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Neural Representation of Uncertainty in Mouse Visual Cortex

Reference	BB/X013308/1
Principal Investigator / Supervisor	Dr Nathalie Louise Isabelle Rochefort
Co-Investigators / Co-Supervisors	Professor Máté Lengyel
Institution	University of Edinburgh
Department	Centre for Discovery Brain Sciences
Funding type	Research
Value (£)	200,800
Status	Current
Type	Research Grant
Start date	01/02/2023
End date	31/01/2025
Duration	24 months

Abstract

Our decisions are usually based on information that is partial and may be ambiguous in several ways. Previous work supports the notion that humans and other animals perform (approximate) Bayesian decision making, appropriately representing and propagating uncertainty, in a broad range of cognitive domains (from perception to motor planning). However, the actual neuronal representations underlying these computations remain unknown. The central question of this project is: at which stage(s) of the decision making process is uncertainty represented in the brain? We address this question in the context of perceptual decision making, using the mouse primary visual cortex (V1) as a model system. We will use a dataset obtained in the Rochefort lab, in which the activity of large neuronal populations was recorded in V1 while mice were performing a visually-guided goal-directed behavioural task. After training animals, we manipulated both their perceptual and decision uncertainty by modifying specific features of the visual stimulus. The project is organised around 2 aims: - Aim 1. Analyse behavioural data in order to obtain trial-by-trial measures of perceptual and decision uncertainty. - Aim 2. Determine the neuronal representation of perceptual vs. decision uncertainty in mouse V1. This project will enable a strong interdisciplinary partnership between a neurophysiology (Rochefort) and a computational neuroscience (Lengyel) lab. This collaboration will drive research to comprehensively understand the encoding of perceptual uncertainty in the adult brain at the cellular, network, and behavioural level. It will also lead to the development of new analysis tools and new computational models of the neuronal implementation of decision-making in the brain. By integrating the skills and expertise from an experimental and a computational lab, members of both labs will develop new interdisciplinary expertise that is of high demand in this field of research.

Summary

When interacting with our environment, we need to evaluate the potential consequences of our decisions. However, our decisions are usually based on information that is partial and may be ambiguous in different ways. For example, when we are looking for our keys in the flat, we have to take into account that the keys may be partially or fully occluded, we may have imperfect memory about where we put them, or where we found them last time we lost them. Such uncertainty in various modalities (perceptual, motor, cognitive) presents a fundamental challenge that the brain must tackle in order to ensure that behaviour remains adapted to the constraints and demands of the environment. In this interdisciplinary project, we propose to bring together the expertise of neurophysiologists (Rochefort lab) and computational neuroscientists (Lengyel lab) to tackle this critical question in neuroscience: how does the brain encode uncertainty? A rich body of literature supports the notion that humans and other animals make decisions based on representing and propagating uncertainty (at least approximately) according to the rules of Bayesian decision making, in a broad range of cognitive domains (from sensory perception to motor planning and execution). However, the actual neuronal representations underlying these computations remain unknown. In particular, it is unclear whether uncertainty in the brain is represented opportunistically, i.e. only about decision variables (variables at the final stages of the decision-making process), or whether it is represented ubiquitously, throughout different stages of information processing, including perceptual uncertainty, memory uncertainty, as well as over variables relevant to the decision. In artificial intelligence, there are examples for both strategies, with complementary advantages and disadvantages. A fundamental factor limiting progress in resolving whether probabilistic representations in the brain are opportunistic or ubiquitous is the lack of experimental paradigms that can distinguish between uncertainty at different stages of decision making (e.g. uncertainty about perceptual vs. decision variables). Thus, the main goal of the project is to analyse behavioural and neural data recorded during a paradigm that has been specifically designed to address this challenge. We address this question in the context of perceptual decision making, using the mouse primary visual cortex (V1) as a model system. We will use a dataset obtained in the Rochefort lab, in which the activity of large neuronal populations was recorded in V1 while mice were performing a visually-guided goal-directed behavioural task. After training animals on the task, we manipulated the sensory uncertainty of the animals by modifying visual stimulus properties. The project is organised around 2 aims: - Aim 1. Analyse behavioural data in order to obtain trial-by-trial measures of perceptual and decision uncertainty. - Aim 2. Determine the neuronal representation of perceptual vs. decision uncertainty in mouse V1. This project will enable a strong interdisciplinary partnership between a neurophysiology (Rochefort) and a computational neuroscience (Lengyel) lab. This collaboration will drive research to comprehensively understand the encoding of perceptual uncertainty in the adult brain at the cellular, network, and behavioural level. It will also lead to the development of new analysis tools and new computational models of the neuronal implementation of decision-making in the brain. By integrating the skills and expertise from an experimental and a computational lab, members of both labs will develop new interdisciplinary expertise that is of high demand in this field of research.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	Supporting research in cognitive computational neuroscience [2022]
Funding Scheme	X – not Funded via a specific Funding Scheme

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