Topic: Principal Components Analysis

Behavior results from neural computations that occur at the level of populations of neurons, and understanding how a given computation produces a particular behavior requires a characterization of how distinct signals interact and evolve. For example, in motor cortex, discrete reaches are generated by a combination of ‘preparatory’ and ‘execution-related’ signals. While the number of computationally-relevant signals is much lower than the number of neurons in a population – and is therefore amenable to dimensionality-reduction methods – standard techniques, like principal components analysis,

Sensory representations are typically thought as neuronal activity patterns that encode physical attributes of the outside world. However, increasing evidence is showing that as animals learned the association between a sensory stimulus and its behavioral relevance, stimulus representation in sensory cortical areas can change. In this seminar I will present recent experiments from our lab showing that the activity in the olfactory piriform cortex (PC) of mice encodes not only odor information, but also non-olfactory variables associated with the behavioral task. By developing an associative ol

Many networks in the brain are sparsely connected, and the brain eliminates synapses during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a synapse between two neurons is a difficult computational problem, depending on the role that both neurons play and on all possible pathways of information flow between them. Noise is ubiquitous in neural systems, and often considered an irritant to be overcome. In the first part of this talk, I will suggest that noise could play a functional role in synaptic pruning, allo