Abstract
A subset of visually sensitive neurons in the parietal lobe apparently can encode the locations of stimuli, whereas visually sensitive neurons in the inferotemporal cortex (area IT) cannot. This finding is puzzling because both sorts of neurons have large receptive fields, and yet location can be encoded in one case, but not in the other. The experiments reported here investigated the hypothesis that a crucial difference between the IT and parietal neurons is the spatial distribution of their response profiles. In particular, IT neurons typically respond maximally when stimuli are presented at the fovea, whereas parietal neurons do not. We found that a parallel-distributed-processing network could map a point in an array to a coordinate representation more easily when a greater proportion of its input units had response peaks off the center of the input array. Furthermore, this result did not depend on potentially implausible assumptions about the regularity of the overlap in receptive fields or the homogeneity of the response profiles of different units. Finally, the internal representations formed within the network had receptive fields resembling those found in area 7a of the parietal lobe.
Highlights
For many years it was the fashion in neuroscience to use common sense to analyze the functional properties of neurons
To the extent that neurons in area 7a have receptive fields whose peaks are distributed off the fovea, these results suggest one reason why the parietal lobe is better suited to encoding spatial location
We hypothesized that overlapping receptive fields could encode spatial location via coarse coding, but only if the units have different locations of maximal response
Summary
For many years it was the fashion in neuroscience to use common sense to analyze the functional properties of neurons. If a neuron responded well to faces, some inferred that it was a “face detector.”. This reasoning is incomplete and misleading, in part because it ignores the role the neuron plays in the context of other neurons (e.g., see Van Essen 1985). In this article we explore an example of another way of analyzing the functional properties of neurons: Given that neurons produce specific output on receiving specific input from other neurons, they can be thought of as performing computations,which are systematic mappings of input to output that transform the input or operate on it in some way (cf Marr 1982). Attempting to characterize neural function as computation leads one to ask much more detailed questions about neural information processing than were asked previously, and can produce insights into nonobvious properties of the specific mappings performed by neural systems
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