A Neuromorphic CMOS Circuit With Self-Repairing Capability

Rahiminejad, Ehsan; Azad, Fatemeh; Parvizi-Fard, Adel; Amiri, Mahmood; Linares-Barranco, B.

doi:10.1109/tnnls.2020.3045019

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…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

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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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“…Another aspect that opens new perspectives is the myriad of neural sensors at our fingertips. As it can now be anticipated, CMOS technologies could fulfill the requirements for implantable long-term sensors, as their ultra-low power consumption, neuromorphic design, and, lately, their capability to self-repair should be exploited (Rahiminejad et al, 2021 ). Even though a great variety of recording devices provides us with data of ever-improving signal-to-noise ratios and of diminishing invasiveness, there are fundamental questions about extracellular action potentials that are, so far, unanswered.…”

Section: Discussionmentioning

confidence: 99%

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Bod

Rokai

Meszéna

et al. 2022

Front. Neuroinform.

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The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

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“…Finally, while there have been prior efforts at mimicking astrocyte functionality in hardware (Rahiminejad et al 2021), they primarily focus on implementing the detailed computational model in hardware without considering aspects of astrocyte functionality relevant to self-repair of neuromorphic hardware.…”

Section: Related Work and Main Contributionsmentioning

confidence: 99%

Astromorphic Self-Repair of Neuromorphic Hardware Systems

Zhuangyu

Islam

Sengupta

2023

AAAI

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While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. The work investigates stronger correlations with astrocyte computational neuroscience models to develop macro-models with a higher degree of bio-fidelity that accurately captures the dynamic behavior of the self-repair process. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults in neuromorphic hardware systems with significantly better accuracy and repair convergence for unsupervised learning tasks on the MNIST and F-MNIST datasets. Our implementation source code and trained models are available at https://github.com/NeuroCompLab-psu/Astromorphic_Self_Repair.

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A Neuromorphic CMOS Circuit With Self-Repairing Capability

Cited by 10 publications

References 47 publications

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

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

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Astromorphic Self-Repair of Neuromorphic Hardware Systems

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