Numenta published papers code and data

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Deep Learning paper theory htm neocortex numenta
Overview

Numenta research papers code and data

This repository contains reproducible code for selected Numenta papers. It is currently under construction and will eventually include the source code for all the scripts used in Numenta's papers.

Grid Cell Path Integration For Movement-Based Visual Object Recognition

This paper demonstrates the implementation of a sensorimotor network that uses grid-cell computations to process a sequence of visual inputs, specifically a sequence of image patches from the MNIST dataset. The network is able to classify novel digits (as well as perform other tasks) in a way that is robust to the specific sequence over which the visual space is sampled, a challenging setting for typical machine learning approaches. The work builds on our previous paper, “Locations in the Neocortex."

Sources

Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual Learning

In this paper we investigate how dendritic properties can add value to ANNs in the context of continual learning, an area where ANNs suffer from catastrophic forgetting

Sources

How Can We Be So Dense? The Benefits of Using Highly Sparse Representations

In this paper we discuss inherent benefits of high dimensional sparse representations. We focus on robustness and sensitivity to interference. These are central issues with today’s neural network systems where even small and large perturbations can cause dramatic changes to a network’s output.

Sources

Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells

This paper provides an implementation for a location layer with grid-like modules that encode object-specific locations. This layer is incorpated into a network with an input layer and simulations show how the model can learn many complex objects and later infer which learned object is being sensed.

Sources

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

This paper proposes a network model composed of columns and layers that performs robust object learning and recognition. The model introduces a new feature to cortical columns, location information, which is represented relative to the object being sensed. Pairing sensory features with locations is a requirement for modeling objects and therefore must occur somewhere in the neocortex. We propose it occurs in every column in every region.

Sources

The HTM Spatial Pooler – a neocortical algorithm for online sparse distributed coding

This paper describes an important component of HTM, the HTM spatial pooler, which is a neurally inspired algorithm that learns sparse distributed representations online. Written from a neuroscience perspective, the paper demonstrates key computational properties of HTM spatial pooler.

Sources

Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

14th IEEE ICMLA 2015 - This paper discusses how we should think about anomaly detection for streaming applications. It introduces a new open-source benchmark for detecting anomalies in real-time, time-series data.

Sources

Unsupervised Real-Time Anomaly Detection for Streaming Data

This paper discusses the requirements necessary for real-time anomaly detection in streaming data, and demonstrates how Numenta's online sequence memory algorithm, HTM, meets those requirements. It presents detailed results using the Numenta Anomaly Benchmark (NAB), the first open-source benchmark designed for testing real-time anomaly detection algorithms.

Sources

Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex

Foundational paper describing core HTM theory for sequence memory and its relationship to the neocortex. Written with a neuroscience perspective, the paper explains why neurons need so many synapses and how networks of neurons can form a powerful sequence learning mechanism.

Sources

Comments
  • how_can_we_be_so_dense - How to modify to generate binary encodings?

    how_can_we_be_so_dense - How to modify to generate binary encodings?

    Hi, I'm interested in your HTM Spatial Pooler and the paper in the title.

    Here's the question: the SparseNet introduced in the paper generates sparse scalar vectors. But the original HTM generates binary vectors. Can I modify the SparseNet to do the same??

    opened by kyoungrok0517 4
  • Questions about running the_htm_spatial_pooler program

    Questions about running the_htm_spatial_pooler program

    I tried running train_sp.py but it didn't work. I don't know the reason, so I want to ask for help. Error message: Traceback (most recent call last):    File "/disk2/zya/htm_pro/htm-paper/frontiers/the_htm_spatial_pooler_a_neocortical_algorithm_for_online_sparse_distributed_coding/train_sp.py", line 640, in      Metrics, expName = runSPexperiments(expConfig)    File "/disk2/zya/htm_pro/htm-paper/frontiers/the_htm_spatial_pooler_a_neocortical_algorithm_for_online_sparse_distributed_coding/train_sp.py", line 553, in runSPexperiments      reconstructionError(sp, testInputs, activeColumnsCurrentEpoch))    File "/disk2/zya/env/paper-htm/lib/python2.7/site-packages/htmresearch/frameworks/sp_paper/sp_metrics.py", line 836, in reconstructionError      numActiveColumns = int(sp._localAreaDensity * sp._numColumns) + 0.0 AttributeError: 'SpatialPooler' object has no attribute '_localAreaDensity'

    opened by PerfectVeo 2
  • Bump pillow from 5.2.0 to 6.2.0 in /frontiers/location_in_the_neocortex_a_theory_of_sensorimotor_object_recognition_using_cortical_grid_cells

