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.

Linking connectivity and dynamics in heterogeneous circuits

We are interested in understanding how network connectivity shapes neural dynamics, and how neural networks compute. Here are some specific questions we are currently working on:

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

– Which network mechanisms enable a gradient of responses, from highly ordered to disordered, within a circuit?

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

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

– 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, and jointly modulated at the population level?

Ultraslow periodic sequences and oscillations

Some 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.

We are currently focused on determining the extent to which ultraslow oscillations and sequences are present during free foraging conditions, and during sleep.

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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