Anatomical connectivity

Brain networks communicate through anatomical connections which are thought to mediate flexible, state-dependent interactions. We call the resulting pattern of communication “effective” or “functional” connectivity. In the motor system, within-day learning may rely primarily on modulation of networks, although the source of this modulation is unclear [1]. Over longer timescales, changes in anatomical connectivity can shape behavior through processes like Hebbian plasticity [2]. Understanding how these mechanisms combine to shape learning will require measurements of anatomical and functional c

While anatomical connectivity changes slowly through synaptic learning, the functional connectivity of neurons changes rapidly with ongoing activity of neurons and their functional interactions. Functional networks of neurons and neural populations reflect how their interactions change with behaviors, stimulus types, and internal states. Therefore, the information propagation across a network can be analyzed through the varying topological properties of the functional networks. Our study investigates the functional networks of the visual cortex at both the single-cell and population levels. Ou

The characterization of brain activity has become a cornerstone of neuroscience to study cognition and neuropathologies. Here we focus on models of neural masses that aim to fit neurophysiological signals like EEG and MEG, in particular by optimizing interactions between brain regions, following previous work on the dynamic causal model (DCM) . We compare several modern Bayesian inference schemes for model inversion on simulated data . We benchmark the identifiability of local parameters governing the interplay between several neuronal populations in each region, as well as global parameters t

A major neuroscientific challenge is to identify the neural mechanisms that support consciousness. Though experimental studies have accumulated evidence about the location of the neural substrate of consciousness, we still lack a full understanding of why certain brain areas, but not others, can support consciousness. In this talk, I will give an overview of our approach, taking advantage of the theoretical framework provided by Integrated Information Theory (IIT). First, I will introduce results showing that a maximum of integrated information within the human brain matches our best evidence