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

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.

Experimental validation of an analog spiking neural network with STDP learning rule in CMOS technology

“…The resulting characteristic curve approximates a STDP learning rule. The noise of the measurement is common in analog networks and is generally considered an advantage in terms of computational power [22] and biological plausibility [23]. The general results agree with the simulations but shifted in time by ≈20ms.…”

Revealing neural dynamical structure of C. elegans with deep learning

“…Gaussian white noise is commonly employed in biological modeling and stems from diverse sources. 18 , 27 , 33 These sources categorize white noise into sensory noise, cellular noise, electrical noise, and synaptic noise. 34 In our model, Gaussian noise plays a crucial role in facilitating escape from attractors.…”

Analog-circuit implementation of multiplicative spike-timing-dependent plasticity with linear decay

“…With the increasing demand for faster, safer, and more energy-efficient data processing, neuromorphic computing has attracted attention as a potential paradigm to support next-generation information and communication technology [1][2][3]. Hardware technology for the efficient implementation of artificial neural networks, particularly in the form of spiking neural networks (SNNs) [4][5][6][7][8][9], has also been studied intensively to decrease power consumption [10][11][12][13][14][15][16][17][18][19]. A central challenge in the development of SNN hardware is the implementation of learning, or the tuning of synaptic connection strengths in response to training events, which is achieved mostly through software programming [3].…”