well as the bandwidth and the decay of the input signal. We numerically validate the effect of these parameters on the reconstruction error. |
| first_indexed | 2024-09-23T11:56:18Z |
| format | Article |
| id | mit-1721.1/99951 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:56:18Z |
| publishDate | 2015 |
| publisher | Neural Information Processing Systems Foundation |
| record_format | dspace |
| spelling | mit-1721.1/999512022-09-27T22:56:56Z Real-Time Decoding of an Integrate and Fire Encoder Saxena, Shreya Dahleh, Munther A. MIT Institute for Data, Systems, and Society Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Saxena, Shreya Dahleh, Munther A. Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal. Decoding the former becomes intractable, while the latter does not adequately capture the nonlinearities present in the neuronal encoding system. For use in practical applications, we wish to record the output of neurons, namely spikes, and decode this signal fast in order to act on this signal, for example to drive a prosthetic device. Here, we introduce a causal, real-time decoder of the biophysically-based Integrate and Fire encoding neuron model. We show that the upper bound of the real-time reconstruction error decreases polynomially in time, and that the L[subscript 2] norm of the error is bounded by a constant that depends on the density of the spikes, as well as the bandwidth and the decay of the input signal. We numerically validate the effect of these parameters on the reconstruction error. National Science Foundation (U.S.) (Emerging Frontiers in Research and Innovation Grant 1137237) 2015-11-20T15:34:15Z 2015-11-20T15:34:15Z 2014-12 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/99951 Saxena, Shreya, and Munther Dahleh. "Real-Time Decoding of an Integrate and Fire Encoder." Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS) (December 2014). https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0001-5617-1202 en_US https://nips.cc/Conferences/2014 Proceedings of the Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Neural Information Processing Systems Foundation MIT web domain |
| spellingShingle | Saxena, Shreya Dahleh, Munther A. Real-Time Decoding of an Integrate and Fire Encoder |
| title | Real-Time Decoding of an Integrate and Fire Encoder |
| title_full | Real-Time Decoding of an Integrate and Fire Encoder |
| title_fullStr | Real-Time Decoding of an Integrate and Fire Encoder |
| title_full_unstemmed | Real-Time Decoding of an Integrate and Fire Encoder |
| title_short | Real-Time Decoding of an Integrate and Fire Encoder |
| title_sort | real time decoding of an integrate and fire encoder |
| url | http://hdl.handle.net/1721.1/99951 https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0001-5617-1202 |
| work_keys_str_mv | AT saxenashreya realtimedecodingofanintegrateandfireencoder AT dahlehmunthera realtimedecodingofanintegrateandfireencoder |