Background noise in biological cortical microcircuits constitutes a powerful resource to assess their computational tasks, including, for instance, the synchronization of spiking activity, the enhancement of the speed of information transmission, and the minimization of the corruption of signals. We explore the correlation of spontaneous firing activity of ≈ 100 biological neurons adhering to engineered scaffolds by governing the number of functionalized patterned connection pathways among groups of neurons. We then emulate the biological system by a series of noise-activated silicon neural network simulations. We show that by suitably tuning both the amplitude of noise and the number of synapses between the silicon neurons, the same controlled correlation of the biological population is achieved. Our results extend to a realistic silicon nanoelectronics neuron design using noise injection to be exploited in artificial spiking neural networks such as liquid state machines and recurrent neural networks for stochastic computation.

Role of Noise in Spontaneous Activity of Networks of Neurons on Patterned Silicon Emulated by Noise{ extendash}activated {CMOS} Neural Nanoelectronic Circuits / R. Hasani, G. Ferrari, H. Yamamoto, T. Tanii, E. Prati. - In: NANO EXPRESS. - ISSN 2632-959X. - 2:2(2021), pp. 020025.1-020025.15. [10.1088/2632-959x/abf2ae]

Role of Noise in Spontaneous Activity of Networks of Neurons on Patterned Silicon Emulated by Noise{ extendash}activated {CMOS} Neural Nanoelectronic Circuits

E. Prati
Ultimo
2021

Abstract

Background noise in biological cortical microcircuits constitutes a powerful resource to assess their computational tasks, including, for instance, the synchronization of spiking activity, the enhancement of the speed of information transmission, and the minimization of the corruption of signals. We explore the correlation of spontaneous firing activity of ≈ 100 biological neurons adhering to engineered scaffolds by governing the number of functionalized patterned connection pathways among groups of neurons. We then emulate the biological system by a series of noise-activated silicon neural network simulations. We show that by suitably tuning both the amplitude of noise and the number of synapses between the silicon neurons, the same controlled correlation of the biological population is achieved. Our results extend to a realistic silicon nanoelectronics neuron design using noise injection to be exploited in artificial spiking neural networks such as liquid state machines and recurrent neural networks for stochastic computation.
patterned adhering scaffolds; cortical microcircuits; tonic spiking; silicon brains; neuromorphic engineering; noise assisted information processing
Settore FIS/03 - Fisica della Materia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/905450
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