Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Yarden Cohen, Tatiana A. Engel, Christopher Langdon, Grace W. Lindsay, Torben Ott, Megan A.K. Peters, James M. Shine, Vincent Breton-Provencher, Srikanth Ramaswamy

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Original languageEnglish (US)
Pages (from-to)8514-8523
Number of pages10
JournalJournal of Neuroscience
Issue number45
StatePublished - Nov 9 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Neuroscience


  • artificial neural networks
  • behavior
  • cognition
  • neuromodulators
  • plasticity
  • vision


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