Back to the present: how neurons deal with delays

Laurent U Perrinet, INT

Acknowledgements:

Jim Bednar @ Continuum Analytics & University of Edinburgh
Rick Adams and Karl Friston @ UCL - Wellcome Trust Centre for Neuroimaging
Wahiba Taouali, Giacomo Benvenuti, Frédéric Chavane - ANR BalaV1
Mina Aliakbari Khoei, Guillaume Masson and Anna Montagnini - FACETS-ITN Marie Curie Training

Workshop on Computational Neuroscience "New trends and challenges for 2030"
January 18, 2017
https://laurentperrinet.github.io/talk/2017-01-18-laconeu

(AUTHOR) Hi, I am Laurent Perrinet from (LOGO) the Institute de Neurosciences de la Timone in Marseille, a joint unit from the CNRS and AMU. Using computational models, I am investigating the link between the efficiency of behavioural responses in vision, their underlying neural code and their adaptation to the structure of the world.
(SHOW TITLE - THEME) = In particular, I will use today a visual illusion -the FLE- to provide evidence that we use predictive mechanism to compensate for neural delays and perceive the visual world in real-time (I will try to define today what I mean by that...)
(ACKNO) this endeavour involves different techniques, tools and... persons. From the head on, I wish to thank the people who collaborated to this project and in particular Rick Adams and Karl Friston and the Wellcome Trust Centre for Neuroimaging for providing the tools for a successful visit / but also Mina, Guillaume and Anna for the great oppportunity to link theories with psychophysical data; Wahiba, Giacomo and Frédéric for the challenging task of linking such models to the neural activity in NHP, and finally Jean-Bernard, Sohir and Laurent Madelain for their essential knowledge in adaptation and reinforcement.

* ... but before starting, I would like to say one word. in fact, it may not obvious to the general public that problems in artificial intelligence and more generally in understanding the brain is different from one thinks.

* this is perfectly illustrated in this cartoon from the famous XKCD author Randall Munroe: it is easy for an artificial intelligence to geolocalize itself anywhere in the world (or to browse a database of zillions of go games as for alpha go), but whenever it has to something apparently simple ("is there a bird?") computers are clueless

* so, present computers are a bad analogy to understand the brain and we look here at different solutions found in nature by natural selection - so thanks to the organizers for providing a forum for such endeavour - let's start by looking at anatomy:

The Behavioral receptive field

LP and Masson (2012) Bio Behav Reviews, https://laurentperrinet.github.io/publication/masson-12

Montagnini, LP and Masson (2015) in Biologically Inspired Computer Vision

... we have been busy in the last couple of years to make sense of this repertoire of behaviors, in particular in light with the architecture of the brain areas involved in producing these EMs, for instance for pursuit initiation, ...

the Left-side plot (extracted from this review paper) illustrates in primates a sketch of the hierarchy of neuronal population yielding to ocular following responses from the early visual areas (retina, thalamus, primary visual cortex), finessed by processing in motion specific information in downstream sensory areas (MT for speed or MST for global motion) to the brain stem and the oculomotor muscles which tranform this neural activity in a graded motor output. As such, moving stimuli covering the retina are processed by a cascade of neuronal populations, where information is processed through feedforward, lateral and feedback interactions.
in particular, we took advantage of a Bayesian, probabilistic framework to develop an unified theory, the "behavioral receptive field" which encompasses a large range of psychophysical experiments by modeling behavioral responses from population dynamics. Such cascade implements local, context-dependent extraction of motion information… I will speak more about that on Friday for those in the summer school
today, I would like to focus on a particular problem which will help us unravel the dynamics of decision making: oculomotor delays. Indeed, one challenge for modelling is to understand EMs using AI as a problem of optimal motor control under axonal delays. The central nervous system has to contend with axonal delays, both at the sensory and the motor levels. For instance, in the human visuo-oculomotor system, it takes approximately $ au_s=50~ms$ for the retinal image to reach the visual areas implicated in motion detection, and a further $ au_m=40~ms $ to reach the oculomotor muscles.

* ... As a consequence, for a tennis player ---here (highly trained) Jo-Wilfried Tsonga at Wimbledon--- trying to intercept a passing-shot ball at a (conservative) speed of $20~m.s^{-1}$, the position sensed on the retinal space corresponds to the instant when its image formed on the photoreceptors of the retina and reaches our hypothetical motion perception area behind:

Back to the present: how neurons deal with delays

Laurent U Perrinet, INT

Acknowledgements:

Workshop on Computational Neuroscience "New trends and challenges for 2030" January 18, 2017 https://laurentperrinet.github.io/talk/2017-01-18-laconeu

The Behavioral receptive field

Workshop on Computational Neuroscience "New trends and challenges for 2030"
January 18, 2017
https://laurentperrinet.github.io/talk/2017-01-18-laconeu