Difference between revisions of "NumentaResearch"
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<pre style="color: green">Results of Numenta Research</pre> | <pre style="color: green">Results of Numenta Research</pre> | ||
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== References == | == References == | ||
| − | * Jeff Hawkins and others - | + | * Jeff Hawkins and others - [http://rstb.royalsocietypublishing.org/content/364/1521/1203.full.pdf Sequence memory for prediction, inference and behaviour] - This paper proposes how each region of neocortex might learn the sequences necessary for this theory |
| − | * 2010 Lecture by Jeff Hawkins - | + | * 2010 Lecture by Jeff Hawkins - [http://www.youtube.com/watch?v=TDzr0_fbnVk link] |
| − | * 2010 Understanding the Bitworm NuPIC HTM Example Program - | + | * 2010 Understanding the Bitworm NuPIC HTM Example Program - [http://www.openicon.com/mu/mu_blog/2010/mu_04_04_2010.html Link] |
== Hierarchical Memory Models == | == Hierarchical Memory Models == | ||
| − | * | + | * '''Spatial only''': Convolutional Neural Networks (Le Cun & Bengio) - 1995; HMAX (Riesenhuber & Poggio) - 1999 |
| − | * | + | * '''Temporal''': HHMM - hierarchical hidden Markov model (Fine et al.) - 1998 |
| − | * | + | * '''Spatial and Temporal''': HTM (Numenta) - 2005 |
== Constraints == | == Constraints == | ||
| Line 21: | Line 20: | ||
Requirements that a biological sequence memory must meet, which are different from linear computer memory. | Requirements that a biological sequence memory must meet, which are different from linear computer memory. | ||
| − | * | + | * '''Probabilistic prediction''' - input and prediction are probability distributions |
| − | * | + | * '''Simultaneous learning and recall''' - simultaneously recalling and predicting |
| − | * | + | * '''Auto-associative recall''' - recognize sequences even if it is presented with a partial sequence from the middle of a previously learned sequence; naturally auto-associative |
| − | * | + | * '''Variable-order memory''' - ‘ABCDE’ and ‘YBCDZ’ - correctly predict the last element of the sequence based on an input that occurred many time steps earlier; the internal representation of an afferent pattern must change depending on the temporal context in which it occurs; the representation for the elements ‘B’, ‘C’ and ‘D’ must be somehow different when preceded by ‘A’ than by ‘Y’ |
| − | * | + | * '''Biological constraints''' - any proposed mechanism should map to one or more prominent features of neocortical anatomy |
== Biological Implications == | == Biological Implications == | ||
| − | * | + | * '''Sparsification of response''' - general cell activity in the neocortex should become more sparse and selective when receiving input in naturally occurring sequences versus receiving spatial inputs in temporal isolation or random order; sparse encoding - efficient method of representation in neural tissue |
| − | * | + | * '''Inhibitory requirements''' - excitatory lateral input to one or a few cells inhibits all the other cells in the near proximity; this laterally induced inhibition must be stronger and faster than the feed-forward excitation |
| − | * | + | * '''Distributed representations''' - distributed representations in two ways: |
| − | + | ** in almost all cases, multiple cells are simultaneously active, although the pattern of activation will always be sparse | |
| − | + | ** activations are distributed; every region of the hierarchy passes a distribution of potentially active sequences to its parent regions | |
| − | * | + | * '''Efficient computation''' - use information from previous inputs when making predictions, and both the history of inputs and the forward predictions are distributions over many states; calculation using dynamic programming by Bellman (see George, D. 2008 How the brain might work: a hierarchical and temporal model for learning and recognition. PhD thesis, Stanford University. §4.6.2) |
| − | * | + | * '''Cortical layers''' - some cell layers are learning feed-forward sequences (layers 4 and 3) and other layers are learning feedback sequences (layers 2 and 6). Layer 5 is where they are combined to form a belief |
| − | * | + | * '''Sequence timing''' - remember the duration for each element in the sequence; can speed up or slow down a recalled sequence, but the absolute duration of the sequence elements is stored and can be recalled; needs a neural mechanism that can encode the durations of sequence elements; this neural mechanism should exist in all regions of the neocortex and should be tightly coupled with the sequence memory mechanism; duration limits need not be fixed, but could depend on the parameters of the model |
| − | * | + | * '''Memory capacity''' - if have 12 columns each with 50 cells, where each column represents one of the 12 musical tones inWestern music, then: |
| − | + | ** it could learn a single sequence of 600 notes using exactly 50 of each of the 12 tones | |
| − | + | ** it could learn 100 sequences of six notes each | |
| − | + | ** capacity of HTM derives primarily from the hierarchy, not the sequence memory in each node; the hierarchy allows learned sequences to be used repeatedly in different combinations | |
== Implementation Hints == | == Implementation Hints == | ||
Latest revision as of 19:08, 28 November 2018
Results of Numenta Research
@@Home -> MemoryPredictionResearch -> NumentaResearch
Contents
References
- Jeff Hawkins and others - Sequence memory for prediction, inference and behaviour - This paper proposes how each region of neocortex might learn the sequences necessary for this theory
- 2010 Lecture by Jeff Hawkins - link
- 2010 Understanding the Bitworm NuPIC HTM Example Program - Link
Hierarchical Memory Models
- Spatial only: Convolutional Neural Networks (Le Cun & Bengio) - 1995; HMAX (Riesenhuber & Poggio) - 1999
- Temporal: HHMM - hierarchical hidden Markov model (Fine et al.) - 1998
- Spatial and Temporal: HTM (Numenta) - 2005
Constraints
Requirements that a biological sequence memory must meet, which are different from linear computer memory.