    Bump pillow from 5.2.0 to 6.2.0 in /frontiers/location_in_the_neocortex_a_theory_of_sensorimotor_object_recognition_using_cortical_grid_cells

    Bumps pillow from 5.2.0 to 6.2.0.

    Release notes

    Sourced from pillow's releases.

    6.2.0

    https://pillow.readthedocs.io/en/stable/releasenotes/6.2.0.html

    6.1.0

    https://pillow.readthedocs.io/en/stable/releasenotes/6.1.0.html

    6.0.0

    No release notes provided.

    5.4.1

    No release notes provided.

    5.4.0

    No release notes provided.

    5.3.0

    No release notes provided.

    Changelog

    Sourced from pillow's changelog.

    6.2.0 (2019-10-01)

    • Catch buffer overruns #4104 [radarhere]

    • Initialize rows_per_strip when RowsPerStrip tag is missing #4034 [cgohlke, radarhere]

    • Raise error if TIFF dimension is a string #4103 [radarhere]

    • Added decompression bomb checks #4102 [radarhere]

    • Fix ImageGrab.grab DPI scaling on Windows 10 version 1607+ #4000 [nulano, radarhere]

    • Corrected negative seeks #4101 [radarhere]

    • Added argument to capture all screens on Windows #3950 [nulano, radarhere]

    • Updated warning to specify when Image.frombuffer defaults will change #4086 [radarhere]

    • Changed WindowsViewer format to PNG #4080 [radarhere]

    • Use TIFF orientation #4063 [radarhere]

    • Raise the same error if a truncated image is loaded a second time #3965 [radarhere]

    • Lazily use ImageFileDirectory_v1 values from Exif #4031 [radarhere]

    • Improved HSV conversion #4004 [radarhere]

    • Added text stroking #3978 [radarhere, hugovk]

    • No more deprecated bdist_wininst .exe installers #4029 [hugovk]

    • Do not allow floodfill to extend into negative coordinates #4017 [radarhere]

    ... (truncated)
    Commits

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.12.0 to 1.12.2 in /arxiv/how_can_we_be_so_dense

    Bump tensorflow from 1.12.0 to 1.12.2 in /arxiv/how_can_we_be_so_dense

    Bumps tensorflow from 1.12.0 to 1.12.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 1.12.2

    Release 1.12.2

    Bug Fixes and Other Changes

    • Fixes a potential security vulnerability where carefully crafted GIF images can produce a null pointer dereference during decoding
    Changelog

    Sourced from tensorflow's changelog.

    Release 1.12.2

    Bug Fixes and Other Changes

    • Fixes a potential security vulnerability where carefully crafted GIF images can produce a null pointer dereference during decoding.

    Release 1.13.0

    Major Features and Improvements

    • TensorFlow Lite has moved from contrib to core. This means that Python modules are under tf.lite and source code is now under tensorflow/lite rather than tensorflow/contrib/lite.
    • TensorFlow GPU binaries are now built against CUDA 10 and TensorRT 5.0.
    • Support for Python3.7 on all operating systems.
    • Moved NCCL to core.

    Behavioral changes

    • Disallow conversion of python floating types to uint32/64 (matching behavior of other integer types) in tf.constant.
    • Make the gain argument of convolutional orthogonal initializers (convolutional_delta_orthogonal, convolutional_orthogonal_1D, convolutional_orthogonal_2D, convolutional_orthogonal_3D) have consistent behavior with the tf.initializers.orthogonal initializer, i.e. scale the output l2-norm by gain and NOT by sqrt(gain). (Note that these functions are currently in tf.contrib which is not guaranteed backward compatible).

