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The Region-Layer: A building block for AGI

Posted by ProjectAGI on
The Region-Layer: A building block for AGI
Figure 1: The Region-Layer component. The upper surface in the figure is the Region-Layer, which consists of Cells (small rectangles) grouped into Columns. Within each Column, only a few cells are active at any time. The output of the Region-Layer is the activity of the Cells. Columns in the Region-Layer have similar – overlapping – but unique Receptive Fields – illustrated here by lines joining two Columns in the Region-Layer to the input matrix at the bottom. All the Cells in a Column have the same inputs, but respond to different combinations of active input in particular sequential contexts. Overall, the Region-Layer demonstrates self-organization at two scales: into Columns with unique receptive fields, and into Cells responding to unique (input, context) combinations of the Column’s input. 

Introducing the Region-Layer

From our background reading (see here, here, or here) we believe that the key component of a general intelligence can be described as a structure of “Region-Layer” components. As the name suggests, these are finite 2-dimensional areas of cells on a surface. They are surrounded by other Region-Layers, which may be connected in a hierarchical manner; and can be sandwiched by other Region-Layers, on parallel surfaces, by which additional functionality can be achieved. For example, one Region-Layer could implement our concept of the Objective system, another the Region-Layer the Subjective system. Each Region-Layer approximates a single Layer within a Region of Cortex, part of one vertex or level in a hierarchy. For more explanation of this terminology, see earlier articles on Layers and Levels.
The Region-Layer has a biological analogue – it is intended to approximate the collective function of two cell populations within a single layer of a cortical macrocolumn. The first population is a set of pyramidal cells, which we believe perform a sparse classifier function of the input; the second population is a set of inhibitory interneuron cells, which we believe cause the pyramidal cells to become active only in particular sequential contexts, or only when selectively dis-inhibited for other purposes (e.g. attention). Neocortex layers 2/3 and 5 are specifically and individually the inspirations for this model: Each Region-Layer object is supposed to approximate the collective cellular behaviour of a patch of just one of these cortical layers.
We assume the Region-Layer is trained by unsupervised learning only – it finds structure in its input without caring about associated utility or rewards. Learning should be continuous and online, learning as an agent from experience. It should adapt to non-stationary input statistics at any time.
The Region-Layer should be self-organizing: Given a surface of Region-Layer components, they should arrange themselves into a hierarchy automatically. [We may defer implementation of this feature and initially implement a manually-defined hierarchy]. Within each Region-Layer component, the cell populations should exhibit a form of competitive learning such that all cells are used efficiently to model the variety of input observed.
We believe the function of the Region-Layer is best described by Jeff Hawkins: To find spatial features and predictable sequences in the input, and replace them with patterns of cell activity that are increasingly abstract and stable over time. Cumulative discovery of these features over many Region-Layers amounts to an incremental transformation from raw data to fully grounded but abstract symbols. 
Within a Region-Layer, Cells are organized into Columns (see figure 1). Columns are organized within the Region-Layer to optimally cover the distribution of active input observed. Each Column and each Cell responds to only a fraction of the input. Via these two levels of self-organization, the set of active cells becomes a robust, distributed representation of the input.
Given these properties, a surface of Region-Layer components should have nice scaling characteristics, both in response to changing the size of individual Region-Layer column / cell populations and the number of Region-Layer components in the hierarchy. Adding more Region-Layer components should improve input modelling capabilities without any other changes to the system.
So let’s put our cards on the table and test these ideas. 

Region-Layer Implementation


For the algorithm outlined below very few parameters are required. The few that are mentioned are needed merely to describe the resources available to the Region-Layer. In theory, they are not affected by the qualities of the input data. This is a key characteristic of a general intelligence.
  • RW: Width of region layer in Columns
  • RH: Height of region layer in Columns
  • CW: Width of column in Cells 
  • CH: Height of column in Cells

Inputs and Outputs

  • Feed-Forward Input (FFI): Must be sparse, and binary. Size: A matrix of any dimension*.
  • Feed-Back Input (FBI): Sparse, binary Size: A vector of any dimension
  • Prediction Disinhibition Input (PDI): Sparse, rare. Size: Region Area+
  • Feed-Forward Output (FFO): Sparse, binary and distributed. Size: Region Area+
* the 2D shape of input[s] may be important for learning receptive fields of columns and cells, depending on implementation.
+  Region Area = CW * CH * RW * RH


