A stimulus-dependent spike threshold is an optimal neural coder
A neural code based on sequences of spikes can consume a significant portion of the brain’s energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-06-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00061/full |
| _version_ | 1818591970860204032 |
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| author | Douglas L Jones Douglas L Jones Douglas L Jones Douglas L Jones Erik Christopher Johnson Erik Christopher Johnson Erik Christopher Johnson Rama eRatnam Rama eRatnam Rama eRatnam |
| author_facet | Douglas L Jones Douglas L Jones Douglas L Jones Douglas L Jones Erik Christopher Johnson Erik Christopher Johnson Erik Christopher Johnson Rama eRatnam Rama eRatnam Rama eRatnam |
| author_sort | Douglas L Jones |
| collection | DOAJ |
| description | A neural code based on sequences of spikes can consume a significant portion of the brain’s energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. |
| first_indexed | 2024-12-16T10:20:54Z |
| format | Article |
| id | doaj.art-6dc5d5da581f40aaac6cba0fc864161f |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-16T10:20:54Z |
| publishDate | 2015-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-6dc5d5da581f40aaac6cba0fc864161f2022-12-21T22:35:19ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882015-06-01910.3389/fncom.2015.00061130141A stimulus-dependent spike threshold is an optimal neural coderDouglas L Jones0Douglas L Jones1Douglas L Jones2Douglas L Jones3Erik Christopher Johnson4Erik Christopher Johnson5Erik Christopher Johnson6Rama eRatnam7Rama eRatnam8Rama eRatnam9University of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignIllinois at Singapore Pte. Ltd.University of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignIllinois at Singapore Pte. Ltd.A neural code based on sequences of spikes can consume a significant portion of the brain’s energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00061/fullNeural codingadaptive thresholdspike-timingSource codingMoving thresholdEnergy-efficient coding |
| spellingShingle | Douglas L Jones Douglas L Jones Douglas L Jones Douglas L Jones Erik Christopher Johnson Erik Christopher Johnson Erik Christopher Johnson Rama eRatnam Rama eRatnam Rama eRatnam A stimulus-dependent spike threshold is an optimal neural coder Frontiers in Computational Neuroscience Neural coding adaptive threshold spike-timing Source coding Moving threshold Energy-efficient coding |
| title | A stimulus-dependent spike threshold is an optimal neural coder |
| title_full | A stimulus-dependent spike threshold is an optimal neural coder |
| title_fullStr | A stimulus-dependent spike threshold is an optimal neural coder |
| title_full_unstemmed | A stimulus-dependent spike threshold is an optimal neural coder |
| title_short | A stimulus-dependent spike threshold is an optimal neural coder |
| title_sort | stimulus dependent spike threshold is an optimal neural coder |
| topic | Neural coding adaptive threshold spike-timing Source coding Moving threshold Energy-efficient coding |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00061/full |
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