Shirin Dora

This study uses recurrent neural networks to model multisensory decision-making and finds that behavioral variability arises from network-specific strategies and modality-selectivity.

This paper develops a spiking neural network for predictive coding that generates hierarchical internal representations and reconstructs unseen data, suggesting biologically plausible mechanisms for perceptual inference.

The paper develops a deep predictive coding network trained with Hebbian learning that exhibits neural response properties similar to experimental findings in the visual cortex.

This paper introduces a deep predictive coding network that learns latent representations from real-world images in an unsupervised manner, successfully inferring representations for novel objects.