Abstract We start with a statistical formulation of Helmholtz’s ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organization and responses. Perceptual inference and learning Collège de France 2008
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Abstract We start with a statistical formulation of Helmholtz’s ideas about neural energy to furnish a model of perceptual inference and learning that.
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Abstract
We start with a statistical formulation of Helmholtz’s ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organization and responses.
Perceptual inference and learningCollège de France 2008
Inference and learning under the free energy principleHierarchical Bayesian inference
A simple experiment
Bird songs (inference)Structural and dynamic priorsPrediction and omissionPerceptual categorisation
Suppression of inferotemporal responses to repeated faces
Main effect of faces
Henson et al 2000
Repetition suppression and the MMN
The MMN is an enhanced negativity seen in response to any change (deviant) compared to the standard response.
10 20 30 40 50 60-5
0
5
10
15
20
25
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35prediction and error
time10 20 30 40 50 60
-2
-1
0
1
2hidden states
time
10 20 30 40 50 60-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5causes - level 2
time Time (sec)
Freq
uenc
y (H
z)
0.1 0.2 0.3 0.42000
2500
3000
3500
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5000
Prediction errorencoded by superficial
pyramidal cells
A simple chirp
0.1 0.2 0.3 0.4-4
-2
0
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4Hidden states
Time (sec)
Freq
uenc
y (H
z)
percept
0.2 0.42000
3000
4000
5000
100 200 300 400 500
-5
0
5
10
peristimulus time (ms)
LFP
(micr
o-vo
lts)
prediction error
0.1 0.2 0.3 0.4-4
-2
0
2
4
Time (sec)
Freq
uenc
y (H
z)
0.2 0.42000
4000
100 200 300 400 500
-5
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10
peristimulus time (ms)
LFP
(micr
o-vo
lts)
0.1 0.2 0.3 0.4-4
-2
0
2
4
Time (sec)
Freq
uenc
y (H
z)
0.2 0.42000
4000
100 200 300 400 500
-5
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10
peristimulus time (ms)
LFP
(micr
o-vo
lts)
0.1 0.2 0.3 0.4-4
-2
0
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4
Time (sec)Fr
eque
ncy
(Hz)
0.2 0.42000
4000
100 200 300 400 500
-5
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10
peristimulus time (ms)
LFP
(micr
o-vo
lts)
0.1 0.2 0.3 0.4-4
-2
0
2
4
Time (sec)
Freq
uenc
y (H
z)
0.2 0.42000
4000
100 200 300 400 500
-5
0
5
10
peristimulus time (ms)
LFP
(micr
o-vo
lts)
0.1 0.2 0.3 0.4-4
-2
0
2
4
Time (sec)
Freq
uenc
y (H
z)
0.2 0.42000
4000
100 200 300 400 500
-5
0
5
10
peristimulus time (ms)
LFP
(micr
o-vo
lts)
min F
Perceptual inference: suppressing error over peristimulus time
Perceptual learning: suppression over repetitions
Simulating ERPs to
repeated chirps
minu F
1 2 3 4 5 60
100
200
300
400
500
600
700SSQ prediction error
repetition
0 100 200 300 400 500-8
-6
-4
-2
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8LFP: Oddball
peristimulus time (ms)0 100 200 300 400 500
-8
-6
-4
-2
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8LFP: Standard (P1)
peristimulus time (ms)
0 100 200 300 400 500-8
-6
-4
-2
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8Difference waveform (MMN)
peristimulus time (ms)
primary area - amplitudeprimary area - frequencysecondary area - chirp
The MMN
Enhanced N1 (primary area) MMN (secondary area)
Last presentation(after learning)
First presentation(before learning)
P300 (tertiary area)?
Summary
A free energy principle can account for several aspects of action and perception
The architecture of cortical systems speak to hierarchical generative models
Estimation of hierarchical dynamic models corresponds to a generalised deconvolution of inputs to disclose their causes
This deconvolution can be implemented in a neuronally plausible fashion by constructing a dynamic system that self-organises when exposed to inputs to suppress its free energy
Minimisation of free energy proceeds over many spaces, including the state of a model (perception), its parameters (learning), its hyperparameters (salience and attention) and the model itself (selection in somatic or evolutionary time).