5 research outputs found

    A neuromorphic CMOS circuit with self-repairing capability

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    Neurophysiological observations confirm that the brain not only is able to detect the impaired synapses (in brain damage) but also it is relatively capable of repairing faulty synapses. It has been shown that retrograde signaling by astrocytes leads to the modulation of synaptic transmission and thus bidirectional collaboration of astrocyte with nearby neurons is an important aspect of self-repairing mechanism. Specifically, the retrograde signaling via astrocyte can increase the transmission probability of the healthy synapses linked to the neuron. Motivated by these findings, in the present research, a CMOS neuromorphic circuit with self-repairing capabilities is proposed based on astrocyte signaling. In this way, the computational model of self-repairing process is hired as a basis for designing a novel analog integrated circuit in the 180-nm CMOS technology. It is illustrated that the proposed analog circuit is able to successfully recompense the damaged synapses by appropriately modifying the voltage signals of the remaining healthy synapses in the wide range of frequency. The proposed circuit occupies 7500- μm2 silicon area and its power consumption is about 65.4 μW . This neuromorphic fault-tolerant circuit can be considered as a key candidate for future silicon neuronal systems and implementation of neurorobotic and neuro-inspired circuits.10.13039/501100004837-Ministerio de Ciencia e Innovación (Grant Number: PID2019-105556GB-C31). 10.13039/501100005317-Kermanshah University of Medical Sciences, Kermanshah, IranPeer reviewe

    Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors

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    10.1038/s41598-021-81199-3Scientific Reports111210

    A functional spiking neuronal network for tactile sensing pathway to process edge orientation

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    10.1038/s41598-020-80132-4Scientific Reports111132

    A biomimetic circuit for electronic skin with application in hand prosthesis

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    10.1109/tnsre.2021.3120446IEEE Transactions on Neural Systems and Rehabilitation Engineering292333-234
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