- Probabilistic prediction - input and prediction are probability distributions
- Simultaneous learning and recall - simultaneously recalling and predicting
- Auto-associative recall - recognize sequences even if it is presented with a partial sequence from the middle of a previously learned sequence; naturally auto-associative
- Variable-order memory - ‘ABCDE’ and ‘YBCDZ’ - correctly predict the last element of the sequence based on an input that occurred many time steps earlier; the internal representation of an afferent pattern must change depending on the temporal context in which it occurs; the representation for the elements ‘B’, ‘C’ and ‘D’ must be somehow different when preceded by ‘A’ than by ‘Y’
- Biological constraints - any proposed mechanism should map to one or more prominent features of neocortical anatomy
Biological Implications
- Sparsification of response - general cell activity in the neocortex should become more sparse and selective when receiving input in naturally occurring sequences versus receiving spatial inputs in temporal isolation or random order; sparse encoding - efficient method of representation in neural tissue
- Inhibitory requirements - excitatory lateral input to one or a few cells inhibits all the other cells in the near proximity; this laterally induced inhibition must be stronger and faster than the feed-forward excitation
- Distributed representations - distributed representations in two ways:
- in almost all cases, multiple cells are simultaneously active, although the pattern of activation will always be sparse
- activations are distributed; every region of the hierarchy passes a distribution of potentially active sequences to its parent regions
- Efficient computation - use information from previous inputs when making predictions, and both the history of inputs and the forward predictions are distributions over many states; calculation using dynamic programming by Bellman (see George, D. 2008 How the brain might work: a hierarchical and temporal model for learning and recognition. PhD thesis, Stanford University. §4.6.2)
- Cortical layers - some cell layers are learning feed-forward sequences (layers 4 and 3) and other layers are learning feedback sequences (layers 2 and 6). Layer 5 is where they are combined to form a belief
- Sequence timing - remember the duration for each element in the sequence; can speed up or slow down a recalled sequence, but the absolute duration of the sequence elements is stored and can be recalled; needs a neural mechanism that can encode the durations of sequence elements; this neural mechanism should exist in all regions of the neocortex and should be tightly coupled with the sequence memory mechanism; duration limits need not be fixed, but could depend on the parameters of the model
- Memory capacity - if have 12 columns each with 50 cells, where each column represents one of the 12 musical tones inWestern music, then:
- it could learn a single sequence of 600 notes using exactly 50 of each of the 12 tones
- it could learn 100 sequences of six notes each
- capacity of HTM derives primarily from the hierarchy, not the sequence memory in each node; the hierarchy allows learned sequences to be used repeatedly in different combinations
Implementation Hints
cortex implementation:
- columns with neurons having the same inputs
- lateral excitatory and inhibitory connections to model sequence activation; in human brain neocortex lateral connections are 90%, with only 10% feed-forward ones
- if one cell in the column becomes active it uses inhibitory lateral connections to hide other cells in the same column - "shut up the other guys"
general:
- bayesian network
- dynamic programming by Bellman to re-use history
- FDR - Fixed-sparsity Distributed Representation
spatial details:
- RBFs are used to make quantization points
- continuous representaion is ultimate goal, still not implemented in HTM
temporal patterns:
- variable-order Markov chain
- sequences of different length
- no sequence begin/end marks
- able to match sequence by middle part
- state-splitting algorithm
- store duration for each sequence item
Regions
- Region 1: run Spatial Pooler ->
- Region 1: run Temporal Pooler (form sequence memory) ->
- Region 2: run Spatial Pooler -> ...
