Lecture

In Silico Neuroscience: Modeling the Brain Complexity

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

BIOENG-450: In silico neuroscience

"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies. ...

Description

This lecture delves into the necessity of in silico neuroscience alongside in vivo and in vitro studies to comprehend the brain's complexity. Discussions revolve around the advantages of computer simulations in bridging scales, reducing costs, and enhancing experimental data value. The debate touches on the ethical considerations of animal experimentation, emphasizing the importance of continued research for brain understanding and disease treatment. The lecture also explores the distinctions between biophysical and phenomenological models, highlighting the significance of both approaches in unraveling neural mechanisms and network behaviors.

Instructors (2)

Felix Schürmann

Felix Schürmann is co-director of the Blue Brain Project and involved in several research challenges of the European Human Brain Project. He studied physics at the University of Heidelberg, Germany, supported by the German National Academic Foundation. Later, as a Fulbright Scholar, he obtained his Master's degree (M.S.) in Physics from the State University of New York, Buffalo, USA, under the supervision of Richard Gonsalves. During these studies, he became curious about the role of different computing substrates and dedicated his master thesis to the simulation of quantum computing. He studied for his Ph.D. at the University of Heidelberg, Germany, under the supervision of Karlheinz Meier. For his thesis he co-designed an efficient implementation of a neural network in hardware.

Armando Romani

Official source

Related concepts (18)

Brain simulation

Brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and diagnosing brain diseases. Various simulations from around the world have been fully or partially released as open source software, such as C. elegans, and the Blue Brain Project Showcase.

Computational neuroscience

Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous.

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.

Network theory

In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the nodes or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components. Network theory has applications in many disciplines, including statistical physics, particle physics, computer science, electrical engineering, biology, archaeology, linguistics, economics, finance, operations research, climatology, ecology, public health, sociology, psychology, and neuroscience.

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.

Related lectures (35)

Attractor Networks and Spiking Neurons MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Cell type specific connectivity and shotgun connections

Explores cell type specific connectivity and shotgun connectomics in mapping neural connections at different scales.

Modeling Neuronal Activity

Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.

In Silico Neuroscience: Ion Channels and Neuronal Models