Competition is a common motif in biology & neuroscience. In this post I describe and analyse using a competition to control synapse formation.

Written by David McDougall, 2019

# Hebbian Learning and Thresholds

Synapses in the cortex use Hebbian Learning. Neurons in the cortex use a constant activation threshold to discriminates between cells which recognize their inputs and cells which do not. Naive models of the cortex incorporated just these two features and observed that Hebbian learning has two characteristic failure conditions.

- If the threshold is too high then cells never activate. Inactive cells do not learn, and so they never form additional synapses with which they might overcome the activation threshold. These cells are stuck-off.
- If the threshold is too low then cells activate, learn and form new synapses, which makes them more likely to activate. This can lead to lead to run away activity, where all cells activate at the same time, or a cell activates in response to everything. These cells are stuck-on.

Inhibitory cells in the cortex facilitate a competition between neurons. The competition augments the activation threshold be raising it such that only a small fraction of the strongest neurons activate. The competition can raise the threshold as high as it needs to, allowing it to scale to any number of neurons with any number of inputs. Models which include a competition do not suffer from the same characteristic failures of the naive models. In general, hebbian learning works better with a competition than with only a constant threshold.

There is a component of HTM models which uses hebbian learning with only a constant threshold: synapses. The permanence value of a synapse is controlled by hebbian learning. A simple constant threshold discriminates between potentially and actually connected synapses. Synapses suffer from failure conditions which are characteristic of hebbian learning combined with only a constant threshold.

# Methods of Analysis

I trained a Spatial Pooler to recognize the handwritten digits 0-9 in the MNIST dataset. All experiments scored between 95% and 96% accuracy, and changes in accuracy as a result of these experiments were insignificant.

I measured the number of connected synapses on each segment. This experiment seeks to control this number. Segments have at most 73 potentially connected synapses, meaning that regardless of any experimental modifications the maximum number of connected synapses is 73.

I measured the activation frequencies of each neuron. Activation frequencies are reported in graphs as a fraction between 0 and 1. The Spatial Pooler enforces a sparsity of 1% cell activations, which means that the average activation frequency across all cells in the Spatial Pooler will also be 1%.

I calculated the binary entropy of the activations, which is a measure of how much information the cells are transmitting. The entropy is reported as a percent of the theoretical maximum entropy of the system with the sparsity held constant. Higher entropy is better.

# Run Away Hebbian Learning

In this section I demonstrate the failures which are characteristic of Hebbian learning combined with a constant threshold.

Figure 1: Histogram of the number of connected synapses per segment, in a Spatial Pooler with no constraints on the number of connected synapses per segment. Notice that some segments have very few synapses (4) and that some segments have connected every potential synapse (73).

Figure 2: Histogram of cell activation frequencies, in a Spatial Pooler with no constraints on the number of connected synapses per segment. Notice that a significant number of segments are underutilized / stuck-off, having close to zero activations. A small minority of segments are overutilized / stuck-on, activating significantly more often than the average activation frequency of 1%.

# Model of Synapse Competition

I modified a Spatial Pooler such that the number of connected synapses on each proximal segment is constrained. I introduce two new global parameters: minimumSynapsesPerSegment and maximumSynapsesPerSegment. Whenever a segment has too few or too many connected synapses the permanence values of all synapses on the segment are changed uniformly such that the segment has a valid number of connected synapses. The permanence change is calculated to be the smallest possible change which achieves the desired effect.

# Results

I implemented the model of synapse competition and I hope to eventually contribute it to the community fork of Nupic. I experimented with several different parameter sets, shown here:

Minimum Connected Synapses Per Segment | 0 (No Limit) | 15 | 20 | 30 |
---|---|---|---|---|

Maximum Connected Synapses Per Segment | 73 (No Limit) | 50 | 40 | 35 |

Maximum Cell Activation Frequency | 12% | 9.3% | 6.8% | 3.1% |

Binary Entropy | 87% | 91% | 94% | 96% |

Figure 3: Data table of results. Notice that as the constraints of the synapse competition are tightened, the entropy increases.

Figure 4: Histogram of cell activation frequencies, in a Spatial Pooler with the constraint that the number of connected synapses per segment is between 30 and 35. Notice that significantly fewer cells are underutilized and that no cells are overutilized.

# Effect of Synapse Competition on Permanences

Figure 5: Histogram of synapse permanence values, in a Spatial Pooler with the constraint that the number of connected synapses per segment is between 30 and 35. The red line indicates the connected threshold for synapses. All synapses to the right of the red line are connected. All synapses to the left of the red line are disconnected.

Notice the small bump which coincides with the connected threshold. This bump exists on both sides of the threshold. This bump is only present when the number of connected synapses per segment is constrained (evidence not shown). This bump is caused by synapses which lost their competition and are trying to cross the threshold, but which are being held back by the new competition rules. These synapses could be either trying to connect or disconnect, and in both cases theyâ€™re unable to.

Thank you for reading! I look forward to your comments and questions.