Analog implementation of neuron–astrocyte interaction in tripartite synapse

Ranjbar, Mehdi; Amiri, Mahmood

doi:10.1007/s10825-015-0727-8

Cited by 17 publications

(7 citation statements)

References 49 publications

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“…(iv) The proposed astrocyte circuit was shown to have interestingproperties such as frequency adaptation [16] and the synchronisation of neuronal connections [10], making it useful in stoplearning control circuits. These features have not yet been proven inany research for the analogue astrocyte circuit proposed in [27].…”

Section: Discussionmentioning

confidence: 99%

“…Although astrocyte circuits have not been extensively used in previous spike‐based neuromorphic learning circuits and architectures, new experimental results indicate the significant role of astrocytes that actively participate in information processing, synaptic transmission [10–13] and spike synchronisation [14]. Therefore, our new spike‐based learning circuit that deploys the astrocyte could improve the learning and information processing dynamics of spiking neural networks.…”

Section: Introductionmentioning

confidence: 99%

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New analogue stop‐learning control module using astrocyte for neuromorphic learning

Almasi

Karimi

Ranjbar

et al. 2020

IET Circuits, Devices & Systems

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Learning algorithms and devices are an essential part of neural networks and neuromorphic architectures. Astrocyte, as an important element in the learning of neural networks, is believed to play a key role in long-term synaptic plasticity and memory. In addition, recent experimental observations indicate that astrocytes are active elements in learning in complex networks. In this study, the authors propose a new analogue astrocyte circuit that consumes fewer numbers of transistors compared to its previous counterparts. The authors then use this astrocyte to design a novel analogue circuit to implement a stop-learning mechanism in a spike-based learning algorithm and present its neuromorphic very large scale integration (VLSI) simulations. Experimental results demonstrate that the designed circuit can precisely implement the learning mechanisms shown by previous studies implementing spike-based learning rules without astrocyte. The proposed circuit proposes the first analogue stop-learning control algorithm that uses astrocytes. It has been designed and simulated in Taiwan semiconductor manufacturing company (TSMC) 0.35 µm complementary metal-oxide semiconductor (CMOS) technology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New analogue stop‐learning control module using astrocyte for neuromorphic learning

Almasi

Karimi

Ranjbar

et al. 2020

IET Circuits, Devices & Systems

Self Cite

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“…In this design, the Izhikevich neuron model is used due to its capability to exhibit adaptation, which is a key feature of mechanoreceptors, and also to reproduce the dynamic characteristics of the both spiking and bursting responses. The dynamics of the membrane potential, v , of the SA-I mechanoreceptors are as follows: (Ranjbar and Amiri, 2016 );…”

Section: Methodsmentioning

confidence: 99%

“…One of the most common methods to realize the neural computational models is developing hardware circuit due to its high operating efficiency for practical applications (Cassidy et al, 2011 ; Nazari et al, 2014a ; Ranjbar and Amiri, 2016 ). 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 ).…”

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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“…Considering the fact that the design of the basic units of the nervous system in the form of hardware in neuromorphic systems has attracted a lot of attention, 3 in recent years, systems efficiency and reliability have increased by inspired hardware from brain in the form of analog [4][5][6] and digital 7-10 neuromorphic architecture. In addition, recent studies have focused on neuron-astrocyte interactions and synaptic plasticity [11][12][13][14] and have designed neuromorphic architecture based on neural cells interactions.…”

Section: Introductionmentioning

confidence: 99%

Digital realization of the proposed linear model of the Hodgkin‐Huxley neuron

Amiri

Nazari

Faez

2019

Circuit Theory & Apps

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Summary It seems that computing systems that imitate the brain can be achieved by integrating of electronics and neuroscience. In recent years, neuromorphic systems have been developed by the fusion of electronics and neuroscience. Since neurons are the basis of neural systems, constructing the optimized digital neuron plays a critical role in neuromorphic applications. Furthermore, the dynamic of ionic channels, which are the causative agents of synaptic plasticity is the main characteristic of biological neurons. The Hodgkin‐Huxley neuron model is a mathematical description of biological neuron that is widely used in neuroscience to explore the relation of action potential propagation and information transmission. This model consists of nonlinear differential equations, which approximates the electrical characteristics of excitable cells such as neurons and cardiac muscle. In this paper, a simplified version of the Hodgkin‐Huxley neuron model was proposed by substituting its complex nonlinear dynamics with linear ones. Next, a digital circuit for the proposed linear model was designed, which can be implemented on a low‐cost hardware platform, such as field‐programmable gate array (FPGA). Comparison of MATLAB simulation of original model and Vivado simulation of the proposed digital circuit confirm that obtained results are in good agreement. The proposed digital circuit compared with the earlier circuits is more successful in terms of replicate essential characteristics of spiking responses and ionic currents in the biologically plausible model of neural activities. This new digital design will be applicable in developing neuro‐inspired chips and exploring the brain's functionality in information processing.

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Analog implementation of neuron–astrocyte interaction in tripartite synapse

Cited by 17 publications

References 49 publications

New analogue stop‐learning control module using astrocyte for neuromorphic learning

New analogue stop‐learning control module using astrocyte for neuromorphic learning

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor

Digital realization of the proposed linear model of the Hodgkin‐Huxley neuron

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