Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Neurobiological Signal Processing

In course

COM-500: Statistical signal and data processing through applications

Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numeri ...

Description

This lecture covers the processing of neurobiological signals, focusing on modeling neurobiological spikes, classification of samples, de-noising signals, and data characterization using principal component analysis. The instructor explains the challenges in understanding brain function, experiments on animals, and research on stimulus-response relations. Various tools and methods for signal processing are discussed, such as the Poisson process, Markov chains, and mixture models. The lecture also delves into the use of electrodes to collect brain responses in insects and the importance of spike sorting. Additionally, the session explores the Poisson distribution, EM for Gaussian distributions, and numerical exercises for data classification.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Andrea Ridolfi

I am a professor of Signal Processing and Communication Technologies at Bern University of Applied Sciences. Since 2004 I hold a lecturer position at EPFL, teaching “Mathematical Principles of Signal Processing” (Doctoral School, 2004 – 2011), “Statistical Signal and Data Processing through Applications” (Master Program, (2004 – ongoing), and Signal Processing and Machine Learning for Digital Humanities (Master, 2017 – 2019, co-taught with Mathieu Salzmann). Previously, I have been working as Project Manager and R&D Engineer at EPFL (2011-2014), coordinating the LCAV activities within the NSF – Nanotera project Opensense, and as Project Manager and R&D Engineer with the biomedical signal processing group at CSEM (2006-2011).

Official source

Related concepts (32)

Markov chain

A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs now." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC).

Insect

Insects (from Latin insectum) are pancrustacean hexapod invertebrates of the class Insecta. They are the largest group within the arthropod phylum. Insects have a chitinous exoskeleton, a three-part body (head, thorax and abdomen), three pairs of jointed legs, compound eyes and one pair of antennae. Their blood is not totally contained in vessels; some circulates in an open cavity known as the haemocoel. Insects are the most diverse group of animals; they include more than a million described species and represent more than half of all known living organisms.

Lateralization of brain function

The lateralization of brain function (or hemispheric dominance/ latralisation ) is the tendency for some neural functions or cognitive processes to be specialized to one side of the brain or the other. The median longitudinal fissure separates the human brain into two distinct cerebral hemispheres, connected by the corpus callosum. Although the macrostructure of the two hemispheres appears to be almost identical, different composition of neuronal networks allows for specialized function that is different in each hemisphere.

Signal processing

Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, , potential fields, seismic signals, altimetry processing, and scientific measurements. Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, subjective video quality and to also detect or pinpoint components of interest in a measured signal. According to Alan V. Oppenheim and Ronald W.

Brain

A brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. It is located in the head, usually close to the sensory organs for senses such as vision. It is the most complex organ in a vertebrate's body. In a human, the cerebral cortex contains approximately 14–16 billion neurons, and the estimated number of neurons in the cerebellum is 55–70 billion. Each neuron is connected by synapses to several thousand other neurons.

Related lectures (52)

Neural Signals and Signal Processing: Hemodynamic Imaging

Explores hemodynamic imaging techniques to measure brain activity and understand brain structure and function.

Neural Signals and Signal Processing

Explores neuronal signals, brain organization, measurement techniques, and MRI principles.

Functional Brain Imaging: Signal Processing

Covers neuroimaging techniques, brain structure, MRI basics, and big data in brain research.

Optimization of Neuroprosthetic Systems

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.