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

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

3. visual cortex motor cortex association cortex to motor output

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

5. Molecular basis action potential Ca 2+ Na + K+K+ -70mV Ions/proteins

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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. How fast is neuronal signal processing? animal -- no animal Simon Thorpe Nature, 1996 Visual processingMemory/associationOutput/movement eye Reaction time experiment

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

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