    Here is some pseudocode for iterative update and training of a Region-Layer. Both occur simultaneously.
    We also have fully working code. In the next few blog posts we will describe some of our concrete implementations of this algorithm, and the tests we have performed on it. Watch this space!
    function: UpdateAndTrain( 

    // if no active input, then do nothing
    if( sum( input ) == 0 ) {

    // Sparse activation
    // Note: Can be implemented via a Quilt[1] of any competitive learning algorithm, 
    // e.g. Growing Neural Gas [2], Self-Organizing Maps [3], K-Sparse Autoencoder [4].
    activity(t) = 0

    for-each( column c ) {
      // find cell x that most responds to FFI 
      // in current sequential context given: 
      //  a) prior active cells in region 
      //  b) feedback input.
      x = findBestCellsInColumn( feed_forward_input, feed_back_input, c )

      activity(t)[ x ] = 1

    // Change detection
    // if active cells in region unchanged, then do nothing
    if( activity(t) == activity(t-1) ) {

    // Update receptive fields to organize columns
    trainReceptiveFields( feed_forward_input, columns )

    // Update cell weights given column receptive fields
    // and selected active cells
    trainCells( feed_forward_input, feed_back_input, activity(t) )

    // Predictive coding: output false-negative errors only [5]
    for-each( cell x in region-layer ) {

      coding = 0

      if( ( activity(t)[x] == 1 ) and ( prediction(t-1)[x] == 0 ) ) {
        coding = 1
      // optional: mute output from region, for attentional gating of hierarchy
      if( prediction_disinhibition(t)[x] == 0 ) {
        coding = 0 

      output(t)[x] = coding

    // Update prediction
    // Note: Predictor can be as simple as first-order Hebbian learning. 
    // The prediction model is variable order due to the inclusion of sequential 
    // context in the active cell selection step.
    trainPredictor( activity(t), activity(t-1) )
    prediction(t) = predict( activity(t) )
    AGI/Artificial General Intelligence/deep belief networks/Reading List/Reinforcement Learning/sparse coding/Sparse Distributed Representations/stationary problem/unsupervised learning

    Reading list – May 2016

    Posted by ProjectAGI on
    Reading list – May 2016
    Digit classification error over time in our experiments. The image isn’t very helpful but it’s a hint as to why we’re excited 🙂

    Project AGI

    A few weeks ago we paused the “How to build a General Intelligence” series (part 1, part 2, part 3, part 4). We paused it because the next article in the series requires us to specify everything in detail, and we need working code to do that.

    We have been testing our algorithm on a variety of MNIST-derived handwritten digit datasets, to better understand how well it generalizes its representation of digit-images and how it behaves when exposed to varying degrees of predictability. Initial results look promising: We will post everything here once we’ve verified them and completed the first batch of proper experiments. The series will continue soon!

    Deep Unsupervised Learning

    Our algorithm is a type of Online Deep Unsupervised Learning, so naturally we’re looking carefully at similar algorithms.

    We recommend this video of a talk by Andrew Ng. It starts with a good introduction to the methods and importance of feature representation and touches on types of automatic feature discovery. He looks at some of the important feature detectors in computer vision, such as SIFT and HoG and shows how feature detectors – such as edge detectors – can emerge from more general pattern recognition algorithms such as sparse coding. For more on sparse coding see Shakir’s excellent machine learning blog.

    For anyone struggling to intuit deep feature discovery, I also loved this post on yCombinator which nicely illustrates how and why deep networks discover useful features, and why the depth helps.

    The latter part of the video covers Ng’s latest work on deep hierarchical sparse coding using Deep Belief Networks, in turn based on AutoEncoders. He reports benchmark-beating results on video activity and phoneme recognition with this framework. You can find details of his deep unsupervised algorithm here:

    Finally he presents a plot suggesting that training dataset size is a more important determiner of eventual supervised network performance than algorithm choice! This is a fundamental limitation of supervised learning where the necessary training data is much more limited than in unsupervised learning (in the latter case, the real world provides a handy training set!)

    Effect of algorithm and training set size on accuracy. Training set size more significant. This is a fundamental limitation of supervised learning.

    Online K-sparse autoencoders (with some deep-ness)

    We’ve also been reading this paper by Makhzani and Frey about deep online learning with auto-encoders (a type of supervised learning neural network that is used in an unsupervised way to reconstruct its input, often known as semi-supervised learning). Actually we’ve struggled to find any comparison of autoencoders to earlier methods of unsupervised learning both in terms of computational efficiency and ability to cover the search space effectively. Let us know if you find a paper that covers this.

