Why Neurons have thousands of synapses? A model of sequence memory in the brain
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Presentation given by Yuwei Cui, Numenta Research Engineer at Beijing Normal University. December 2015. Collaborators: Jeff Hawkins, Subutai Ahmad, Chetan Surpur
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Why Neurons have thousands of synapses? A model of sequence memory in the brain
- 1. Beijing Normal University December, 2015 Yuwei Cui ycui@numenta.com Why neurons have thousands of synapses? A model of sequence memory in the brain Collaborators: Jeff Hawkins (PI) Subutai Ahmad Chetan Surpur
- 2. 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 2005 2014 – ?? Sensorimotor Goal directed behavior Sequence classification
- 3. Numenta Research HTM theory HTM algorithms NuPIC Open source community Technology Validation and Development Streaming Analytics Natural Language Sensorimotor Inference Numenta’s Approach *HTM = Hierarchical Temporal Memory Neuroscience Experimental Research
- 4. 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
- 5. What Does the Neocortex Do? Sensory stream retina cochlea somatic The neocortex learns a model of the world, primarily through behavior. Sensory arrays Motor stream The model is time-based and predictive. Top three neocortical principles 1) Memory-prediction 2) Continuous learning 3) Sensory-motor integration
- 6. Cortical Architecture Hierarchy Cellular layers Mini-columns Neurons: 5-10K synapses Active dendrites Learning = new synapses Remarkably uniform - anatomically - functionally 2.5 mm Sheet of cells 2/3 4 6 5
- 7. 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 Feedforward Linear Generate spikes Non-linear 8-20 coactive synapses lead to dendritic NMDA spikes Weakly depolarize soma Hawkins & Ahmad, arXiv 2015
- 8. High Order Sequences Two sequences: A-B-C-D X-B-C-Y Hawkins & Ahmad, arXiv 2015 X A B B C C D Y Before learning X B’’ C’’ D’ Y’’ After learning A B’ C’ Same columns, but only one cell active per column after learning. Active cells Depolarized (predictive) cells Inactive cells Time X A B B C C D Y Before learning X B’’ C’’ D’ Y’’ After learning A B’ C’ Same columns, but only one cell active per column after learning. Active cells Depolarized (predictive) cells Inactive cells Time
- 9. 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-D X-B-C-Y Hawkins & Ahmad, arXiv 2015
- 10. 1) On-line learning 2) High-order representations For example: sequences “ABCD” vs. “XBCY” 3) Multiple simultaneous predictions For example: “BC” predicts both “D” and “Y” 4) Fully local and unsupervised learning rules 5) Extremely robust Tolerant to >40% noise and faults 6) High capacity HTM Sequence Memory : Computational Properties Extensively tested, deployed in commercial applications Full source code and documentation available: numenta.org & github.com/numenta Paper in progress, arXiv version available: (Hawkins & Ahmad, 2015; Cui et al, 2015)
- 11. 015-04-23 Thursday 2015-04-24 Friday 2015-04-25 Saturday 2015-04-26 Sunday B 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Meanabsolutepercenterror 0.0 0.5 1.0 1.5 2.0 2.5 NegativeLog-likelihood ShiftAR IM A LSTM 1000 LSTM 3000 LSTM 6000 TM LSTM 1000 LSTM 3000 LSTM 6000 TM C Performance On Real-World Streaming Data Sources 2015-04-20 Monday 2015-04-21 Tuesday 2015-04-22 Wednesday 2015-04-23 Thursday 2015-04-24 Friday 2015-04-25 Saturday 2015-04-26 Sunday 0 k 5 k 10 k 15 k 20 k 25 k 30 k PassengerCountin30minwindow A B C Shift AR IM A LSTM 1000 LSTM 3000 LSTM 6000 TM 0.0 0.2 0.4 0.6 0.8 1.0 NRMSE 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 MAPE 0.0 0.5 1.0 1.5 2.0 2.5 NegativeLog-likelihood Shift AR IM A LSTM 1000 LSTM 3000 LSTM 6000 TM LSTM 1000 LSTM 3000 LSTM 6000 TM D ARIMA (statistical method) Recurrent Neural network (LSTM) HTM NYC Taxi demand Cui et al, arXiv 2015
