Objective
A fundamental issue in the study of human cognition is what computations are carried out by the brain to implement cognitive processes. The connectionist framework assumes that cognitive processes are implemented in terms of complex, nonlinear interactions among a large number of simple, neuron-like processing units that form a neural network. This approach has been used in cognitive psychology - often with some success – to develop functional models that clearly represent a great advance over previous verbal-diagrammatic models because they can produce highly detailed simulations of human skilled performance and its breakdown following brain damage. However, a crucial step for the computational modeling of cognition is to bridge the gap between function and structure. Much of the modeling work has been carried out using connectionist networks that have no biological plausibility beyond the metaphor of “neuron-like” processing. Most models have one, or more often a combination, of the following undesirable features: i) strictly feed-forward spread of activation (e.g. no feedback and/or lateral connections); ii) implausible learning procedures (e.g. error back-propagation); iii) implausible learning environment (e.g. supervised learning). Researchers have chosen to ignore these problems as it was seen as an essential compromise to achieve efficient learning of complex cognitive tasks. The aim of the present research program is to exploit the latest findings in neural network and machine learning research to develop generative connectionist models of cognition. Generative models are appealing because they represent plausible models of cortical learning that emphasize the mixing of bottom-up and top-down interactions in the brain. Moreover, generative models of cognition would offer a unified theoretical framework that encompasses classic connectionism and the emerging Bayesian approach to cognition, as well as a means to bridge the gap between neurons and behavior.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences artificial intelligence machine learning supervised learning
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- social sciences psychology cognitive psychology
- 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.
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.
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.
ERC-2007-StG
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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.
Host institution
35122 PADOVA
Italy
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.