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Abstract The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.
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Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models?
Many details are known about microcircuitry in visual cortices. For example, neurons have supralinear activation functions, they're either excitatory (E) or inhibitory (I), connection strengths fall off with distance, and the output cells of an area are excitatory. This circuitry is important as it's believed to support core functions such as normalization and surround suppression. Yet, multi-area models of the visual processing stream don't usually include these details. Here, we introduce known-features of recurrent processing into the architecture of a convolutional neural network and observe how connectivity and activity change as a result. We find that certain E-I differences found in data emerge in the models, though the details depend on which architectural elements are included. We also compare the representations learned by these models to data, and perform analyses on the learned weight structures to assess the nature of the neural interactions.
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- PAR ID:
- 10213175
- Date Published:
- Journal Name:
- 2019 Conference on Cognitive Computational Neuroscience
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.32470/CCN.2019.1175-0
