Neural Dynamics, Adaptive Computations, and Sensory Invariance in an Olfactory System

Download or read book Neural Dynamics, Adaptive Computations, and Sensory Invariance in an Olfactory System written by Srinath Nizampatnam and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensory stimuli evoke spiking activities that are patterned across neurons and time in the early processing stages of olfactory systems. What features of these spatiotemporal neural response patterns encode stimulus-specific information (i.e. 'neural code'), and how they are translated to generate behavioral output are fundamental questions in systems neuroscience. The objective of this dissertation is to examine this issue in the locust olfactory system. In the locust antennal lobe (analogous to the vertebrate olfactory bulb), a neural circuit directly downstream to the olfactory sensory neurons, even simple stimuli evoke neural responses that are complex and dynamic. We found each odorant activated a distinct neural ensemble during stimulus presentation (ON response) and following its termination (OFF response). Our results indicate that the ON and OFF ensemble neural activities differed in their ability to recruit recurrent inhibition, entrain field-potential oscillations, and more importantly in their relevance to behavior (initiate versus reset conditioned responses). Furthermore, when the same stimulus was encountered in a multitude of ways, we found that the neural response patterns in individual neurons varied unpredictably. Intriguingly, a simple, linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of ON neurons was sufficient to achieve robust recognition. We found that the incorporation of OFF neurons could enhance pattern discriminability and reduce false positives thereby further improving performance. These results indicate that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic. Lastly, we examined how the ON and OFF neural ensembles varied with stimulus intensity. We found that neurons that were ON responsive at low intensity switched and became OFF responsive at higher intensities. Similarly, OFF responsive neurons at low intensity were recruited and responded during odor stimulation at higher intensities. We found a competitive network involving two sub-categories of GABAergic local neurons can mediate this switch between ON and OFF responsive ensembles and how they vary as a function of stimulus intensity. In sum, our results provide a comprehensive understanding of how a relatively simple invertebrate olfactory system could perform complex adaptive computations with very simple individual components.