An analog astrocyte–neuron interaction circuit for neuromorphic applications

Abstract

Recent neurophysiologic findings have shown that astrocytes (the most abundant type of glial cells) are active partners in neural information processing and regulate the synaptic transmission dynamically. Motivated by these findings, in the present research, an analog neuromorphic circuit to study neuron-astrocyte signaling is presented. In this analog circuit, the firing dynamics of the neuron is described by Izhikevich neuron circuit and the Ca 2+ dynamics of a single astrocyte explained by a functional simplified model introduced by Montaseri et al. Using the proposed neuronastrocyte circuit, it is demonstrated that the proposed analog astrocyte is able to activate the analog neuron or change the neuron spiking frequency through bidirectional communication. This suggests that analog astrocyte is capable of modulating spike transmission frequency. Moreover, our results suggest that the analog circuit of neuron-astrocyte crosstalk produces diverse neural responses and therefore enhances the information processing capabilities of the neuromorphic circuits. This is suitable for applications in reconfigurable neuromorphic devices which implement biologically brain circuits.

Analog implementation of neuron–astrocyte interaction in tripartite synapse

“…(3)] [19]. It exploits underlying nonlinear characteristics of MOS transistors (in the analog domain, above threshold) to implement the astrocyte model using a low number of transistors.…”

“…In this way, the two circuits that produce the p and q dynamics were obtained [19]. Parameter values of astrocyte circuit are list in Table 3.…”

“…Increasing evidence indicate that astrocytes also participate in higher cognitive functions related to learning and memory [1]. While the interest in understanding the biological operations and computational modeling of neuron-astrocyte signaling is constantly increasing, the neuromorphic neuron-astrocyte cross-talk circuit is less studied [13][14][15][16][17][18][19][20].…”

“…While these architectures are useful for discovering the computational properties of large-scale models of the nervous system, the challenge of building physical devices that can behave intelligently in real world and exhibit cognitive abilities still remains open [26]. In fact, design and their effective implementation in hardware is now an active domain of research [18,19,27] and several procedures have been proposed for designing and construction of brain like processing systems for conventional computing [28]. It should be pointed out that more theoretical backgrounds are still required to find the computational properties of nervous system [29][30][31][32].…”

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor

“…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 ). 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 ).…”