FPGA Implementation of a Cortical Network Based on the Hodgkin-Huxley Neuron Model

Bonabi, Safa Yaghini; Asgharian, Hassan; Bakhtiari, Reyhaneh; Safari, Saeed; Ahmadabadi, Majid Nili

doi:10.1007/978-3-642-34475-6_30

Cited by 7 publications

(10 citation statements)

References 10 publications

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“…The method used to implement this module is the same as our previous work (Bonabi et al, 2012a). Equation (11) is used to implement the integrator.…”

Section: Methodsmentioning

confidence: 99%

“…There are different biological neuron models; however, we opt for Hodgkin-Huxley (H-H) neural model because of its biological plausibility and inclusion of synaptic details. Due to the FPGA area limitation, we use the reduced order of the previous implementation (Bonabi et al, 2012a ). The implemented neural network in this work is a modified model used in Moldakarimov et al ( 2005 ).…”

Section: Introductionmentioning

confidence: 99%

“…Using the hardware as an accelerator for reducing the computation time of such neural networks is our main objective. The basis of single pool implementation are stated in Bonabi et al ( 2012b ). We use MATLAB simulations for high-level design of neural network and the results of simulations are used as a golden model to check correctness of our implementation.…”

Section: Introductionmentioning

confidence: 99%

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FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

et al. 2014

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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.

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“…The method used to implement this module is the same as our previous work (Bonabi et al, 2012a). Equation (11) is used to implement the integrator.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

et al. 2014

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“…Very large scale integration (VLSI) design can be more realistic for hardware implementations of spiking neuronal networks due to its capability to implement nonlinear models in a straightforward way (Ranjbar and Amiri, 2015 ; Yang et al, 2016 ), however the long development time and high costs of this method limit its usage (Nazari et al, 2015a , b ). On the one hand, digital execution with field-programmable gate array, (FPGA) can be faster and thus FPGAs have increasing applications in the neural computing area, in recent years (Bonabi et al, 2012 ; Sabarad et al, 2012 ; Nanami and Kohno, 2016 ). Currently, with the advancement in HDL synthesis tools (high-level hardware description language), configurable devices (such as FPGA) can be operated as effective hardware accelerators for neuromorphic systems.…”

Section: Introductionmentioning

confidence: 99%

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor

et al. 2018

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Inspired by the biology of human tactile perception, a hardware neuromorphic approach is proposed for spiking model of mechanoreceptors to encode the input force. In this way, a digital circuit is designed for a slowly adapting type I (SA-I) and fast adapting type I (FA-I) mechanoreceptors to be implemented on a low-cost digital hardware, such as field-programmable gate array (FPGA). This system computationally replicates the neural firing responses of both afferents. Then, comparative simulations are shown. The spiking models of mechanoreceptors are first simulated in MATLAB and next the digital neuromorphic circuits simulated in VIVADO are also compared to show that obtained results are in good agreement both quantitatively and qualitatively. Finally, we test the performance of the proposed digital mechanoreceptors in hardware using a prepared experimental set up. Hardware synthesis and physical realization on FPGA indicate that the digital mechanoreceptors are able to replicate essential characteristics of different firing patterns including bursting and spiking responses of the SA-I and FA-I mechanoreceptors. In addition to parallel computation, a main advantage of this method is that the mechanoreceptor digital circuits can be implemented in real-time through low-power neuromorphic hardware. This novel engineering framework is generally suitable for use in robotic and hand-prosthetic applications, so progressing the state of the art for tactile sensing.

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“…Few FPGA implementation are used for hybrid experiments [15][16][17] but they used threshold neuron model. The implementation in FPGA of HH model are rare [18], [19]. These works are used for neural network simulation (accelerated-time) and they are not optimized in terms of surface and number of neurons.…”

Section: Introductionmentioning

confidence: 99%

Digital implementation of Hodgkin–Huxley neuron model for neurological diseases studies

Levi

Khoyratee

Saïghi

et al. 2017

Artif Life Robotics

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Neurological disorders affect millions of people which influence their cognitive and/or motor capabilities. The realization of a prosthesis must consider the biological activity of the cells and the connection between machine and biological cells. Biomimetic neural network is one solution in front of neurological diseases. The neuron replacement should be processed by reproducing the timing and the shape of the spike. Several mathematical equations which model neural activities exist. The most biologically plausible one is the Hodgkin-Huxley (HH) model. The connection between electrical devices and living cells require a tunable real-time system. The FPGA (Field Programmable Gate Array) is a nice component including flexibility, speed and stability. Here we propose an implementation of HH neurons in FPGA serving as a presage for a modulating network opening a large scale of possibilities like damage cells replacement and the study of the effect of the cells disease on the neural network.

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FPGA Implementation of a Cortical Network Based on the Hodgkin-Huxley Neuron Model

Cited by 7 publications

References 10 publications

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor

Digital implementation of Hodgkin–Huxley neuron model for neurological diseases studies

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