Open AccessInvariant odor recognition with ON–OFF neural ensembles
Author(s) -
Srinath Nizampatnam,
Lijun Zhang,
Rishabh Chandak,
James Li,
Baranidharan Raman
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2023340118
Subject(s) - stimulus (psychology) , sensory system , neural system , artificial intelligence , pattern recognition (psychology) , population , computer science , artificial neural network , neuroscience , biology , psychology , cognitive psychology , demography , sociology
Significance 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.