Simple cortical and thalamic neuron models for digital arithmetic circuit implementation
Trade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN) model is...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-05-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00181/full |
| _version_ | 1818570889527033856 |
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| author | Takuya eNanami Takashi eKohno |
| author_facet | Takuya eNanami Takashi eKohno |
| author_sort | Takuya eNanami |
| collection | DOAJ |
| description | Trade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmeticcircuits. We expanded the DSSN model and found appropriate parameter sets with which itreproduces the dynamical behaviors of the ionic-conductance models of four classes of corticaland thalamic neurons. We first developed a 4-variable model by reducing the number of variablesin the ionic-conductance models and elucidated its mathematical structures using bifurcationanalysis. Then, expanded DSSN models were constructed that reproduce these mathematicalstructures and capture the characteristic behavior of each neuron class. We confirmed thatstatistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductancemodels. Computational cost of the DSSN model is larger than that of the recent sophisticatedIntegrate-and-Fire-based models, but smaller than the ionic-conductance models. This modelis intended to provide another meeting point for above trade-off that satisfies the demand forlarge-scale neuronal network simulation with closer-to-biology models. |
| first_indexed | 2024-12-14T13:47:57Z |
| format | Article |
| id | doaj.art-44f5a348477541f69130a437014dea67 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-14T13:47:57Z |
| publishDate | 2016-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-44f5a348477541f69130a437014dea672022-12-21T22:59:15ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2016-05-011010.3389/fnins.2016.00181186196Simple cortical and thalamic neuron models for digital arithmetic circuit implementationTakuya eNanami0Takashi eKohno1The University of TokyoThe University of TokyoTrade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmeticcircuits. We expanded the DSSN model and found appropriate parameter sets with which itreproduces the dynamical behaviors of the ionic-conductance models of four classes of corticaland thalamic neurons. We first developed a 4-variable model by reducing the number of variablesin the ionic-conductance models and elucidated its mathematical structures using bifurcationanalysis. Then, expanded DSSN models were constructed that reproduce these mathematicalstructures and capture the characteristic behavior of each neuron class. We confirmed thatstatistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductancemodels. Computational cost of the DSSN model is larger than that of the recent sophisticatedIntegrate-and-Fire-based models, but smaller than the ionic-conductance models. This modelis intended to provide another meeting point for above trade-off that satisfies the demand forlarge-scale neuronal network simulation with closer-to-biology models.http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00181/fullCerebral CortexNonlinear DynamicsThalamusdigital silicon neuronal networkQualitative neuron model |
| spellingShingle | Takuya eNanami Takashi eKohno Simple cortical and thalamic neuron models for digital arithmetic circuit implementation Frontiers in Neuroscience Cerebral Cortex Nonlinear Dynamics Thalamus digital silicon neuronal network Qualitative neuron model |
| title | Simple cortical and thalamic neuron models for digital arithmetic circuit implementation |
| title_full | Simple cortical and thalamic neuron models for digital arithmetic circuit implementation |
| title_fullStr | Simple cortical and thalamic neuron models for digital arithmetic circuit implementation |
| title_full_unstemmed | Simple cortical and thalamic neuron models for digital arithmetic circuit implementation |
| title_short | Simple cortical and thalamic neuron models for digital arithmetic circuit implementation |
| title_sort | simple cortical and thalamic neuron models for digital arithmetic circuit implementation |
| topic | Cerebral Cortex Nonlinear Dynamics Thalamus digital silicon neuronal network Qualitative neuron model |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00181/full |
| work_keys_str_mv | AT takuyaenanami simplecorticalandthalamicneuronmodelsfordigitalarithmeticcircuitimplementation AT takashiekohno simplecorticalandthalamicneuronmodelsfordigitalarithmeticcircuitimplementation |