    Bug Fixes and Other Changes

    • Documentation
      • Update the doc with the details about the rounding mode used in quantize_and_dequantize_v2.
      • Clarify that tensorflow::port::InitMain() should be called before using the TensorFlow library. Programs failing to do this are not portable to all platforms.
    • Deprecations and Symbol renames.
      • Removing deprecations for the following endpoints: tf.acos, tf.acosh, tf.add, tf.as_string, tf.asin, tf.asinh, tf.atan, tf.atan2, tf.atanh, tf.cos, tf.cosh, tf.equal, tf.exp, tf.floor, tf.greater, tf.greater_equal, tf.less, tf.less_equal, tf.log, tf.logp1, tf.logical_and, tf.logical_not, tf.logical_or, tf.maximum, tf.minimum, tf.not_equal, tf.sin, tf.sinh, tf.tan
      • Deprecate tf.data.Dataset.shard.
      • Deprecate saved_model.loader.load which is replaced by saved_model.load and saved_model.main_op, which will be replaced by saved_model.main_op in V2.
      • Deprecate tf.QUANTIZED_DTYPES. The official new symbol is tf.dtypes.QUANTIZED_DTYPES.
      • Update sklearn imports for deprecated packages.
      • Deprecate Variable.count_up_to and tf.count_up_to in favor of Dataset.range.
      • Export confusion_matrix op as tf.math.confusion_matrix instead of tf.train.confusion_matrix.
      • Add tf.dtypes. endpoint for every constant in dtypes.py. Moving endpoints in versions.py to corresponding endpoints in tf.sysconfig.
    ... (truncated)
    Commits
    • 6b63465 Merge pull request #27959 from tensorflow/update-release-notes-version
    • e967833 Update header on release notes
    • cf74798 Merge pull request #27958 from tensorflow/update-release-version
    • 7fba173 Update version to 1.12.2
    • 332f080 Merge pull request #27878 from tensorflow/windows-cpu
    • c9fcc49 Fix windows build for CPU too
    • 416b4a3 Merge pull request #27873 from tensorflow/more-bazel-incompatible-flags
    • 3ebe165 Add --incompatible_disable_cc_toolchain_label_from_crosstool_proto=false flag
    • 5ab9466 Reformat bazel invocation lines
    • 446d393 Merge pull request #27870 from tensorflow/bazel-http-archive
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Why does this repo contain only 2 papers?

    Why does this repo contain only 2 papers?

    In the README file, you say

    This repository is currently under construction and will include the source code for all scripts used on Numenta's papers.

    But, AFAIK, there have been more than 2 papers already published by Numenta. I am not sure why this would take so much time to do. The last commit to this repo was in November of last year.

    opened by nbro 1
  • Add latest dendrites project code

    Add latest dendrites project code

    Experiment files in biorxiv/going_beyond_the_point_neuron/permutedMNIST/experiments/ have been updated to match nupic.research. The requirements file has not yet been updated, and this PR should not be merged until that is resolved.

    These updates attempt to address issue #38.

    opened by karangrewal 0
  • Remove dead line of code

    Remove dead line of code

    Bug introduced during refactoring. (I never noticed because the script hits the error after successfully generating charts.)

    This has moved into plot_aggregate_narrowing.py

    opened by mrcslws 0
  • Missing property on sp in HTM SP paper

    Missing property on sp in HTM SP paper

    See original report on HTM forum. I was able to replicate the error below:

    Traceback (most recent call last):
  File "train_sp.py", line 640, in <module>
    metrics, expName = runSPexperiments(expConfig)
  File "train_sp.py", line 553, in runSPexperiments
    reconstructionError(sp, testInputs, activeColumnsCurrentEpoch))
  File "/Users/mtaylor/nta/htmresearch/htmresearch/frameworks/sp_paper/sp_metrics.py", line 836, in reconstructionError
    numActiveColumns = int(sp._localAreaDensity * sp._numColumns) + 0.0
AttributeError: 'SpatialPooler' object has no attribute '_localAreaDensity'

    This looks like inappropriate intimacy code smell. The sp instance is a SWiG wrapper, and it has no _localAreaDensity property. May need to add a const accessor in the cpp bindings? cc @rcrowder

    bug 
    opened by rhyolight 0
  • 'Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual Learning'

    'Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual Learning'

    opened by zhaoyaqia 0
Owner
Numenta
Biologically inspired machine intelligence
Numenta
GitHub
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