HTM synapses have binary weights. Biological synapses have varying weights but they are also partially stochastic, suggesting a biological neuron cannot rely on precise synaptic weights. The use of distributed representations in HTMs plus our model of dendrite operation allows us to assign binary weights to HTM synapses with no ill effect. To model the forming and un-forming of synapses we use two additional concepts from neuroscience that you may not be familiar with. One is the concept of “potential synapses”. This represents all the axons that pass close
enough to a dendrite segment that they could potentially form a synapse. The second is called “permanence”. This is a scalar value assigned to each potential synapse. The permanence of a synapse represents a range of connectedness between an axon and a dendrite. Biologically, the range would go from completely unconnected, to starting to form a synapse but not connected yet, to a minimally connected synapse, to a large fully connected synapse. The permanence of a synapse is a scalar value ranging from 0.0 to 1.0. Learning involves incrementing
and decrementing a synapse’s permanence. When a synapse’s permanence is above a threshold, it is connected with a weight of “1”. When it is below the threshold, it is unconnected with a weight of “0”.
