Neural Encoding model for Macaque V4. The model is a combination of a data driven core and an attention readout layer.
This model is a combination of a data driven core and an attention readout layer.
The data driven core is a convolutional neural network and the attention readout layer is a multihead attention layer with each head trained to predict the firing rates of a neuron in Macaque V4.
For research purposes, we recommend our
nnvision Github repository (
https://github.com/sinzlab/nnvision), which contains the code for the model defintions and training.
The model is intended for research purposes only.
The model can be used to predict the firing rates of neurons in Macaque V4 given an image.
1import torch
2from nnvision.models.trained_models.v4_data_driven import v4_multihead_attention_ensemble_model
3
4input_image = torch.rand(1, 100, 100)
5firing_rate = v4_multihead_attention_ensemble_model(input_image, data_key="all_sessions")
1from egg.models import models
2model = models['data_driven']['train']
