Abstract: Threshold-linear networks (TLNs) display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a detailed mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. These results enable us to design networks that count stimulus pulses, track position, and encode multiple locomotive gaits in a single central pattern generator circuit.

Some of my past and current research looks at "decisions from  experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information, e.g., vicarious feedback (anecdotal/social and/or in the form of statistical distributions of outcomes) or science- or model-based outcome predictions to make “decisions from description.”  What happens when these differe

Graphic and verbal communication are typically thought to work in very different ways. While speech uses a conventionalized vocabulary that is acquired from children’s environments, drawing is assumed to reflect the articulation of how people see and think, with learning based on “artistic talent.” Yet, research from linguistics and cognitive science upends these assumptions, suggesting that these domains are actually not so distinctive.

Learning and decision making have hitherto been investigated almost exclusively in multicellular neural organisms.

Neural responses and perception of visual inputs strongly depend on the spatial context, i.e., what surrounds a given object or feature. I will discuss our work on developing a visual cortical model based on the hypothesis that neurons represent inputs in a coordinate system that is matched to the statistical structure of images in the natural environment. The model generalizes a nonlinear computation known as normalization, that is ubiquitous in neural processing, and can capture some spatial context effects in cortical neurons.

This is an exciting time for scientists who are interested in cognitive development: there is now a wealth of easily-accessible data that can be used to ask interesting questions about how psychological, neural, and genetic factors affect changes in cognitive functions across the lifespan - and how they differ between individuals. In this talk, I'll describe several studies that apply individual-differences methods to large-scale, sometimes longitudinal datasets that include cognitive and biological information.

Un centre de recherche multidisciplinaire sur le capital humain

Création d'une équipe de recherche sur les neurosciences théoriques, un environnement propice aux échanges, aux collaborations et à la formation

This talk will present a framework for modelling dopamine function in the mammalian brain. In this framework, dopaminergic neurons projecting to different parts of the striatum encode errors in predictions made by the corresponding systems within the basal ganglia. These prediction errors are equal to differences between rewards and expectations in the goal-directed system, and to differences between the chosen and habitual actions in the habit system. The prediction errors enable learning about rewards resulting from actions and habit formation.