Neural representation of action sequences: how far can a simple snippet-matching model take us?

Neural representation of action sequences: how far can a simple snippet-matching model take us?
Title:
Neural representation of action sequences: how far can a simple snippet-matching model take us?
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Advances in Neural Information Processing Systems 26
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Publication Date:
07 May 2021
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NIPS2013_5052, Neural representation of action sequences: how far can a simple snippet-matching model take us? Tan, Cheston and Singer, Jedediah M and Serre, Thomas and Sheinberg, David and Poggio, Tomaso. Advances in Neural Information Processing Systems 26 pg 593--601, 2013
Abstract:
The macaque Superior Temporal Sulcus (STS) is a brain area that receives and integrates inputs from both the ventral and dorsal visual processing streams (thought to specialize in form and motion processing respectively). For the processing of articulated actions, prior work has shown that even a small population of STS neurons contains sufficient information for the decoding of actor invariant to action, action invariant to actor, as well as the specific conjunction of actor and action. This paper addresses two questions. First, what are the invariance properties of individual neural representations (rather than the population representation) in STS? Second, what are the neural encoding mechanisms that can produce such individual neural representations from streams of pixel images? We find that a simple model, one that simply computes a linear weighted sum of ventral and dorsal responses to short action “snippets”, produces surprisingly good fits to the neural data. Interestingly, even using inputs from a single stream, both actor-invariance and action-invariance can be accounted for, by having different linear weights.
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