Gonzalo Cogno Lab

Brain function emerges from the dynamic coordination of interconnected neurons. It is not clear, however, what the interplay between different types of neurons is, and how, together, they underlie cognition and behaviour. Addressing these questions requires a synergistic approach, where computational and experimental neuroscience work hand-in-hand. Our group adopts this synergic approach and seeks to understand the mechanisms and dynamics underlying network computation. We are part of the Kavli Institute for Systems Neuroscience and Center for Algorithms in the Cortex.

Ultraslow periodic sequences

Many of our research questions are directed towards understanding how, and in which conditions, neuronal activity organizes into ultraslow oscillations, and ultraslow periodic sequences.

See our paper: Gonzalo Cogno et al., 2024.

Linking connectivity and dynamics

We are also interested in understanding how network connectivity shapes neuronal dynamics, and how neural networks compute. Here are some questions we are interested in:

– How does network connectivity shape network dynamics in heterogeneous circuits?

– How are different cell types, with cell types understood in a broad manner, from molecularly defined to neurons expressing selectivity to different stimuli or behaviours, connected within and across circuits?

– What are the network mechanisms by which population dynamics, for example sequences of neural activity, reshape across neural circuits or brain functions?

– How do neural networks process information and represent features of the external world in the form of flexible population codes?

Methods

We build models that explain features of experimental data and generate new hypotheses that are used to guide new experiments.

1. Computational models. We build spiking and firing rate neural network models that undergo learning processes, e.g. through plasticity rules or via supervised methods.

2. Analysis of neural data. We use approaches from mathematics, statistical physics, information theory, dynamical systems theory and machine learning.

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