Computational and Systems Neuroscience (Cosyne) 2015
Maintaining Stable Perception During Active Exploration
Our sensory input changes dramatically as the result of our own behavior, including eye movements, head turns, and body movements. Despite these rapid sensory changes, our perception of the world is amazingly stable, and we can reliably discriminate between different patterns. This suggests that we learn stable but distinct representations through active exploration. There is reason to believe that efference copy, an internal copy of the motor signal, is critical for such sensorimotor learning. However the exact brain mechanisms underlying these computations remain unknown. In this study, we propose a computational model of sensorimotor learning and prediction. Sparse distributed representations of visual scenes are built up incrementally by pooling together predictable temporal transitions during exploration. To enable accurate predictions during active exploration, we modified the Hierarchical Temporal Memory sequence-learning algorithm to use both sensory inputs and efference copy signals. To enable forming stable representations of scenes, we implemented a novel temporal pooling learning rule that allows downstream neurons to form connections with upstream neurons that are predicting correctly.
The overall model is unsupervised and the architecture is consistent with several important aspects of thalamocortical circuits. We tested the algorithm on a set of simulated environments, as well as a robotics test bed. In both cases the model achieves two desired properties: 1) prediction of future sensory inputs during behavior, and 2) emergence of stable and distinct representations for learned patterns. After learning, the sparse activity of cells in downstream regions is stable despite sensor movements, while different images lead to distinct representations. These results demonstrate how efference copy can be used in sensory cortex to make predictions during behavior. We propose temporal pooling as a novel computational principle for forming invariant representations during unsupervised learning and active exploration.