why neurons have thousands of synapses? a model of sequence memory in the brain
TRANSCRIPT
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Beijing Normal UniversityDecember, 2015
Yuwei [email protected]
Why neurons have thousands of synapses?
A model of sequence memory in the brain
Collaborators: Jeff Hawkins (PI) Subutai Ahmad Chetan Surpur
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History
2005 – 2009 HTM theory First generation algorithms Hierarchy and vision problems Vision Toolkit
2002
2004
2009 – 2012 Cortical Learning
Algorithms SDRs, sequence memory,
continuous learning Applications exploration
2013 – 2015 Continued HTM
development NuPIC open source project Grok for anomaly detection
20052014 – ?? Sensorimotor Goal directed
behavior Sequence
classification
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Numenta ResearchHTM theoryHTM algorithms
NuPIC
Open source community
Technology Validation and Development
Streaming AnalyticsNatural LanguageSensorimotor Inference
Numenta’s Approach
*HTM = Hierarchical Temporal Memory
NeuroscienceExperimentalResearch
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1) Reverse Engineer the Neocortex
- information and biological theory- making good progress
2) Create Technology for Machine Intelligence based on neocortical principles
- not whole-brain simulation, not human-like- new senses, new embodiments, faster , larger
Numenta’s Goals
Mission: Be the leader in the coming era of machine intelligence
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What Does the Neocortex Do?
Sensory stream
retina
cochlea
somatic
The neocortex learns a model of the world, primarily through behavior.
Sensory arrays
Motor streamThe model is time-based and predictive.
Top three neocortical principles1) Memory-prediction2) Continuous learning3) Sensory-motor integration
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Cortical Architecture
Hierarchy
Cellular layersMini-columns
Neurons: 5-10K synapses
Active dendritesLearning = new synapses
Remarkably uniform - anatomically - functionally
2.5 mm
Sheet of cells
2/34
65
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The Neuron
Σ
ANN neuron
Few synapses
Sum input x weights
Learn by modifying weights of synapses
HTM neuron
Thousands of synapses
Active dendrites: Cell recognizes 100’s of unique patterns
Learn by modeling growth of new synapses
Biological neuron
Thousands of synapses
Active dendrites: Cell recognizes 100’s of unique patterns
Learn by growing new synapses
Feedback
Local
FeedforwardLinearGenerate spikes
Non-linear
8-20 coactive synapses lead to dendritic NMDA spikes
Weakly depolarize soma
Hawkins & Ahmad, arXiv 2015
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High Order Sequences
Two sequences: A-B-C-DX-B-C-Y
Hawkins & Ahmad, arXiv 2015
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B input C input D’ AND Y” predicted
Multiple simultaneous predictions
C’ AND C” predicted
C’ predicted
Prediction of next input
A input B’ predicted B input
Sequence Prediction
Two sequences: A-B-C-DX-B-C-Y
Hawkins & Ahmad, arXiv 2015
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1) On-line learning
2) High-order representationsFor example: sequences “ABCD” vs. “XBCY”
3) Multiple simultaneous predictionsFor example: “BC” predicts both “D” and “Y”
4) Fully local and unsupervised learning rules
5) Extremely robustTolerant to >40% noise and faults
6) High capacity
HTM Sequence Memory : Computational Properties
Extensively tested, deployed in commercial applicationsFull source code and documentation available: numenta.org & github.com/numenta Paper in progress, arXiv version available: (Hawkins & Ahmad, 2015; Cui et al, 2015)
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Performance On Real-World Streaming Data Sources
ARIMA (statistical method)
RecurrentNeural network(LSTM)
HTM
NYC Taxi demand
Cui et al, arXiv 2015
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On-line learning
HTM
Cui et al, arXiv 2015
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Ability to Make Multiple Predictions
Sequence Noise Sequence Noise ……
Test Prediction Accuracy
Cui et al, arXiv 2015
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Ability to Make Multiple Predictions
Cui et al, arXiv 2015
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Fault Tolerance
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Datacenterserver anomalies
Rogue human behavior
Geospatial tracking
Stock anomalies
Applications Using HTM High-Order Inference
Social media streams (Twitter)
HTM High OrderSequence Memory
Encoder
SDRData PredictionsAnomalies
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Summary- Experimental findings from Neuroscience can lead to improved
learning algorithms - Used properties of active dendrites, Hebbian-style plasticity and minicolumns
- Creating biologically inspired algorithms that really work leads to deeper understanding of cortical principles and numerous testable predictions
Research Roadmap- Understand functional properties of laminar microcircuit and
thalamocortical inputs- Model multiple regions and hierarchy- More biophysically accurate neuron models (e.g. spiking models)
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Numenta Licensees
Cortical.ioNatural language processing using HTM principleswww.Cortical.io
GrokStreamIT monitoring using HTMwww.GrokStream.com
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Numenta Research Partnerships
IBM ResearchCreating complete technology stack for HTM systemsLead: Dr. Winfried Wilcke
DARPAHTM-based “Cortical Processor”Lead: Dr. Dan Hammerstrom
University of HeidelbergPorted HTM sequence memory to HICANN neuromorphic chipLead: Dr. Karlheinz Meier
University of BerlinTesting biological predictions of HTM theoryLead: Dr. Matthew Larkum
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1) Sparser activations during a predictable sensory stream.
2) Unanticipated inputs leads to a burst of activity correlated vertically within mini-columns.
3) Neighboring mini-columns will not be correlated.
4) Predicted cells need fast inhibition to inhibit nearby cells within mini-column.
5) For predictable stimuli, dendritic NMDA spikes will be much more frequent than somatic action potentials.
6) Localized synaptic plasticity for dendritic segments that have spiked followed a short time later by a back action potential.
7) The existence of sub-threshold LTP (in the absence of NMDA spikes) in dendritic segments if a cluster of synapses become active followed by a bAP.
8) The existence of localized weak LTD when an NMDA spike is not followed by an action potential.
Testable Predictions
(Vinje & Gallant, 2002)
(Ecker et al, 2010; Smith & Häusser, 2010)
(Smith et al, 2013)
(Losonczy et al, 2008)
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Summary- Experimental findings from Neuroscience can lead to improved
learning algorithms - Used properties of active dendrites, Hebbian-style plasticity and minicolumns
- Creating biologically inspired algorithms that really work leads to deeper understanding of cortical principles and numerous testable predictions
Research Roadmap- Understand functional properties of laminar microcircuit and
thalamocortical inputs- Model multiple regions and hierarchy- More biophysically accurate neuron models (e.g. spiking models)
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Comparison With Common Sequence Memory Algorithms
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Fault Tolerance
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Branco, T., & Häusser, M. (2011). Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron, 69(5), 885–92.
NMDA Dendritic Spike
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Local
Active Dendrites - Highlights
Feedforward
Feedback
Experimental DataSynapses on distal segments have a non-linear effect.
8 to 20 coactive synapses on a distal dendrite branch will cause an NMDA dendritic spike. (This is a small fraction of spines on the branch.)
Synapse activity must be spatially and temporally localized
NMDA spike will depolarize soma but not cause action potential.
85% of excitatory synapses on distal dendrites.
(Branco & Häusser, 2011; Schiller et al, 2000; Losonczy, 2006; Antic et al, 2010; Major et al, 2013; Spruston, 2008; Milojkovic et al, 2005, etc.)