Talks & Presentations

  1. How to sample a reliable neural estimate of the variable world? 18.01.2015. Computational Neuroscience; National Key Laboratory of Cognitive Neuroscience and Learning. Beijing Normal University (BNU), Beijing, China
  2. How to sample a reliable neural estimate of the variable world? 24.02.2015. Complex, University College London, UK
  3. Bursts Drive Maximal Visual Encoding. 20.04.2015. JFRC Conference: Insect Vision, Cells, Computation, and Behavior. Howard Hughes Medical Research Institute, Janelia Farm, USA
  4. How to sample a reliable neural estimate of the varaible world? 30.06.2015. High-End Foreign Expert Talk. National Key Laboratory of Cognitive Neuroscience and Learning. Beijing Normal University (BNU), Beijing, China
  5. How to sample a reliable neural estimate of the variable world? 16-22.07.2015. Information Processing in Sensory Systems. Organisation for Computational Neurosciences (CNS), Prague, Czech Republic
  6. How to sample a reliable neural estimate of the variable world? 06.11.2015. Viikki Biocenter, University of Helsinki, Finland
  7. How to sample a reliable neural estimate of the variable world? 10.07.2016. FASEB, Keystone, Colorado, USA
  8. How to sample a reliable neural estimate of the variable world? 04.10.2016. Champalimaud Center for Unknown, Lisbon, Portugal
  9. How to sample a reliable neural estimate of the variable world? 10.10.2016. Adrian Seminar, University of Cambridge
  10. How to sample a reliable neural estimate of the variable world? 02.11.2016. University of Oulu, Finland

How to sample a reliable neural estimate of the variable world?
Mikko Juusola
24/02/2015

The world is variable and dynamic. Its matter/energy is clustered into changing structures and events encompassing macro- and micro-scales. The central problem facing all animals is how to best sample a reliable estimate of the world, when the estimation itself is limited by variations in their neural machineries and by uncertainty of their surroundings. New results suggest that rather than working against variability, evolution works with it, giving rise to reliable and robust information sampling and representation in the nervous tissue. Photoreceptors sample visual information stochastically and weight it against fluctuating responses of their neighbours. Such anti-aliased sampling improves neural estimates of intensity changes in image pixels. Visual interneurones further adaptively sample and integrate synaptic information of photoreceptors to improve their estimates of the structure of the world. I will present new evidence for the hypothesis that variability in animals’ sensory systems is less noise and more a part of a solution to sample reliable estimates of the variable world.

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