Objective
Just as our experience has its origin in our perceptions, our perceptions are fundamentally shaped by our experience. How does the brain build expectations from experience and how do expectations impact perception? In a Bayesian framework, expectations determine the environment’s prior probability, which combined with stimulus information, can yield optimal decisions. While the accumulation-to-bound model describes temporal integration of sensory inputs and their combination with the prior, we still lack electrophysiological evidence showing neural circuits that integrate previous events adaptively to generate advantageous expectations.
I aim to understand (1) how circuits in the cerebral cortex integrate the recent history of stimuli and rewards to generate expectations, (2) how expectations are combined with sensory input across the processing hierarchy to bias decisions and (3) whether the dynamics of the expectation can dominate neuronal and choice variability. I will train rats in a new auditory discrimination task using predictable stimulus sequences that, once learned, are used to compute adaptive priors that improve discrimination. I will perform population recordings and optogenetic manipulations to identify the brain areas involved in the computation of priors in the task. To reveal the circuit mechanisms underlying the observed dynamics I will train a computational network model to classify fluctuating inputs and, by adapting its dynamics to the statistics of the stimulus sequence, accumulate evidence across trials to maximize performance. The model will generalize the accumulation-to-bound model by integrating information across various time scales and will partition choice variability into that caused by the dynamics of the prior or by fluctuations in the stimulus response. My proposal points at a paradigm shift from viewing neuronal variability as a corrupting source of noise to the result of our brain’s inevitable tendency to predict the future.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences neurobiology
- natural sciences computer and information sciences software
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- natural sciences mathematics applied mathematics statistics and probability
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-COG - Consolidator Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2015-CoG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08036 BARCELONA
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.