Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times
In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-10-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00042/full |
| _version_ | 1828425916492021760 |
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| author | Satoshi eYamauchi Hideaki eKim Shigeru eShinomoto |
| author_facet | Satoshi eYamauchi Hideaki eKim Shigeru eShinomoto |
| author_sort | Satoshi eYamauchi |
| collection | DOAJ |
| description | In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of firing responses to transient current inputs. Simplistic models based on leaky integrate-and-fire mechanisms have demonstrated the ability to adapt to biological neurons. In particular, the multi-timescale adaptive threshold (MAT) model reproduces and predicts precise spike times of regular-spiking, intrinsic-bursting, and fast-spiking neurons, under any fluctuating current; however, this model is incapable of reproducing such specific firing responses as inhibitory rebound spiking and resonate spiking. In this paper, we augment the MAT model by adding a voltage dependency term to the adaptive threshold so that the model can exhibit the full variety of firing responses to various transient current pulses while maintaining the high adaptability inherent in the original MAT model. Furthermore, with this addition, our model is actually able to better predict spike times. Despite the augmentation, the model has only four free parameters and is implementable in an efficient algorithm for large-scale simulation due to its linearity, serving as an element neuron model in the simulation of realistic neuronal circuitry. |
| first_indexed | 2024-12-10T16:39:05Z |
| format | Article |
| id | doaj.art-8c4779b75c2d4386a946e2b4ede0f902 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-10T16:39:05Z |
| publishDate | 2011-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-8c4779b75c2d4386a946e2b4ede0f9022022-12-22T01:41:17ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882011-10-01510.3389/fncom.2011.0004211566Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing timesSatoshi eYamauchi0Hideaki eKim1Shigeru eShinomoto2Kyoto UniversityKyoto UniversityKyoto UniversityIn simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of firing responses to transient current inputs. Simplistic models based on leaky integrate-and-fire mechanisms have demonstrated the ability to adapt to biological neurons. In particular, the multi-timescale adaptive threshold (MAT) model reproduces and predicts precise spike times of regular-spiking, intrinsic-bursting, and fast-spiking neurons, under any fluctuating current; however, this model is incapable of reproducing such specific firing responses as inhibitory rebound spiking and resonate spiking. In this paper, we augment the MAT model by adding a voltage dependency term to the adaptive threshold so that the model can exhibit the full variety of firing responses to various transient current pulses while maintaining the high adaptability inherent in the original MAT model. Furthermore, with this addition, our model is actually able to better predict spike times. Despite the augmentation, the model has only four free parameters and is implementable in an efficient algorithm for large-scale simulation due to its linearity, serving as an element neuron model in the simulation of realistic neuronal circuitry.http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00042/fulladaptive thresholdleaky integrate-and-fire modelMAT modelpredicting spike timesreproducing firing patternsspiking neuron model |
| spellingShingle | Satoshi eYamauchi Hideaki eKim Shigeru eShinomoto Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times Frontiers in Computational Neuroscience adaptive threshold leaky integrate-and-fire model MAT model predicting spike times reproducing firing patterns spiking neuron model |
| title | Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
| title_full | Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
| title_fullStr | Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
| title_full_unstemmed | Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
| title_short | Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
| title_sort | elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
| topic | adaptive threshold leaky integrate-and-fire model MAT model predicting spike times reproducing firing patterns spiking neuron model |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00042/full |
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