it is a pleasure to announce our next speaker for the onLINe seminar this Friday, 14th May, at 2 pm CET. Dimitris Pinotsis from the Centre for Mathematical Neuroscience and Psychology at University of London - City.
Please contact us for the Zoom link (Max-Philipp.Stenner@lin-magdeburg.de)
See you there,
your onLINe organizers
Brain theory, pathology and consumer behavior
Dimitris Pinotsis (www.pinotsislab.com)
Associate Professor, Centre for Mathematical Neuroscience and Psychology Department of Psychology , University of London — City
Research Affiliate, The Picower Institute for Learning and MemoryDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT)
Predictive Coding (PC) is a theory of brain function based on ideas from Bayesian inference and machine learning. It suggests that the brain tries to understand the world by generating predictions about the information that should be present in the world. The brain searches for these predictions. In this context, brain activity represents the brain’s predictions and the discrepancies between them and the information the brain receives, called prediction errors. Then, understanding the world amounts to minimizing prediction errors.
Despite its successes, whether Predictive Coding is implemented by the brain is an open question. I will discuss tests that assess evidence in support of PC in brain data. I will use data from animal and human studies and different brain imaging modalities and tasks -- to show that prediction error representations were found in superficial layers. Also, that representations of expectations in schizophrenics were different from normal people and that the brain seemed to reliably encode probabilities of sensory signals.
Then, I will also discuss new mathematical tools that allow one to perform new tests of PC:
1) Tests bridging scale: hypotheses about the micro scale by analysing non-invasive human M/EEG data obtained at the macro scale. As an illustration, I will show how to test one of the tenets of PC; that deep cortical layer activity represents predictions. This uses mathematical tools from statistical decision theory.
2) Tests about neuronal connections that carry PC signals. As an illustration, I will show that a well-known behavioral effect in psychophysics, known as the oblique effect, can be explained in terms of sparser microscopic connectivity and metabolic efficiency. This uses a combination of mathematical tools from machine learning and dynamical systems.
Time permitting, I will finish with some recent work that opens up exciting new questions by applying PC in consumer neuroscience.