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

Ramin Hasani, Giorgio Ferrari, Hideaki Yamamoto, Takashi Tanii, Enrico Prati*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number020025
JournalNano Express
Volume2
Issue number2
DOIs
Publication statusPublished - 2021 Jun 1

Keywords

  • cortical microcircuits
  • neuromorphic engineering
  • noise assisted information processing
  • patterned adhering scaffolds
  • silicon brains
  • tonic spiking

ASJC Scopus subject areas

  • Biomaterials
  • Electronic, Optical and Magnetic Materials
  • Polymers and Plastics
  • Materials Science (miscellaneous)

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