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This is presentation in the nanosymposium of the Society for Neuro Science, in 2013 at San Diego
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Hiroshi Yamakawa
FUJITSU LABORATORIES LTD.JAPAN
Brain-inspired equivalence structure (ES) extraction technique for generating frames
Outline
1. Human-level intelligence can explore from neocortex learning.
Artificial intelligence (AI) lacks flexible sampling function of neocortex Equivalence structure (ES) extraction is key for such a function
2. Use local sequences to extract equivalence structures (ESs). Inspired by theta phase precession of hippocampal formation
3. Simple simulation of ES extraction
4. Summary Frame generation:
Promising way to achieve artificial general intelligence (AGI)
2
HLDL mostly means human-level artificial intelligence (HLAI)
3
Complete intelligenceDeep learning (DL) High-performance unsupervised machine learning technology corresponding to neocortex
Creativity
Intuition
General intelligence
Artificialintelligence
Humanintelligence
D
Emotion(Amygdala)
Reinforcement learning
(Basal ganglia)
Control theory(Cerebellum) Efficient arithmetic
operation and logic inference
Retrieval from big
data
Pattern recognition
Deep learning(DL)
HLDL (Neocortex + Hippocampus)
…because untrodden machine intelligences D
are concentrated on neocortex.
Human-level DL (HLDL) Fully simulate neocortex computing and its learning functions. (with help of hippocampus).
Feasible intelligence (with limited resources)
What is the problem in achieving HLDL?
Convolution layer : • Well-developed for
machine learning: Simple cell, Auto encoder
network, SOM, Boltzmann machine, Info-MAX, Manifold learning, ...
Eye
Visualcortex
Deep learning lacks flexible sampling
4
CaudateQuick generation of best next-move
V1
V2
V3
MTG/V6
Sampling/Pooling layer: • Human encode structure of
hierarchical retinotopy:→ Complex cell, Max-
pooling, ...• Supports visual invariances → Need flexible sampling
Example: Intuitive “decision making” for chess-like game
Chess-like game
Sampling
Convolution
Sampling
Convolution
Sampling
Convolution
Sampling
Convolution
High-level featuresSupports intuitive decision making - Cannot be explained by experts - Cannot be acquired by deep learning
Hippocampussupportlearning
PrecuneusPerception of board patternHippocampus
Support learning of neocortex
(Wan, Science 2011)
5
C
AB
DEFZ
XY
Time
1 2 3 4 5 6 7
Time
1 2 3 4 5 6 7
Subspace Subspace
Combinedframe
Equivalence structures for flexible sampling
Time: t
Var
iabl
e se
t:
x
D
FE
G
A
H1 2 3 4 5 6 7
BC
Original frame
InvarianceIncreased events enhance deductive inference.
Equivalence structure (ES) …indicates portions of subspace that could be regarded as equivalent.
Invariance in basic image processing Invariance for face recognition
D
FE
G
A
H
Input sequence
1 2 3 4 5 6 7
BC
Need more flexibility for higher-level
sampling
Outline
1. Human-level intelligence can explore from neocortex learning.
Artificial intelligence (AI) lacks flexible sampling function of neocortex Equivalence structure (ES) extraction is key for such a function
2. Use local sequences to extract equivalence structures (ESs). Inspired by theta phase precession of hippocampal formation
3. Simple simulation of ES extraction
4. Summary Frame generation:
Promising way to achieve artificial general intelligence (AGI)
6
7
Static patterns are poor for ES extraction
If using common static binary patterns
as similarity to compare subspaces,
Needs similarity with rich variation.
C
AB
DEFZ
XY
ESs Subspace of d variables
Combined frame
Time: t
Set
of N
var
iabl
es
D
FE
G
A
H
Input sequence
1 2 3 4 5 6 7
BC
Original frame
Too many other
subspaces
…this could exist in
neocortex.
Problem:
Variation in static
patterns 2d is not
enough to categorize
thousands of
subspaces NCd(~Nd).
