Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control
In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain machine interface (BMI) controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of c...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2015-08-01
|
| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnbot.2015.00008/full |
| _version_ | 1818264955372175360 |
|---|---|
| author | Mehmet eKocaturk Mehmet eKocaturk Halil Ozcan Gulcur Resit eCanbeyli |
| author_facet | Mehmet eKocaturk Mehmet eKocaturk Halil Ozcan Gulcur Resit eCanbeyli |
| author_sort | Mehmet eKocaturk |
| collection | DOAJ |
| description | In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain machine interface (BMI) controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations. |
| first_indexed | 2024-12-12T19:43:08Z |
| format | Article |
| id | doaj.art-a55eda7b2d79404f9f2d53ccedc51acf |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-12T19:43:08Z |
| publishDate | 2015-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-a55eda7b2d79404f9f2d53ccedc51acf2022-12-22T00:14:09ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182015-08-01910.3389/fnbot.2015.00008150994Towards building hybrid biological/in silico neural networks for motor neuroprosthetic controlMehmet eKocaturk0Mehmet eKocaturk1Halil Ozcan Gulcur2Resit eCanbeyli3Bogazici UniversityIstanbul Medipol UniversityBogazici UniversityBogazici UniversityIn this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain machine interface (BMI) controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.http://journal.frontiersin.org/Journal/10.3389/fnbot.2015.00008/fullMotor Cortexbrain-machine interfaceneuroprostheticsspiking neuron modelsSpike timing-dependent plasticity |
| spellingShingle | Mehmet eKocaturk Mehmet eKocaturk Halil Ozcan Gulcur Resit eCanbeyli Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control Frontiers in Neurorobotics Motor Cortex brain-machine interface neuroprosthetics spiking neuron models Spike timing-dependent plasticity |
| title | Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control |
| title_full | Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control |
| title_fullStr | Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control |
| title_full_unstemmed | Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control |
| title_short | Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control |
| title_sort | towards building hybrid biological in silico neural networks for motor neuroprosthetic control |
| topic | Motor Cortex brain-machine interface neuroprosthetics spiking neuron models Spike timing-dependent plasticity |
| url | http://journal.frontiersin.org/Journal/10.3389/fnbot.2015.00008/full |
| work_keys_str_mv | AT mehmetekocaturk towardsbuildinghybridbiologicalinsiliconeuralnetworksformotorneuroprostheticcontrol AT mehmetekocaturk towardsbuildinghybridbiologicalinsiliconeuralnetworksformotorneuroprostheticcontrol AT halilozcangulcur towardsbuildinghybridbiologicalinsiliconeuralnetworksformotorneuroprostheticcontrol AT resitecanbeyli towardsbuildinghybridbiologicalinsiliconeuralnetworksformotorneuroprostheticcontrol |