Principal investigators: Sophie Denève, Nabil Bouaouli and Timm Lochmann
We seek to build new models of neural processing that parallel the explicit, neural space, consisting of spikes, synapses, neurons, and neural circuits, and an implicit probability space that implements Bayesian learning and inference in an underlying statistical model.
As a starting point, we propose to view cortical neurons as accumulating evidence over time about a particular hypothesis regarding the state of the environment, the body or the task. This hypothesis could be the presence or absence of a preferred stimulus in this neuron's receptive field, the appropriateness of a particular movement, or a more abstract component in a combinatorial code, a "hidden variable" describing an underlying statistical structure in sensory data. However, rather than computing directly an answer to their questions (is the hypothesis true or false?), neurons compute and communicate to other neurons their certainty about this hypothesis being true or false.
Because the state of these variables are likely to change over time, and the main difficulty is to correctly detect these changes, spikes, and more generally neural activity, signals the occurrence of new probabilistic evidence that could be not predicted from previous spikes fired by the same neuron pr other neurons in the same population. In support of this hypothesis, neural responses to salient and unpredictable events, or to errors of predictions, tend to be much stronger than responses to static, boring stimuli. In particular, our current research suggests that a neuron involved in such probabilistic computations would have the dynamics of a leaky integrate and fire neuron with adaptation (spike-based, synaptic) and plasticity (spike time dependant) close to those observed in cortical neurons.
Denève, S., Bayesian Spiking Neurons I: Inference, Neural Computation, 20, 91-117 (2008).
Denève, S., Bayesian Spiking Neurons II: Learning, Neural Computation, 20, 118-145 (2008).
Mongillo, G. and Denève, S., Online Learning with Hidden Markov Models, Neural Computation, 20(7), 1706-16 (2008).
Lochmann, T. and Denève, S., Information transmission with spiking Bayesian neurons, New Journal of Physics, 10, article ID: 055019 (2008).