A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity
Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Rece...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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National Academy of Sciences
2012
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| Online Access: | http://hdl.handle.net/1721.1/71924 |
| _version_ | 1811093236987461632 |
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| author | Rachmuth, Guy Shouval, Harel Z. Bear, Mark Poon, Chi-Sang |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Rachmuth, Guy Shouval, Harel Z. Bear, Mark Poon, Chi-Sang |
| author_sort | Rachmuth, Guy |
| collection | MIT |
| description | Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems. |
| first_indexed | 2024-09-23T15:42:05Z |
| format | Article |
| id | mit-1721.1/71924 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:42:05Z |
| publishDate | 2012 |
| publisher | National Academy of Sciences |
| record_format | dspace |
| spelling | mit-1721.1/719242022-09-29T15:33:32Z A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity Rachmuth, Guy Shouval, Harel Z. Bear, Mark Poon, Chi-Sang Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Bear, Mark Rachmuth, Guy Bear, Mark Poon, Chi-Sang Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems. National Institutes of Health (U.S.) (Grant Number EB005460) National Institutes of Health (U.S.) (Grant Number RR028241) National Institutes of Health (U.S.) (Grant Number HL067966) 2012-07-31T20:15:47Z 2012-07-31T20:15:47Z 2011-11 2011-05 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/71924 Rachmuth, G. et al. “PNAS Plus: A Biophysically-based Neuromorphic Model of Spike Rate- and Timing-dependent Plasticity.” Proceedings of the National Academy of Sciences 108.49 (2011): E1266–E1274. en_US http://dx.doi.org/10.1073/pnas.1106161108 Proceedings of the National Academy of Sciences 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 National Academy of Sciences PNAS |
| spellingShingle | Rachmuth, Guy Shouval, Harel Z. Bear, Mark Poon, Chi-Sang A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity |
| title | A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity |
| title_full | A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity |
| title_fullStr | A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity |
| title_full_unstemmed | A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity |
| title_short | A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity |
| title_sort | biophysically based neuromorphic model of spike rate and timing dependent plasticity |
| url | http://hdl.handle.net/1721.1/71924 |
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