Project description
Machine learning discovers the rules governing the neural substrates of real learning
Models are used in virtually all fields from aerospace design to drug development. They are developed with observational or experimental data and refined through additional data and high-tech methods including machine learning. The better they get at predicting real-life and new outcomes, the more valuable they are in so many ways. Modelling the rules governing the changing 'weights' of synaptic connection between neurons (synaptic plasticity) that underlies learning and memory is an extremely challenging task. The EU-funded SynapSeek project will bring together a wealth of published data with advanced machine learning methods to 'discover' synaptic plasticity rules in silico.
Objective
How do we learn to dance, play an instrument, or a game as complex as chess or go? How do we make a memory? The common answer to these questions is “through synaptic plasticity”, through changing the synaptic connectivity of neural circuits so that representative brain activity can be reliably triggered. Such connectivity changes are governed by rules, i.e. synaptic mechanisms which monitor the activity of their environment and stereotypically strengthen or weaken synapses accordingly. The shape and mode of operation of these rules is still largely unknown: For the more than hundred different connection types in cortical circuits, only a handful of rules has been described at all. Similarly, testing observed rules in simulations of cortical function has only seen limited success. Our slow progress is due to the extraordinary difficulty of measuring and observing synapses without interference.
Here, we propose a new approach. By utilizing the growing power of machine learning methods we can deduce synaptic plasticity rules directly. Newly developed search algorithms and sheer computational power allow us to integrate published data and infer synaptic rules in silico. We aim to (1) develop a new mathematical expression of synaptic plasticity rules, experimentally appropriate and flexible enough to be implemented in a Machine Learning framework, dubbed SYNAPSEEK. Next (2), we will apply SYNAPSEEK to deduce the rules for building various neural structures with increasing complexity. Finally (3), we will incorporate additional constraints to SYNAPSEEK to develop synaptic rules that shape network function as much as its structure. Our work will establish, for the first time, canonical sets of synaptic plasticity rules, based on the circuit structure they must produce, and the function they are meant to support. SYNAPSEEK will have immediate and wide ranging applications, from a basic understanding of cortical development to better protocols for Deep Brain Stimulation.
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.
-
H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
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
See all projects funded under this funding scheme
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-2018-COG
See all projects funded under this callHost institution
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.
3400 KLOSTERNEUBURG
Austria
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.