Skip to main content

Spike-based predictive coding: Closing the loop between neural dynamics and computation

Objective

Progress in understanding brain functions rely in great part on filling the conceptual and experimental gaps between different levels of analysis, from single neurons to behaviour. Thus, “rate models”, units of representations are the mean activity of large neural populations, while function and behaviour emerge from the responses of very large networks. While experimental investigations have focused on predicting (describing) spiking neural responses from their (sensory or synaptic) inputs, functional models instead concentrate on understanding how neural populations represent properties of (i.e. predict) the world.
This proposal aims at developing an alternative approach, spike-based predictive coding. It combines two basic hypotheses: Neural networks reliably estimate the state of the environment based on their inputs and prior experience. And their dynamics insures that these estimates can be decoded from their spike trains by postsynaptic integration . By monitoring and decoding its own outputs, the neural structure itself closes the loop between computation and dynamics.
Membrane potentials of model neurons compute a difference between the state estimates constructed from their inputs and the estimate encoded in their outputs. Interestingly, this purely functional approach converges towards powerful descriptive models of spiking neurons, e.g. adaptive integrate and fire neurons, chaotic attractors in balanced spiking networks and generalized linear models (GLMs).
We will use this approach to explore the dynamics of single spiking neurons, suggest new ways of interpreting and exploring sensory and motor spiking neural representations, re-explore the role of top-down attention in sensory processing, and show that previous rate-based interpretations severely under-estimated the precision of the neural code.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence

Call for proposal

ERC-2012-StG_20111109
See other projects for this call

Funding Scheme

ERC-SG - ERC Starting Grant

Host institution

ECOLE NORMALE SUPERIEURE
Address
45, Rue D'ulm
75230 Paris Cedex 05
France
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 276 800
Principal investigator
Sophie Deneve (Dr.)
Administrative Contact
Damien Vogel (Mr.)

Beneficiaries (1)

ECOLE NORMALE SUPERIEURE
France
EU contribution
€ 1 276 800
Address
45, Rue D'ulm
75230 Paris Cedex 05
Activity type
Higher or Secondary Education Establishments
Principal investigator
Sophie Deneve (Dr.)
Administrative Contact
Damien Vogel (Mr.)