Lecture

Machine Learning Models for Neuroscience

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In course

PHYS-754: Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...

Description

This lecture delves into machine learning models for neuroscience, exploring how these models bridge limitations in brain function understanding by linking perception-action coupling at the circuit level. The instructor explains task-driven modeling, showcasing how models are created to understand why neurons fire in response to stimuli. The lecture also covers core object recognition, focusing on convolutional neural networks trained on ImageNet, which are the best models for the ventral pathway in humans and monkeys. The instructor demonstrates how these models can explain neural activity in the primate brain, particularly in the IT area. Additionally, the lecture discusses the application of machine learning in understanding proprioception, showcasing how candidate models are created and tested to explain direction-selective neurons in the brain.

Instructors (10)

Lenka Zdeborová

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.

Paolo De Los Rios

Paolo De Los Rios earned his master in Electronic Engineering at the Turin Institute of Technology (Politecnico di Torino) in May 1993. In November 1993 he moved to Trieste, Italy, to enter the PhD program in Theoretical Condensed Matter Theory at the International School for Advanced Studies (SISSA/ISAS) where he obtained the PhD degree in October 1996 for his work on the statistical physics of disordered systems. After a one year postdoc at the Max-Planck Institute for the Physics of Complex Systems in Dresden, Germany, in November 1997 he moved to the University of Fribourg, Switzerland, to join the group of Prof. Yi-Cheng Zhang. There he has worked on various applications of statistical physics to complex systems. In September 2000 he has been appointed Assistant Professor in Statistical Physics of Living Matter and Complex Systems at the Institute of Theoretical Physics of the University of Lausanne, Switzerland. Since April 2010 he is Associate Professor at the Institute of Theoretical Physics of the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.

Anne-Clémence Corminboeuf

Teaching & PhD PhD Students Liam Oliver Marsh, Pratham Pushparaj Deshmukh, Roel Christiaan M Raike, Thanapat Worakul, Osvaldo Hernandez Cuellar, Juliette Veronique Schleicher, Yannick Calvino Alonso, Sara Bassetta, Yuri Cho, Kelian Dietmar Marc Gaedecke, Guoyuan Liu Past EPFL PhD Students Fabrice Avaltroni, Daniel Filipe Jana, Stephan Niklaus Steinmann, Jérôme Florian Gonthier, Riccardo Petraglia, Antonio Prlj, Laurent Alexis Clément Vannay, Alberto Fabrizio, Kun-Han Lin, Boodsarin Sawatlon, Raimon Fabreg

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Related concepts (32)

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Neural circuit

A neural circuit (also known as a biological neural network BNNs) is a population of neurons interconnected by synapses to carry out a specific function when activated. Multiple neural circuits interconnect with one another to form large scale brain networks. Neural circuits have inspired the design of artificial neural networks, though there are significant differences. Early treatments of neural networks can be found in Herbert Spencer's Principles of Psychology, 3rd edition (1872), Theodor Meynert's Psychiatry (1884), William James' Principles of Psychology (1890), and Sigmund Freud's Project for a Scientific Psychology (composed 1895).

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Neural oscillation

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons.

Neural network

A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.

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