FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model
A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-11-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00379/full |
| _version_ | 1818509762898165760 |
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| author | Safa eYaghini Bonabi Hassan eAsgharian Saeed eSafari Majid eNili Ahmadabadi Majid eNili Ahmadabadi |
| author_facet | Safa eYaghini Bonabi Hassan eAsgharian Saeed eSafari Majid eNili Ahmadabadi Majid eNili Ahmadabadi |
| author_sort | Safa eYaghini Bonabi |
| collection | DOAJ |
| description | A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. |
| first_indexed | 2024-12-10T22:49:51Z |
| format | Article |
| id | doaj.art-a0830ba493de446dae89f2e2d6444662 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T22:49:51Z |
| publishDate | 2014-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a0830ba493de446dae89f2e2d64446622022-12-22T01:30:27ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-11-01810.3389/fnins.2014.00379110373FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron ModelSafa eYaghini Bonabi0Hassan eAsgharian1Saeed eSafari2Majid eNili Ahmadabadi3Majid eNili Ahmadabadi4University of TehranIUSTUniversity of TehranUniversity of TehranIPMA set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00379/fullNeural NetworkFPGAHodgkin-Huxleyneural pooldigital hardware implementation |
| spellingShingle | Safa eYaghini Bonabi Hassan eAsgharian Saeed eSafari Majid eNili Ahmadabadi Majid eNili Ahmadabadi FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model Frontiers in Neuroscience Neural Network FPGA Hodgkin-Huxley neural pool digital hardware implementation |
| title | FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model |
| title_full | FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model |
| title_fullStr | FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model |
| title_full_unstemmed | FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model |
| title_short | FPGA Implementation of a Biological Neural Network Based on the Hodgkin-Huxley Neuron Model |
| title_sort | fpga implementation of a biological neural network based on the hodgkin huxley neuron model |
| topic | Neural Network FPGA Hodgkin-Huxley neural pool digital hardware implementation |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00379/full |
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