Invariant odor recognition with ON–OFF neural ensembles

Abstract

The smell of coffee is the same whether it is smelled in a coffee shop or grocery shop (different backgrounds), on a hot day or a cold day (different ambient conditions), after lunch or dinner (different temporal contexts), or using a deep inhalation or normal inhalation (different stimulus dynamics). This feat of pattern recognition that is still difficult to achieve in artificial chemical sensing systems is performed by most sensory systems for their survival. How is this capability achieved? We explored this issue. We found that there are two orthogonal ensembles of neurons, one activated during stimulus presence (ON neurons) and one activated after its termination (OFF neurons), and both contribute to this important computation in a complementary fashion.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 07, 2022
Source ID
10.1073/pnas.2023340118

Entities

People

  • Baranidharan Raman
  • James Li
  • Lijun Zhang
  • Rishabh Chandak
  • Srinath Nizampatnam

Organizations

  • National Science Foundation
  • Office of Naval Research
  • Washington University in St. Louis

Tags

Readers

  • Gender and Food Studies
  • Immunology and Pathology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks