Robust Dendritic Computations With Sparse Distributed Representations
Cortex encodes information with sparse distributed patterns. How accurately can neurons recognize sparse patterns?
This poster is very similar to a poster we presented at Cosyne 2018 in March on the same topic of sparse distributed representations. It examines how accurately neurons can recognize these sparse patterns by showing:
- Scaling laws for computing error probabilities.
- High dimensional sparse patterns can be classified extremely reliably, even with large amounts of noise.
- Active dendritic segments can reliably classify patterns using a tiny number of synapses.
- The equations explain experimentally observed NMDA spike thresholds in active dendrites.
- Behavior of Poirazi-Mel and HTM neuron models closely match theoretical predictions. Understanding the behavior can lead to dramatically improved accuracies