    The Makhzani paper has some interesting characteristics – the algorithm is online, which means it receives data as a stream rather than in batches. It is also sparse, which we believe is desirable from a representational perspective.
    One limitation is that the solution is most likely unable to handle changes in input data statistics (i.e. non-stationary problems). The reason this is an important quality is that in any arbitrarily deep network the typical position of a vertex is between higher and lower vertices. If all vertices are continually learning, the problem being modelled by any single vertex is constantly changing. Therefore, intermediate vertices must be capable of online learning of non stationary problems otherwise the network will not be able to function effectively. In Makhzani and Frey, they instead use the greedy layerwise training approach from Deep Belief Networks. The authors describe this approach:
    “4.6. Deep Supervised Learning Results The k-sparse autoencoder can be used as a building block of a deep neural network, using greedy layerwise pre-training (Bengio et al., 2007). We first train a shallow k-sparse autoencoder and obtain the hidden codes. We then fix the features and train another ksparse autoencoder on top of them to obtain another set of hidden codes. Then we use the parameters of these autoencoders to initialize a discriminative neural network with two hidden layers.”

    The limitation introduced can be thought of as an inability to escape from local minima that result from prior training. This paper by Choromanska et al tries to explain why this happens.
    Greedy layerwise training is an attempt to work around the fact that deep belief networks of Autoencoders cannot effectively handle nonstationary problems.

    For more information here’s some papers on deep sparse networks built from autoencoders:

    Variations on Supervised Learning – a Taxonomy

    Back to supervised learning, and the limitation of training dataset size. Thanks to a discussion with Jay Chakravarty we have this brief taxonomy of supervised learning workarounds for insufficient training datasets:

    Weakly supervised learning: [For poorly labelled training data] where you want to learn models for object recognition under weak supervision – you have say object labels for images, but no localization (e.g. bounding box) for the object in the image (there might be other objects in the image as well). You would use a Latent SVM to solve the problem of localizing the objects in the images, and at the same time learning a classifier for it.
    Another example of weakly supervised learning is that you have a bag of positive samples mixed up with negative training samples, but also have a bag of purely negative samples – you would use Multiple Instance Learning for this.

    Cross-modal adaptation: where one mode of data supervises another – e.g. audio supervises video or vice-versa.

    Domain adaptation: model learnt on one set of data is adapted, in unsupervised fashion, to new datasets with slightly different data distributions.

    Transfer learning: using the knowledge gained in learning one problem on a different, but related problem. Here’s a good example of transfer learning, a finalist in the NVIDIA 2016 Global Impact Award. The system learns to predict poverty from day and night satellite images, with very few labelled samples.

    Full paper:

    Interactive Brain Concept Map

    We enjoyed this interactive map of the distribution of concepts within the cortex captured using fMRI and produced by the Gallant Lab (source papers here).

    Using the map you can find the voxels corresponding to various concepts, which although maybe not generalizable due to the small sample size (7) gives you a good idea of the hierarchical structure the brain has produced, and what the intermediate concepts represent.

    Thanks to David Ray @ for the link.

    Interactive brain concept map

    OpenAI Gym – Reinforcement Learning platform

    We also follow the OpenAI project with interest. OpenAI have just released their “Gym” – a platform for training and testing reinforcement learning algorithms. Have a play with it here:

    According to Wired magazine, OpenAI will continue to release free and open source software (FOSS) for the wider impact this will have on uptake. There are many companies now competing to win market share in this space.

    The Talking Machines Blog

    We’re regular readers of this blog and have been meaning to mention it for months. Worth reading.

    How the brain generates actions

    A big gap in our knowledge is how the brain generates actions from its internal representation. This new paper by Vicente et al challenges the established (rather vague) dogma on how the brain generates actions.
    “We found that contrary to common belief, the indirect pathway does not always prevent actions from being performed, it can actually reinforce the performance of actions. However, the indirect pathway promotes a different type of actions, habits.”

    This is probably quite informative for reverse-engineering purposes. Full paper here.

    Hierarchical Temporal Memory

    HTM is an online method for feature discovery and representation and now we have a baseline result for HTM on the famous MNIST numerical digit classification problem. Since HTM works with time-series data, the paper compares HTM to LSTM (Long-Short-Term Memory), the leading supervised-learning approach to this problem domain.

    It is also interesting that the paper deals with adaptation to sudden changes in the input data statistics, the very problem that frustrates the deep belief networks described above.

    Full paper by Cui et al here.

    For a detailed mathematical description of HTM see this paper by Mnatzaganian and Kudithipudi.