I have taken this from a scientific paper I wich that helps you ^_^:
SDRs are the neocortically inspired data structure used in HTMs; they represent neurons and their state by means of a binary vector. For such a vector to be an SDR, only a small percentage, typically two percent, of its entries should be active (i.e., set to one). This is based on the fact that the relative number of neurons that are active in the neocortex at any given time is low. SDRs encode the semantic meaning of the data and have some valuable properties such as fault and noise tolerance, can be easily compressed, and the semantic similarity between two inputs can be quickly determined via a bit comparison, among others .
Transforming input data (It ) such as scalar values, dates, and categorical values into binary arrays (xt ) is achieved by using encoders. They are responsible for determining which entries in the array, or which bits, will be ones and which ones will be zero. This must be done in such a way that the semantic characteristics of the data are captured in its encoding. In this way, encodings for semantically similar data will have a larger number of overlapping bits than dissimilar ones.
Each encoded pattern xt is transformed into an SDR representing a set of neurons arranged in columns by the spatial pooler. For the purpose of this component, all neurons in a column are treated as a unit since it is assumed that all neurons within a column detect identical feedforward input patterns . Similarly to the input binary array, each column can be either active or inactive. Furthermore, the resulting SDR maintains the semantic properties of the input array; that is, SDRs of input arrays that are semantically dissimilar will also be dissimilar and vice versa. This is achieved by assigning each neuron with a set of potential connections (or synapses) to a random subset of the input array. Each potential synapse has a permanence value associated with it and only when this permanence value is greater than a threshold, the synapse is said to be connected. A neuron is then said to be active in the output SDR if the number of connected synapses to active entries in the input array is greater than a threshold. The learning process in the spatial pooler consists on adjusting these permanence values so that the model learns to represent spatial properties of the input data using SDRs in a way in which the semantic properties of the input vector are maintained.
The SDR produced by the spatial pooler, a(xt ), is fed as input to the temporal memory. In this case, individual cells or neurons within a column are differentiated upon as at any given point in time, a subset of neurons in the active columns will be used to represent the temporal context of the current spatial pattern. Neurons within a column can be in three different states: active, inactive, or predictive. Neurons in a predictive state become active in the next time step if they receive sufficient feedforward input, that is, if their column becomes active. In this way, neurons in such a state are anticipating that they will be active in the next time step, and hence are predicting what the next input may be. The output of the temporal memory, π(xt ), can then be interpreted as a prediction for a(xt+1), that is, columns that could potentially beactive in the next time step.
