This work computationally extracts population codes from macaque electrophysiology datasets spanning V4 and IT cortex and vision models, introduces an automated method to measure representation interpretability and diversity, and demonstrates through targeted ablations that interpretable representations play a causal role in downstream behavior.
A dual coding architecture in anterior IT is suggested: shared low-dimensional structure supports category generalization, whereas sparse and reliably feature-random single-neuron responses expand the representational space and support efficient identification of individual visual inputs.
Qi Lu, Xin-Yi Xiong, Yang Li et al.· bioRxiv· 0 citations
ACNet, a foundation model of cortical sound encoding, is developed to predict the time-varying activity of >3000 neurons in AC of ferrets, and the emergence of rate-based, sparse sound coding across layers, a prominent feature of the auditory cortex is revealed.
Satyabrata Parida, Jereme C. Wingert, Jonah D. Stickney et al.· bioRxiv· 0 citations
The developed “reverse correlation against stimulus elements (RCASE)” approach enables rapid and interpretable functional characterization of large populations of visual neurons, enabling to simultaneously recover receptive fields and feature tuning, whenever a low-dimensional feature space can be specified beforehand.
Marc Büttner, M. Žnidarič, Roland Diggelmann et al.· bioRxiv· 0 citations
It is suggested that the central challenge is not to choose between direct and distributed codes, but to determine when geometric descriptions illuminate mechanism and when they merely rename it.
M. Shadlen· Current Opinion in Neurobiol...· 1 citation
Visual recognition requires stable representations of object identity despite large changes in the retinal image. Yet this stability must be selective: some transformations change not only how an image looks, but also what object it appears to depict. Visual anagrams highlight this challenge: a single image can resembl...
Yi-Peng Li, Rui Zhang, Jing-Qiu Luo et al.· bioRxiv· 0 citations
This survey reviews the geometry, learning, and computation of superposed representations, explaining how feature statistics and decoder choice affect the conclusions and compares practical methods for recovering and analyzing features.
Dai Shi, Xiao-Yu Li, Andi Han et al.· 0 citations
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