Hierarchical Temporal Memory - Slovensk technick univerzita v

Hierarchical Temporal MemoryBio-inspired model of neocortex

Kristian Valentın

FMFI UK, UM SAV

[email protected]

June 13, 2012

IntroductionNeocortex

Hierarchical Temporal Memory

Overview

1 IntroductionMotivationResearch GoalsTask of Visual Object Recognition

2 NeocortexCommon Cortical AlgorithmNeocortex

3 Hierarchical Temporal MemoryHistoryMemory-Prediction Theory of Brain FunctionHierarchical Temporal MemoryGoals

Kristian Valentın Hierarchical Temporal Memory

MotivationResearch GoalsTask of Visual Object Recognition

Outline

Motivation

Interesting field of AI

Many possible applications

Finding out something about us

Motivation

Research Goals

Long-term goals

Build an object recognition system which enables to classify visualobjects in complex scenes

Short-term goal

Hierarchical Temporal Memory (HTM)

Current goal

Improve spatial and temporal learning methods (invariancy, speed ofconvergence)

Research Goals

Long-term goals

Short-term goal

Current goal

Research Goals

Long-term goals

Short-term goal

Current goal

Task of Visual Object Recognition

Classification of an object in a visual scene

Invariant classification

scalepositionrotation

Task of Visual Object RecognitionExample

Figure: (George, 2007)

Similarity Measure: Euclidean distance

Figure: (George, 2007)

Spatio-temporal Representation

Common Cortical AlgorithmNeocortex

Outline

Common Cortical AlgorithmSupporting evidence

“Seeing in the Sound Zone,“ by Michael Merzenich, Nature, Vol.404, April 20, 2000, pp. 820-821.

”Induction of visual orientation modules in auditory cortex,“ byJitendra Sharma, Alessandra Angelucci and Mriganka Sur, Nature,Vol. 404, April 20, 2000, pp. 841-847

Sensory Substitution

A sensorimotor account of vision and visual consciousness, KevinO’Regan and Alva Noe, 2001.Photograph courtesy: P. Bach-y-Rita

Assumptions

Neocortex uses the same algorithm to learn different modalities

It learns efficiently

No Free Lunch Theorem

No learning algorithm has an inherent superiority over otherlearning algorithms for all problems. (Wolpert, 1995)

An algorithm’s superiority comes from the assumptions that it makesabout the problem

No Free Lunch Theorem

No learning algorithm has an inherent superiority over otherlearning algorithms for all problems. (Wolpert, 1995)

An algorithm’s superiority comes from the assumptions that it makesabout the problem

Assumptions

=>Data from different modalities must have the same underlyingstatistical structure

Assumptions

=>Data from different modalities must have the same underlyingstatistical structure

High-level properties of the neocortex

Hierarchical organization (spatial and temporal)

Using time as a supervisor

Ability to make predictions

Sparse Distributed Representations

Feed-forward and feedback connections

Sensory-motor

Why unsupervised pre-training makes sense

If image-label pairs were generatedthis way, it would make sense to trygo straight from images to labels

If image-label pairs were generatedthis way, it makes sense to first learnrecover the stuff that caused theimage by inverting the highbandwidth highway.

Credit: G. Hinton

Causes of the World

HistoryMemory-Prediction Theory of Brain FunctionHierarchical Temporal MemoryGoals

Outline

History

1 Memory-Prediction Framework

proposed that the neocortex uses hierarchical sequence memory forstoring and inferring causes in the worldproposed several learning algorithms including a detailed mappingonto the large scale cortical-thalamic architecture, as well as ontothe microcircuits of cortical columns

2 Hierarchical Temporal Memory (HTM)

proper mathematical foundation for M-P frameworkmapping the mathematics of HTM directly to cortical-thalamicanatomy and the microcircuits of cortical columns

History

1 Memory-Prediction Framework

proposed that the neocortex uses hierarchical sequence memory forstoring and inferring causes in the worldproposed several learning algorithms including a detailed mappingonto the large scale cortical-thalamic architecture, as well as ontothe microcircuits of cortical columns

2 Hierarchical Temporal Memory (HTM)

proper mathematical foundation for M-P frameworkmapping the mathematics of HTM directly to cortical-thalamicanatomy and the microcircuits of cortical columns

Memory-Prediction Theory of Brain Function

Founded in On Intelligence (Hawkins Blakeslee, 2004)

Basic idea

The brain is a mechanism predicting the future and hierarchical regions ofthe brain predict their future input sequences.

Motivated by the observed fact that the mammalian neocortex isremarkably uniform in appearance and structure

Principally, the same hierarchical structures are used for a widerange of behaviors (+plasticity)

Assumptions

patterns from different senses are equivalent inside the brainthe same biological structures are used to process the sensory inputsa single principle (a feedback/recall loop) underlies processing of thepatterns

Basic idea

Assumptions

Basic idea

Assumptions

Large-scale hierarchical model of the neocortex by Hawkins andGeorge, Numenta Inc. (2004, 2008)

Common cortical algorithm for all nodes

Learn common spatial patternsLearn common sequences of those patterns

Goal: Create a hierarchical, spatio-temporal model of data

Time is the teacher

Inference (recognition)

Bayesian belief propagationProbability of sequences passed upPredicted spatial patterns passed down

Time is the teacher

Structure

Figure: (Maltoni, 2011)Kristian Valentın Hierarchical Temporal Memory

Structure

Figure: (Maltoni, 2011)

Structure of a Node

Learning Algorithms

Spatial pooling

Learning common spatial patterns

Temporal pooling

Learning common sequences of those patterns

Learning sequence

Summary of Learning in the HTM Node

Example of Resulting Representations

Figure: (Maltoni, 2011)

strategy of generating training sequences

feedback information flow – covert attention (peripheral visual andmental focus)

complex natural imagesmulti-object recognition

experimenting with the training process

unsupervised pre-training followed be supervised refinement

studying sparse coding for increasing the scalability of HTM

incorporating color

Article

Stolc, Bajla, Valentın, Skoviera: Pair-Wise Temporal PoolingMethod for Rapid Training of the HTM Networks, Vol. 31, 2012,901–919

64 pixels

Rectangles Triangles Circles

8 pixels

Codebook Generation

Proposed coordinates Accepted coordinates Codebook

Pair-wise Explorer

Smooth explorer Pair-wise explorer

3 x 104

tion a

Number of training

images per object class Number of TAM updates

Spatially specific features

3 x 104

Number of TAM updates

Number of training

images per object class

Bag of features

Pair-wise explorer Smooth explorer

0 0.5 1 1.5 2 2.5 3 3.5 x 1040.98

Spatially specific features

2896 and 4096 TAM updatesA

0 0.5 1 1.5 2 2.5 3 3.5 x 1040.98

Bag of features

racy r

2896 and 4096 TAM updates

Thank you for your attention

Hierarchical Temporal Memory - Slovensk technick univerzita v

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