8
Subspaces can be compared using local sequences
Theta phase precessionSeveral sequential events are packed in each phase (~5 Hz)
( Sato and Yamaguchi : Neural Computation 2003)
Inspired by information representation in
hippocampus.
Local sequences are used to compare subspaces.
(Skipping a detailed explanation.)
C
AB
DEFZ
XY
Time
1 2 3 4 5 6 7
Time
1 2 3 4 5 6 7Combined
frame
Time: t
D
FE
G
A
H1 2 3 4 5 6 7
BC
Original frame
D
FE
G
A
H
Input sequence
1 2 3 4 5 6 7
BC
ESs Subspace of d variables
Set
of N
var
iabl
es
Outline
1. Human-level intelligence can explore from neocortex learning.
Artificial intelligence (AI) lacks flexible sampling function of neocortex Equivalence structure (ES) extraction is key for such a function
2. Use local sequences to extract equivalence structures (ESs). Inspired by theta phase precession of hippocampal formation
3. Simple simulation of ES extraction
4. Summary Frame generation:
Promising way to achieve artificial general intelligence (AGI)
9
10
Simple simulation to validate this idea
dim. ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
4 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
5 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0
6 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0
7 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0
Time
A swinging dot image in sequence of one-dimensional spaces,representing an idealized video image of natural scenes (up to 300 frames)
D
F
E
G
A
H
B
C
Input image for experiment: Dot wave sequence
Expected ES: A cluster of adjacent variable sets
300
Cluster of 12 subspaces, each of which consisting of 3 adjacent variables, is expected to be extracted depending on spatial continuity of input sequence.
C
A
B
E
F
G Z
X
Y
D
B
C
E
C
D
F
G
H
D
E
F
C
A
B
E
F
GD
B
C
E
C
D
F
G
H
D
E
F
Combined frame Cluster of subspaces
11
All
perm
utat
ion
of s
ubsp
ace
s (
366
patt
erns
)
Index of local sequences (Only non-zero elements shown)
Num
bers
of l
ocal
seq
uenc
es
Expected ES containing adjacent variable set is extracted as cluster from a numbers of local sequences clustering.
Combined frame
X Y Z
E D CB C DG F ED E FF E DE F GD C BC D EC B AA B CH G FF G H
Result: Expected ES is extracted as a cluster
Su
bsp
aces
Outline
1. Human-level intelligence can explore from neocortex learning.
Artificial intelligence (AI) lacks flexible sampling function of neocortex Equivalence structure (ES) extraction is key for such a function
2. Use local sequences to extract equivalence structures (ESs). Inspired by theta phase precession of hippocampal formation
3. Simple simulation of ES extraction
4. Summary Frame generation:
Promising way to achieve artificial general intelligence (AGI)
12
Summary
Untrodden machine intelligent functions are concentrated on neocortex, so emergence of HLDL mostly means emergence of HLAI.
Learning of sampling layer is minimally needed to generate high-level features for HLDL. This learning is assumed to be equivalence structure extraction.
13
(Buzsaki, 2007)
Where is responsible sub-region for ES extraction in theta loop of
hippocampal formation?
Inspired by theta phase precession, I introduced “numbers of local sequences” for each subspace. Clustering of subspaces by these frequencies enabled extraction of ESs in a simple demonstration.
I'd like to specify the sub-region of the hippocampal formation within theta loopsthat perform ES extraction.
Future work includes constructing a neocortex-hippocampus model implementing ES extraction.
Human-level general AI needs ability to generate frames.
14
123456
EA B C
Eve
nts
VariablesD
Values
General intelligence systems should be able to learn to solve problems that were unknown at time of their creation.
NeuronColumn
Combined new frame
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Ⅴ
Ⅵ
Equivalence structure
(ES)
Obviously, human brain can generate new frames to solve various new problems using learning ability of neocortex.
Designing HLDL by referring to neocortex is a promising approach.
frame