Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience,...
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ntroduction: Neurons and the Problem of Neural Codin aboratory of Computational Neuroscience, LCN, CH 1015 Lausann Swiss Federal Institute of Technology Lausanne, EPFL BOOK: Spiking Neuron Models, W. Gerstner and W. Kistler Cambridge University Press, 2002 Chapter 1
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Transcript of Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience,...
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
Background: Neurons and SynapsesBook: Spiking Neuron Models Chapter 1.1
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
visual cortex
motor cortex
association cortex
to motoroutput
10 000 neurons3 km wires
1mm
Signal:action potential (spike)
action potential
Molecular basis
action potential
Ca2+
Na+
K+
-70mV
Ions/proteins
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
populations of neurons
computationalmodel
Swiss Federal Institute of Technology Lausanne, EPFL
neurons
moleculesion channels
behavior
signals
Modeling of Biological neural networks
predict
Background: What is brain-style computation?
Brain Computer
Systems for computing and information processing
Brain Computer
CPU
memory
input
Von Neumann architecture
(10 transistors)1 CPU
Distributed architecture10
(10 proc. Elements/neurons)No separation of processing and memory
10
Systems for computing and information processing
Brain Computer
Tasks:Mathematical
Real worldE.g. complex scenes
slow
slow
fast
fast
5
7cos5
Elements of Neuronal DynamicsBook: Spiking Neuron Models Chapter 1.2
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
Phenomenology of spike generation
iuij
Spike reception: EPSP, summation of EPSPs
Spike reception: EPSP
Threshold Spike emission (Action potential)
threshold -> Spike
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
populations of neurons
computationalmodel
Swiss Federal Institute of Technology Lausanne, EPFL
neurons
moleculesion channels
behavior
signals
Modeling of Biological neural networks
spiking neuron model
A simple phenomenological neuron modelBook: Spiking Neuron Models Chapter 1.3
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
electrode
IntroductionCourse (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
Spike Response Model
iuij
fjtt
Spike reception: EPSP
fjtt
Spike reception: EPSP
^itt
^itt
Spike emission: AP
fjtt ^
itt tui j f
ijw
tui Firing: tti ^
linear
threshold
Spike emission
Last spike of i All spikes, all neurons
Integrate-and-fire Model
iui
fjtt
Spike reception: EPSP
)(tRIuudt
dii
tui Fire+reset
linear
threshold
Spike emission
resetI
j
The Problem of Neuronal CodingBook: Spiking Neuron Models Chapter 1.4
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
The Problem of Neuronal CodingBook: Spiking Neuron Models Chapter 1.4
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
stimT
nsp
RateT
Tttnsp );(
Rate defined as temporal average
The Problem of Neuronal CodingRate defined as average over stimulus repetitionsPeri-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
tK
tttntPSTH
);(
)(
The problem of neural coding:population activity - rate defined by population average
I(t)
?
population dynamics? t
t
tN
tttntA
);(
)(populationactivity
The problem of neural coding:temporal codes
t
Time to first spike after input
Phase with respect to oscillation
correlations
Reverse Correlations
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
fluctuating input
I(t)
Reverse-Correlation Experiments (simulations)
after 1000 spikes
)(tI
after 25000 spikes
Stimulus Reconstruction
Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne
Swiss Federal Institute of Technology Lausanne, EPFL
fluctuating input
I(t)
Stimulus Reconstruction
Bialek et al
The problem of neural coding:What is the code used by neurons?
Constraints from reaction time experiments
How fast is neuronal signal processing?
animal -- no animalSimon ThorpeNature, 1996
Visual processing Memory/association Output/movement
eye
Reaction time experiment
How fast is neuronal signal processing?
animal -- no animalSimon ThorpeNature, 1996
Reaction time
Reaction time
# ofimages
400 msVisual processing Memory/association Output/movement
Recognition time 150ms
eye
If we want to avoid priorassumptions about neural coding,we need to model neuronson the level of action potentials:
spiking neuron models
