Aucun titre de diapositive

1
Introduction Neurons and the Problem of Neural
Coding
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
3
motor cortex
association cortex
visual cortex
to motor output
4
10 000 neurons 3 km wires
1mm
5
-70mV
Na
K
Ca2
Ions/proteins
6
Modeling of Biological neural networks
populations of neurons
behavior
neurons
signals
computational model
molecules
ion channels
Swiss Federal Institute of Technology Lausanne,
EPFL
Laboratory of Computational
Neuroscience, LCN, CH 1015 Lausanne
7
Background What is brain-style computation?
Brain
Computer
8
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
Elements of Neuronal Dynamics
Book Spiking Neuron Models
Chapter 1.2
11
Phenomenology of spike generation
threshold -gt 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
Laboratory of Computational
Neuroscience, LCN, CH 1015 Lausanne
13
A simple phenomenological neuron model
Book Spiking Neuron Models
Chapter 1.3
electrode
14
Introduction
Course (Biological Modeling of Neural Networks)
Chapter 1.1
A first phenomenological model
electrode
15
Spike Response Model
Spike emission
i
Spike emission AP
All spikes, all neurons
Last spike of i
linear
threshold
16
Integrate-and-fire Model
Spike emission
i
reset
I
linear
Firereset
threshold
17
The Problem of Neuronal Coding
Book Spiking Neuron Models
Chapter 1.4
18
The Problem of Neuronal Coding
Book Spiking Neuron Models
Chapter 1.4
nsp
T
stim
Rate
Rate defined as temporal average
19
The Problem of Neuronal Coding
Rate defined as average over stimulus
repetitions Peri-Stimulus Time Histogram
t
20
The problem of neural coding population
activity - rate defined by population average
t
population dynamics?
21
The problem of neural coding temporal codes
Time to first spike after input
correlations
Phase with respect to oscillation
22
Reverse Correlations
I(t)
fluctuating input
23
Reverse-Correlation Experiments
(simulations)
after 1000 spikes
after 25000 spikes
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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
How fast is neuronal signal processing?
Simon Thorpe Nature, 1996
animal -- no animal
Reaction time experiment
Visual processing
Memory/association
Output/movement
28
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29
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
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