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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ghaderi, V.S. Roach, S. Dong Song Marmarelis, V.Z. Choma, J. Berger, T.W. |
| Copyright Year | 2012 |
| Description | Author affiliation: Dept. Neurosci., Univ. of Southern California, Los Angeles, CA, USA (Roach, S.) || Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA (Ghaderi, V.S.; Choma, J.) || Dept. of Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA (Dong Song; Marmarelis, V.Z.; Berger, T.W.) |
| Abstract | The right level of abstraction for a model mimicking a neural function is often difficult to determine. There are trade-offs between capturing biological complexities on one hand and the scalability and efficiency of the model on the other. In this work, we describe a nonlinear Laguerre-Volterra model of the synaptic temporal integration of input spikes to postsynaptic potentials. This model is then efficiently implemented using analog subthreshold circuits and can serve as a foundation for future large-scale hardware systems that can emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error in estimating real data using the circuit implementation of this model is less than 15%. The model components are modular and its parameters are adjustable for modeling temporal integration by neurons in other brain regions. The total power consumption of this nonlinear Laguerre-Volterra system is less than 5nW. |
| Sponsorship | IEEE Eng. Medicine Biol. Soc. |
| Starting Page | 767 |
| Ending Page | 770 |
| File Size | 705056 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424441198 |
| ISSN | 1557170X |
| e-ISBN | 9781457717871 |
| DOI | 10.1109/EMBC.2012.6346044 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hardware Low pass filters Neurons Brain modeling Integrated circuit modeling Estimation Biological system modeling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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