122 citations · 148 across the 4 of their papers we have counts for
6 papers
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs
Furkan Ozcelik, Bhavin Choksi, Milad Mozafari +2
Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research. Prior studies had success in reconstructing either the low…
On the role of feedback in visual processing: a predictive coding perspective
Andrea Alamia, Milad Mozafari, Bhavin Choksi +1
Brain-inspired machine learning is gaining increasing consideration, particularly in computer vision. Several studies investigated the inclusion of top-down feedback connections in…
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
Bhavin Choksi, Milad Mozafari, Callum Biggs O'May +3
Deep neural networks excel at image classification, but their performance is far less robust to input perturbations than human perception. In this work we explore whether this shor…
Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN
Milad Mozafari, Leila Reddy, Rufin VanRullen
Decoding and reconstructing images from brain imaging data is a research area of high interest. Recent progress in deep generative neural networks has introduced new opportunities…
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini +1
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and en…
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini +2
The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less en…