Circuit modelling of 2-AG indirect pathway via astrocyte as a catalyst for synaptic self repair

Azad, Fatemeh; Shalchian, Majid; Amiri, Mahmood

doi:10.1007/s10470-018-1106-8

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…These figures show that the outputs of the proposed circuit are in good agreement with the other stop-learning spiking circuit for a range of input currents. This is all achieved while the proposed astrocyte circuit utilises a fewer number of transistors compared to previous astrocyte circuits in the literature [26,27]. As shown in these figures, when the input neuron current is increased, the firing rate of the neuron increases, therefore the input astrocyte voltage (V 1 ) faster reaches the certain values to start and stop the learning module circuit.…”

Section: Simulation Resultsmentioning

confidence: 73%

“…These figures show that the outputs of the proposed circuit are in good agreement with the other stop‐learning spiking circuit for a range of input currents. This is all achieved while the proposed astrocyte circuit utilises a fewer number of transistors compared to previous astrocyte circuits in the literature [26, 27].…”

Section: Simulation Resultsmentioning

confidence: 93%

“…Implementing efficient VLSI spike-based neural networks has always been essential to high-performance neuromorphic systems. To design such systems, devising novel spike-based algorithms and VLSI circuits that are well-matched with existing solutions and contribute to the adaptation and organisation of the system is of [27] analogue Postnov 46 sub-threshold ±1.5 [26] digital-analogue Li-Rinzel -above-threshold ±1.5 and ±5 paramount importance. One of the main components of any neuromorphic system targeted for real-world applications is learning.…”

Section: Resultsmentioning

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: Simulation Resultsmentioning

confidence: 73%

Section: Simulation Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Almasi

Karimi

Ranjbar

et al. 2020

IET Circuits, Devices & Systems

View full text Add to dashboard Cite

show abstract

“…Previous implementations of the SR mechanism employed the transition probability (PR) of synapses to demonstrate this concept. 30 , 31 , 76 , 77 , 78 , 79 However, these studies were limited to use a probabilistic neuronal firing mechanism in a small network, and the entire process was anchored based on a constant modulation from astrocytes. Additionally, to test those models, the damage had to be manually imposed on some of the synapses.…”

Section: Discussionmentioning

confidence: 99%

Astrocyte’s self-repairing characteristics improve working memory in spiking neuronal networks

Naghieh,

Delavar,

Amiri

et al. 2023

iScience

Self Cite

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“…Memristors and memristive electronics can already reproduce in silico both spiking neuron dynamics, synaptic signal transmission, and synaptic plasticity. With respect to the implementation of astrocytes and the astrocyte-to-neuron control in silico, several articles reported successful implementations of the astrocyte dynamics [22], [23], [67]. This suggests that hardware implementations of SNN-based neuromorphic memory could be viable in the near future.…”

Section: Discussionmentioning

confidence: 99%

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network

Gordleeva,

Tsybina,

Krivonosov

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific set-up centered around a relatively small set of patterns presented in a particular environment. For example, at a party, people recognize friends immediately, without deep analysis, just by seeing a fragment of their clothes. This setup with reduced "ontology" is referred to as a "situation." Situations are usually local in space and time. In this work, we propose that neuron-astrocyte networks provide a network topology that is effectively adapted to accommodate situationbased memory. In order to illustrate this, we numerically simulate and analyze a well-established model of a neuronastrocyte network, which is subjected to stimuli conforming to the situation-driven environment. Three pools of stimuli patterns are considered: external patterns, patterns from the situation associative pool regularly presented to the network and learned by the network, and patterns already learned and remembered by astrocytes. Patterns from the external world are added to and removed from the associative pool. Then, we show that astrocytes Manuscript

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Circuit modelling of 2-AG indirect pathway via astrocyte as a catalyst for synaptic self repair

Cited by 8 publications

References 33 publications

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

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

Astrocyte’s self-repairing characteristics improve working memory in spiking neuronal networks

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network

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