Fast and stable discrimination in divergent-convergent neural networks: from Deep Learning back to Neuroscience

SEMINARIO DEL DOCTORADO DE ESTADÍSTICA, OPTIMIZACIÓN Y MATEMÁTICA APLICADA. CURSO 2016-2017

Título: Fast and stable discrimination in divergent-convergent neural networks: from Deep Learning back to Neuroscience

Ponentes: Prof. Dr. Thiago Mosqueiro (BioCircuits Institute, Universidad de California en San Diego)

Fecha: Lunes 28 de noviembre de 2016 a las 12:30 horas

Lugar: Sala de seminarios, Instituto Universitario de Investigación CIO, Edificio Torretamarit, Universidad Miguel Hernández (Campus de Elche)

Resumen:

The coding basis for decision making is often provided by a minimal number of higher-order neurons. Before reaching premotor decision layers, sensory information travels through several neural layers. This multi-layered organization is often composed of (i) divergent connectivities, which are essential for pattern recognition and stimulus codification, and (ii) convergent connectivities, which filter down information. However, this architecture based on multiple neuronal layers induces a time lag between peripheral input and adaptive behavior (output), which is inconsistent with the need for speed. Furthermore, an accentuated divergent-convergent architecture may also amplify noise and generate unstable dynamics, which impairs the sensory representation of external stimuli. In this talk, we discuss a recent feedback mechanism that presents robust gain-control, sustains sparse coding, and accelerates the information transfer through layers. An example of such synaptic organization is the early olfactory processing stage of all insects, the Mushroom Bodies (MBs), where a strong divergence from 2k to 300k neurons is followed by a convergence to only 400 neurons. The stability analysis of this system provided an analytical formula for the gain-control maintenance. In addition to in-vivo recordings, we used data from gas-sensor arrays to show that this architecture learns more complex spatio-temporal patterns. Moreover, we will use data from gas-sensor arrays to motivate pre-training of such network. Thus, because such connectivities are ubiquitous to many brains, we believe divergent-convergent networks play a central role in stable and fast decision-making processes in the brain.

Breve Bio:

Dr. Mosqueiro graduated in Physics (2008) and got his PhD in Physics (2015) from the University of São Paulo, with an internship (2014) at the BioCircuits Institute and Rady School of Management, both part of the University of California San Diego. His main areas of interest are statistical physics, mathematical modeling, computational neuroscience, and machine learning. Thiago is currently a Post Doc at the BioCircuits Institute, working in collaboration with the University of California Los Angeles and the Arizona State University, and a lecturer at the Rady School of Management.