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Main Menu - Block
- Overview
- Anatomy and Histology
- Cryo-Electron Microscopy
- Electron Microscopy
- Flow Cytometry
- Gene Targeting and Transgenics
- Immortalized Cell Line Culture
- Integrative Imaging
- Invertebrate Shared Resource
- Janelia Experimental Technology
- Mass Spectrometry
- Media Prep
- Molecular Genomics
- Primary & iPS Cell Culture
- Project Pipeline Support
- Project Technical Resources
- Quantitative Genomics
- Scientific Computing Software
- Scientific Computing Systems
- Viral Tools
- Vivarium
Abstract
Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance comes at the expense of simplicity because the ANN models typically have many hidden layers with many feature maps in each layer. Here we show that ANN models of V1 can be substantially simplified while retaining high predictive power. To demonstrate this, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting a separate "minimodel" to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that these relatively simple models can nonetheless be useful for tasks such as object and visual texture recognition and we use the models to gain insight into how texture invariance arises in biological neurons.