- 12. On-line learning Apr 01 2015 Apr 08 2015 Apr 15 2015 Apr 22 2015 Apr 29 2015 May 06 2015 1.2 1.4 1.6 1.8 2.0 2.2 NegativeLog-likelihood LSTM3000 LSTM6000 TM 0.30 0.35 0.12 2.0 A B HTM Cui et al, arXiv 2015
- 13. Ability to Make Multiple Predictions Sequence Noise Sequence Noise … … Test Prediction Accuracy Cui et al, arXiv 2015
- 14. Ability to Make Multiple Predictions 0 2000 4000 6000 8000 10000 12000 14000 0.0 0.2 0.4 0.6 0.8 1.0 TM (# predictions = 2) LSTM (# predictions = 2) TM (# predictions = 4) LSTM (# predictions = 4) PredictionAccuracy Number of elements seen Cui et al, arXiv 2015
- 15. Fault Tolerance
- 16. Datacenter server anomalies Rogue human behavior Geospatial tracking Stock anomalies Applications Using HTM High-Order Inference Social media streams (Twitter) HTM High Order Sequence Memory Encoder SDRData Predictions Anomalies
- 17. 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)
- 18. Collaborators - Jeff Hawkins (PI) - Subutai Ahmad - Chetan Surpur Contact info: ycui@numenta.com
- 19. Numenta Licensees Cortical.io Natural language processing using HTM principles www.Cortical.io GrokStream IT monitoring using HTM www.GrokStream.com
- 20. Numenta Research Partnerships IBM Research Creating complete technology stack for HTM systems Lead: Dr. Winfried Wilcke DARPA HTM-based “Cortical Processor” Lead: Dr. Dan Hammerstrom University of Heidelberg Ported HTM sequence memory to HICANN neuromorphic chip Lead: Dr. Karlheinz Meier University of Berlin Testing biological predictions of HTM theory Lead: Dr. Matthew Larkum
- 21. 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)
- 22. 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)
- 23. Collaborators - Jeff Hawkins (PI) - Subutai Ahmad - Chetan Surpur Contact info: ycui@numenta.com
- 25. Comparison With Common Sequence Memory Algorithms
- 26. Fault Tolerance
- 27. Branco, T., & Häusser, M. (2011). Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron, 69(5), 885–92. NMDA Dendritic Spike
- 29. Local Active Dendrites - Highlights Feedforward Feedback Experimental Data Synapses 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.)
Editor's Notes
- I don't know how many of you have heard about Numenta. Founded by Jeff Hawkins in 2005, we are an unusual research focused organization - we focus on understanding the computational principles of the neocortex. My background is in computer science and machine learning.
- I don't know how many of you have heard about Numenta. Founded by Jeff Hawkins in 2005, we are an unusual research focused organization - we focus on understanding the computational principles of the neocortex
- We study experimental research in neuroscience. We use these to improve our theory and learning algorithms. Why bother? Why not stick with the existing ML paradigm? Well if you look at the history of ML, insights from neuroscience have led to numerous fundamental advances in machine learning (including by the way, the very first learning algorithm). But lately the field has ignored neuroscience. At Numenta we think that's a big mistake. We validate that our algorithms actually work in real-world applications. We also release everything we do as open source and have cultivated a very fast growing open source community. NuPIC is one of the top machine learning projects on github today. Two points here: 1) we think this approach will lead to qualitative leaps in learning algorithms. 2) <animate back arrow> I am hopeful that our theories will help inform experimental work as well. There is a large set of detailed testable predictions that come out of our theory.
- There are a few more things I don’t show, but this is the architecture we want to understand and the one we need to replicate for MI.
- What we can show is that a population of such neurons arranged in minicolumns leads to an extremely powerful sequence memory algorithm.
- Matt