Brain-inspired
equivalence structure (ES)
extraction technique
for generating frames
Hiroshi Yamakawa
FUJITSU LABORATORIES...
Outline
1. Human-level intelligence can explore from neocortex
learning.



Artificial intelligence (AI) lacks flexible ...
HLDL mostly means human-level artificial intelligence (HLAI)

…because untrodden machine intelligences D
are concentrated ...
Deep learning lacks flexible sampling
Example: Intuitive “decision making” for chess-like game
High-level features

Precun...
Equivalence structures for flexible sampling
Equivalence structure (ES)
…indicates portions of subspace that could be rega...
Outline
1. Human-level intelligence can explore from neocortex
learning.



Artificial intelligence (AI) lacks flexible ...
Static patterns are poor for ES extraction
…this could
exist in
neocortex.

If using common static binary patterns
as simi...
Subspaces can be compared using local sequences
Theta phase precession
Several sequential events are
packed in each phase ...
Outline
1. Human-level intelligence can explore from neocortex learning.



Artificial intelligence (AI) lacks flexible ...
Simple simulation to validate this idea
 Input image for experiment: Dot wave sequence
A swinging dot image in sequence o...
Result: Expected ES is extracted as a cluster

Subspaces

All permutation of subspaces (366 patterns)

Expected ES contain...
Outline
1. Human-level intelligence can explore from neocortex
learning.



Artificial intelligence (AI) lacks flexible ...
Summary
 Untrodden machine intelligent functions are concentrated on
neocortex, so emergence of HLDL mostly means emergen...
Human-level general AI needs ability to generate frames.
General intelligence systems should be able to learn to solve
pro...
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Brain-inspired equivalence structure extraction technique for generating frames

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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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Brain-inspired equivalence structure extraction technique for generating frames

  1. 1. Brain-inspired equivalence structure (ES) extraction technique for generating frames Hiroshi Yamakawa FUJITSU LABORATORIES LTD. JAPAN
  2. 2. 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
  3. 3. HLDL mostly means human-level artificial intelligence (HLAI) …because untrodden machine intelligences D are concentrated on neocortex. Complete intelligence Deep learning (DL) High-performance unsupervised machine learning technology corresponding to neocortex Feasible intelligence (with limited resources) Human intelligence Artificial intelligence Control theory (Cerebellum) D Efficient arithmetic operation and logic inference Deep C r e a t i v i t ylearning Human-level DL (HLDL) Fully simulate neocortex computing and its learning functions. (with help of hippocampus). (DL) I n t u i t P a tn t e r n i o r e c o g n i t i o n Ge n e r a l i n t e l l i g e n c e Retrieval from big data Reinforcement learning Emotion (Basal ganglia) (Amygdala) What is the problem in achieving HLDL?
  4. 4. Deep learning lacks flexible sampling Example: Intuitive “decision making” for chess-like game High-level features Precuneus Caudate Quick generation of best next-move Hippocampus Perception of Supports intuitive decision making board pattern - Cannot be explained by experts Support learning of neocortex - Cannot be acquired by deep learning Eye support learning Visual cortex Convolution Sampling V3 Convolution Sampling V2 Hippocampus MTG /V6 Convolution Sampling Convolution layer: • Well-developed for machine learning: Simple cell, Auto encoder network, SOM, Boltzmann machine, Info-MAX, Manifold learning, ... Sampling/Pooling layer: • Human encode structure of hierarchical retinotopy: → Complex cell, Max-pooling, ... Chess-like game (Wan, V1 Convolution Sampling Science 2011) 4 • Supports visual invariances → Need flexible sampling
  5. 5. Equivalence structures for flexible sampling Equivalence structure (ES) …indicates portions of subspace that could be regarded as equivalent. Variable set: x Original frame A B C D E F G H Subspace Subspace Input sequence X Y Z A B C D E F 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Time Time Combined 1 2 3 4 5 6 7 frame Invariance Increased events enhance deductive inference. Time: t Invariance in basic image processing Invariance for face recognition Need more flexibility for higher-level sampling 5
  6. 6. 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. 7. Static patterns are poor for ES extraction …this could exist in neocortex. If using common static binary patterns as similarity to compare subspaces, Input sequence Set of N variables Problem: Variation in static patterns 2d is not enough to categorize thousands of subspaces NCd(~Nd). A B C D E F G H ESs X Y Z 1 2 3 4 5 6 7 Original frame Time: t Subspace of d variables A B C Combined frame D E F Too many other subspaces Needs similarity with rich variation. 7
  8. 8. Subspaces can be compared using local sequences Theta phase precession Several sequential events are packed in each phase (~5 Hz) Inspired by information representation in hippocampus. ( Sato and Yamaguchi : Neural Computation 2003) Subspace of d variables Set of N variables ESs Input sequence A B C D E F G H X Y Z D E F 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Time Time Combined frame 1 2 3 4 5 6 7 Original frame A B C Time: t Local sequences are used to compare subspaces. (Skipping a detailed explanation.) 8
  9. 9. 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. 10. Simple simulation to validate this idea  Input image for experiment: Dot wave sequence A swinging dot image in sequence of one-dimensional spaces, representing an idealized video image of natural scenes (up to 300 frames) Ti e m di .I m D 1 A 1 B 2 C 3 D 4 E 5 F 6 G 7 H 8 0 0 0 1 0 0 0 0 2 0 0 1 0 0 0 0 0 3 0 0 0 1 0 0 0 0 4 0 0 1 0 0 0 0 0 5 0 0 0 1 0 0 0 0 6 0 0 0 0 1 0 0 0 7 0 0 0 0 0 1 0 0 8 0 0 0 0 0 0 1 0 9 0 0 0 0 0 0 0 1 10 11 12 13 14 15 16 17 18 19 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 300  Expected ES: A cluster of adjacent variable sets Cluster of 12 subspaces, each of which consisting of 3 adjacent variables, is expected to be extracted depending on spatial continuity of input sequence. A B C D E F C D E F G H X B C D E F G B C D E F G Y C D E F G H A B C D E F Z Cluster of subspaces 10 Combined frame
  11. 11. Result: Expected ES is extracted as a cluster Subspaces All permutation of subspaces (366 patterns) Expected ES containing adjacent variable set is extracted as cluster from a numbers of local sequences clustering. Index of local sequences (Only non-zero elements shown) 11 Combined frame X Y Z E D C B C D G F E D E F F E D E F G D C B C D E C B A A B C H G F F G H
  12. 12. 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
  13. 13. 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.  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 loops that perform ES extraction.  Future work includes constructing a neocortex-hippocampus model implementing ES extraction. 13 Where is responsible sub-region for ES extraction in theta loop of hippocampal formation? (Buzsaki, 2007)
  14. 14. Human-level general AI needs ability to generate frames. General intelligence systems should be able to learn to solve problems that were unknown at time of their creation. Obviously, human brain can generate new frames to solve various new problems using learning ability of neocortex. Ⅰ Neuron Ⅱ Column Ⅲ Ⅳ Ⅴ Ⅵ Events A 1 2 3 4 5 6 Variables B C D E Combined new frame Equivalence structure (ES) Values frame Designing HLDL by referring to neocortex is a promising approach. 14
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