Advances in Modeling Neocortexand its impact on machine intelligence

Jeff HawkinsNumenta Inc.VS265 Neural ComputationDecember 2, 2010

Documentation for the algorithms described in this talk can be found at www.numenta.com (papers)

Premise

1) The principles of brain function can be understood.

2) We can build machines that work on these principles.

3) Many machine learning, A.I., and robotics problems can only be solved this way.

Neocortex Is Our Focus

• 75% of volume of human brain• All high level vision, audition, motor, language, thought• Composed of a repetitive element

- complex- hierarchical

Process

Algorithmicneeds

Neurobiologyanatomy, physiology

Biologicalmodel

Computermodel

empirical results

Our computer models are biologically and empirically driven.

Neocortex (large scale architecture)

Neocortex overall- 1000 cm^2, 2 mm thick- 30 billion cells- 100 trillion synapses

Regions- Nearly identical architecture- Differentiated by connectivity- Common algorithms

Hierarchy- Convergence- Temporal slowness

Hierarchical Temporal Memory (Basic)

Regions- Learn common spatial patterns

(Sparse Distributed Representations of input)

- Learn sequences of common spatial patterns(variable order transitions of SDRs)

- Pass stable representations up hierarchy- Unfold sequences going down hierarchy

Hierarchy- Reduces memory and training time- Provides means of generalization

Sequence Memory is Key

Why is sequence memory so important?- Prediction- Motor behavior- Time-based inference- Spatial inference

Attributes- High capacity- Context dependent- Robust- Multiple simultaneous predictions- Form stable representations of sequences- On-line learning

Neocortical regions

Biology

- Five layers of cells Densely packed Massively interconnected

- Cells in columns have similar response properties- Majority of connections are within layer- Feed forward connections are few but strong- Layers 4 and 3 are primary feed forward layers

Layer 4 disappears as you ascend hierarchy

Hypothesis

- Common mechanism is used in each layer- Each layer is a sequence memory

Learns transitions of sparse distributed patterns- Layer 4 learns first order transitions

Ideal for spatial inference, “simple cells”- Layer 3 learns variable order transitions

Ideal for time-based inference, “complex cells”- Layer 5 motor

specific timing- Layers 2, 6 feedback, attention

1

2/3

4

5

6

to higher region

from lower region

Neurons

Real neuronProximal dendrites

Linear summationFeed forward connections

Distal dendritesDozens of regionsNon-linear integrationConnections to other cells in layer

Synapses Thousands on distal dendrites Hundreds on proximal dendrites Numerous learning rules Forming and un-forming constantly

OutputVariable spike rateBursts of spikesProjects laterally and inter-layer

Not a neuronSum of weighted synapsesNon-linear functionScalar output

HTM neuronProximal dendrite

Linear summationFeed forward connections

Distal dendritesDozens of regionsThreshold coincidence detectorsConnections to other cells in layer

Synapses Thousands on distal dendrites (dozens per segment) Hundreds on proximal dendrite Scalar Permanence Binary weight

OutputActive state (fast or burst)Predictive state (slow)Projects laterally and inter-region

HTM Regions

What is an HTM region?

- A set of neurons arranged in columns- Cells in column have same feed forward activation- Cells in column have different response in context

What does a region do?

1) Creates a sparse distributed representation of input2) Creates a representation of input in the context of prior inputs3) Learns sequences of representations from 2)

4) Forms a prediction based on the current input in the context of previous inputsThis prediction is a slow changing representation of sequence.It is the output of the region.

Cellular layer - 1 cell per column

Internal potential of cells (via feed forward input to proximal synapses)

Cellular layer - 1 cell per column

Cells with highest potential fire first, inhibit neighbors

Cellular layer - 1 cell per column

Sparse Distributed Representation of input (time = 1)

Cellular layer - 1 cell per column

Sparse Distributed Representation of input (time = 2)

Cellular layer - 1 cell per column

Sparse Distributed Representation of input (time = 1)

Cellular layer - 1 cell per column

Sparse Distributed Representation of input (time = 2)

Cellular layer - 1 cell per column

Prediction (via lateral connections to distal dendrite segments)

With 1 cell per column, all transitions are first order

Cellular layer - 4 cells per column – no context

Sparse Distributed Representation of input (if unpredicted, all cells in a column fire)

Cellular layer - 4 cells per column – with prior context

Sparse Distributed Representation of input (if predicted, one cell in a column fires)Represents input in the context of prior states (variable order sequence memory)

Cellular layer - 4 cells per column

Prediction In the context of prior states

Cellular layer - 4 cells per column

Predicted columns

Unpredicted column

HTM Neuron

Distal dendrite segments- Act like coincidence detectors Recognize state of region

- When segment active, cell enters predictive state- Typical threshold, 15 active synapses, sufficient!

- Synapses formed from “potential synapse” pool

- Each segment can learn several patterns without error

- One cell can participate in many different sequences

Learning rules- If a segment is active

Modify synapses on active segment Modify synapses on segment best matching t=-1

- Modify permanence for all potential synapses Increase for active synapses Decrease for inactive synapses Permanence range 0.0 to 1.0, >0.2 = valid

Feed forward inputLateral connections from other cells in region

Proximal dendrite- Shared by all cells in a column- Linear summation of input- Boosted by duty cycle- Synapses formed from “potential synapse” pool- Leads to self-adjusting representations

HTM Cortical Learning Algorithm

Variable order sequence memoryTime-based and static inferenceMassive predictive abilityUses sparse distributed representations

High capacityHigh noise immunityOn-line learningDeep biological mappingSelf adjusting representations

What’s next

Commercial ApplicationsNumenta is applying these new algorithms to data analytics problems, e.g.- Credit card fraud- Large sensor environments- Web click prediction

ResearchFull documentation plus pseudo code at www.numenta.com (papers)

We will release software in 2011

All use is free for research purposes

Engage Numenta for further discussions

EmploymentInterns and full time positionjhawkins@numenta.com

The Future of Machine Intelligence