A low cost biomimetic implementation of a CPG based on AdEx neuron model

Gomar, Shaghayegh; Ahmadi, Arash; Alirezaee, Shahpour; Ahmadi, M.; Mirhasani, Mitra

doi:10.1109/icecs.2014.7050073

Cited by 7 publications

(24 citation statements)

References 9 publications

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“…These connections causes the coupling of two connected neurons. These coupled neurons are basic submodules of the CNS that can be realized in real states [ 7 , 8 , 9 , 10 ]. Furthermore, glias are the protection-based cells for neurons and regulate the coupling behaviors between different neuron connections.…”

Section: Introductionmentioning

confidence: 99%

“…The basic signal behaviors of the biological neurons are modeled by these mathematical equations. In neuron modeling, two approaches are selected: first, the models which are based on the biological neurons behavior, such as Hodgkin-Huxley (HH) [ 2 ] or the ADEX neuron model [ 5 , 6 , 7 , 8 ], and the second is based on spike state modeling, such as Izhikevich [ 3 , 4 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Using reconfigurable digital boards, such as FPGAs cause the speed-up and flexibility in this case [ 8 , 9 , 10 , 11 , 12 ]. In the field of neural networks implementation, digital approach may be a suitable procedure [ 1 , 3 , 4 , 5 , 6 , 7 , 8 ]. Indeed, because of high-speed switching in the neuronal networks, it is required to have a accelerate hardware, which is capable of regenerating the same behavior and performance of the human neuronal cells.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, it is best suited for computing and digital electronics, and is less affected since noise response are analog in nature, digital hardware is flexible in implementation and digital instruments are free from observational errors such as parallax and approximation errors (compared to the analog approach). The implementation of the neuronal cells using configurable platforms such as FPGA is an attractive area in terms of low overhead costs and high-frequency digital systems [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. FPGA is an integrated circuit designed to be configured by a customer or a designer after manufacturing, hence the term field-programmable.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Soleimani et al [ 12 ] proposed a digital effective realization of Izhikevich neuron model in low-cost and high-frequency state. In other words, the ADEX neuron model is implemented in the literature [ 5 , 6 , 7 , 8 ]. In detail, Gomar et al [ 5 ] proposed power-2-based implementation of the ADEX model without any multiplications.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms

Wang

Taylan

Alkabaa

et al. 2022

Biology

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Design and implementation of biological neural networks is a vital research field in the neuromorphic engineering. This paper presents LUT-based modeling of the Adaptive Exponential integrate-and-fire (ADEX) model using Nyquist frequency method. In this approach, a continuous term is converted to a discrete term by sampling factor. This new modeling is called N-LUT-ADEX (Nyquist-Look Up Table-ADEX) and is based on accurate sampling of the original ADEX model. Since in this modeling, the high-accuracy matching is achieved, it can exactly reproduce the spiking patterns, which have the same behaviors of the original neuron model. To confirm the N-LUT-ADEX neuron, the proposed model is realized on Virtex-II Field-Programmable Gate Array (FPGA) board for validating the final hardware. Hardware implementation results show the high degree of similarity between the proposed and original models. Furthermore, low-cost and high-speed attributes of our proposed neuron model will be validated. Indeed, the proposed model is capable of reproducing the spiking patterns in terms of low overhead costs and higher frequencies in comparison with the original one. The properties of the proposed model cause can make it a suitable choice for neuromorphic network implementations with reduced-cost attributes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms

Wang

Taylan

Alkabaa

et al. 2022

Biology

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Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization

Haghiri

Yahya

Rezaei

et al. 2023

Circuit Theory & Apps

Self Cite

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Implementation of neural networks in case of hardware helps us to understand the different parts of the human brain operation, using artificial intelligence (AI). This paper presents a new model of the Hindmarsh-Rose (HR) Neuron that is based on basic polynomial functions called Nyquist-look up table-Hindmarsh-Rose (N-LUT-HR) based on an accurate sampling of the original model. The proposed approach is investigated in terms of its digital realization feasibility. According to high matching between the original and proposed terms, it is showed that the new modified model can follow all spiking patterns of primary model with low-error computations. In hardware case, the proposed and original models are implemented on Xilinx FPGA XC2VP30 chip to validate different aspects of the simulation results. Hardware results demonstrate that our model regenerates the desired patterns in low-cost and high-frequency (speed-up) in comparison with the other similar works. Overall saving in FPGA resources show that this new model is capable of being used in large-scale networks in case of minimum required resources (FPGA costs). In addition, the analysis of hardware indicates that the new circuits can work in a maximum frequency of 123 MHz with 98.25% saving in FPGA costs (resources utilization of FPGA).

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A novel memristor based integrate-and-fire neuron implementation using material implication logic

Teimoori

Ahmadi

Alirezaee

et al. 2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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A low cost biomimetic implementation of a CPG based on AdEx neuron model

Cited by 7 publications

References 9 publications

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms

Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization

A novel memristor based integrate-and-fire neuron implementation using material implication logic

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