The document describes a sensorimotor inference system that uses neural networks. Layer 4 learns sensory-motor transitions from copies of motor commands and sensory input. Layer 3 pools the representations from Layer 4 into more stable representations by learning higher-order transitions. The system is able to predict and adapt to changes in sensory input based on what was predicted from the motor commands.

1. A Whole New World
Chetan Surpur, Yuwei Cui
NuPIC Hackathon – October 18-19, 2014

2. Sensorimotor Inference
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Sensory Input
Copy of motor
commands
IR sensor
Learns high-order transitions Stable
Learns sensory-motor transitions
Predicted
Pass through
changes
Un-predicted
Layer 4 learns sensory-motor transitions.
Scalar
Encoder
Motor Layer 3 pools into stable representations.
Scalar
Encoder