Rival Networks: Dissecting the Canonical Circuit of Bi-stable Visual Perception

首席研究员（最后，第一，中）：帕拉吉纳，加纳

Principal Investigator (Last, First, Middle): Palagina, Ganna Abstract / Summary Rival Networks: Dissecting the Canonical Circuit of Bi-stable Visual Perception Viewing visual stimuli with several mutually exclusive interpretations causes subjective perception to vacillate between the interpretations. This process is known as multi-stable perception and provides an excellent well- controlled model for studying how the percepts are formed and maintained in the brain. Multiple hypotheses are proposed for the circuit mechanisms of multi-stable perception: changes in neuronal synchrony, adaptation of population firing rates, mutual inhibition between rival neuronal populations, neural noise and hierarchical inference across the network of cortical areas and subcortical structures. Supporting evidence for involvement of these processes comes from computational models, psychophysical studies, fMRI and TMS studies in humans and single-unit primate electrophysiology. These approaches established that multi-stable perception is a distributed process involving the cooperative network of both low-level and high- level cortical areas. They also made it clear that the activity of single units in the brain cannot be used as a clear indicator of the rivaling percepts, and that one must look at the level of neuronal circuit to understand the process. However, until recently, studying population responses and interplay between the circuits in different cortical areas at the single-cell resolution was a limited possibility. Currently, there is no mechanistic understanding of canonical cortical computations that underlie perceptual transitions at the level of cortical column and local sub-networks. Moreover, there is no circuit-level single-cell data on the interactions between the circuits across the cortical area borders during perceptual rivalry. Recent advances enable us for the first time to map at single cell resolution the dynamics of columnar sub-networks during bi-stable perception. We will do this in in primary sensory area (V1) that is required for percept alternations. To study the behavioral contribution of circuit components and test for causality we will use optogenetic control of specific cell populations with SLM (aim #1). We will then follow up by whole- hemisphere imaging to identify higher-order areas that are the part of multi-stable perception functional network. We will do multiple-area imaging of layer 2/3 sub-circuits in V1, V2 and VRL/A/ALto get a handle on long-range interactions between circuit components and evolution of percept and reversal encoding in primary sensory area V1 and higher-order areas up in the cortical hierarchy during bi-stable perception (aim #2). Expectations: 1) Identify the “bi-stable perception network” of the mouse brain, composed of areas putatively involved in percept stability and reversal 2) Obtain the first comprehensive picture of the dynamics of circuit interactions between sub-networks of pyramidal cells and interneurons in cortical columns during bi-stable perception, across V1, LGN inputs to V1, V2 and visuomotor area VRL/A/AL 3) Identify the contribution of specific processes to perceptual reversals: firing rates adaptation, mutual inhibition of rival sub-networks, synchrony and variability of firing.