A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data
Shi, J.; Buice, M. A.; Shea-Brown, E.; Mihalas, S.; Tripp, B. P.
Show abstract
Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connec-tome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CNN MouseNet: A biologically constrained convolutional neural network model for mouse visual cortex 98%
- How well do models of visual cortex generalize to out of distribution samples? 96%
- Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.