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Introduction: Neurons and the Problem of Neural Coding

Swiss Federal Institute of Technology Lausanne, EPFL. Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne. Introduction: Neurons and the Problem of Neural Coding. BOOK: Spiking Neuron Models, W. Gerstner and W. Kistler Cambridge University Press, 2002

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Introduction: Neurons and the Problem of Neural Coding

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  1. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne Introduction: Neurons and the Problem of Neural Coding BOOK: Spiking Neuron Models, W. Gerstner and W. Kistler Cambridge University Press, 2002 Chapter 1

  2. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne Background: Neurons and Synapses Book: Spiking Neuron Models Chapter 1.1

  3. motor cortex association cortex visual cortex to motor output

  4. Signal: action potential (spike) action potential 10 000 neurons 3 km wires 1mm

  5. Molecular basis action potential -70mV Na+ K+ Ca2+ Ions/proteins

  6. predict Modeling of Biological neural networks populations of neurons behavior neurons signals computational model molecules ion channels Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne

  7. Background: What is brain-style computation? Brain Computer

  8. memory CPU input 10 (10 proc. Elements/neurons) Systems for computing and information processing Brain Computer Distributed architecture Von Neumann architecture 1 CPU 10 (10 transistors) No separation of processing and memory

  9. Systems for computing and information processing Brain Computer Tasks: Mathematical fast slow Real world fast slow E.g. complex scenes

  10. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne Elements of Neuronal Dynamics Book: Spiking Neuron Models Chapter 1.2

  11. j Spike reception: EPSP, summation of EPSPs Spike reception: EPSP Phenomenology of spike generation threshold -> Spike i Threshold Spike emission (Action potential)

  12. Modeling of Biological neural networks populations of neurons spiking neuron model behavior neurons signals computational model molecules ion channels Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne

  13. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne A simple phenomenological neuron model Book: Spiking Neuron Models Chapter 1.3 electrode

  14. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne Introduction Course (Biological Modeling of Neural Networks) Chapter 1.1 A first phenomenological model electrode

  15. j Spike reception: EPSP Spike reception: EPSP Firing: Spike Response Model Spike emission i Spike emission: AP All spikes, all neurons Last spike of i linear threshold

  16. j Spike reception: EPSP Integrate-and-fire Model Spike emission i reset I linear Fire+reset threshold

  17. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne The Problem of Neuronal Coding Book: Spiking Neuron Models Chapter 1.4

  18. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne The Problem of Neuronal Coding Book: Spiking Neuron Models Chapter 1.4 nsp T stim Rate Rate defined as temporal average

  19. PSTH(t) K=500 trials Stim(t) Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne The Problem of Neuronal Coding Rate defined as average over stimulus repetitions Peri-Stimulus Time Histogram t

  20. ? I(t) t population activity The problem of neural coding: population activity - rate defined by population average t population dynamics?

  21. t The problem of neural coding: temporal codes Time to first spike after input correlations Phase with respect to oscillation

  22. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne Reverse Correlations I(t) fluctuating input

  23. Reverse-Correlation Experiments (simulations) after 1000 spikes after 25000 spikes

  24. Swiss Federal Institute of Technology Lausanne, EPFL Laboratoryof Computational Neuroscience, LCN, CH 1015 Lausanne Stimulus Reconstruction I(t) fluctuating input

  25. Stimulus Reconstruction Bialek et al

  26. The problem of neural coding: What is the code used by neurons? Constraints from reaction time experiments

  27. eye How fast is neuronal signal processing? Simon Thorpe Nature, 1996 animal -- no animal Reaction time experiment Visual processing Memory/association Output/movement

  28. eye How fast is neuronal signal processing? Simon Thorpe Nature, 1996 animal -- no animal # of images Reaction time Reaction time 400 ms Visual processing Memory/association Output/movement Recognition time 150ms

  29. If we want to avoid prior assumptions about neural coding, we need to model neurons on the level of action potentials: spiking neuron models

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