8 citations · 23 across the 6 of their papers we have counts for
6 papers
Neural Associative Skill Memories for safer robotics and modelling human sensorimotor repertoires
Pranav Mahajan, Mufeng Tang, T. Ed Li +2
Modern robots face challenges shared by humans, where machines must learn multiple sensorimotor skills and express them adaptively. Equipping robots with a human-like memory of how…
Bidirectional predictive coding
Gaspard Oliviers, Mufeng Tang, Rafal Bogacz
Predictive coding (PC) is an influential computational model of visual learning and inference in the brain. Classical PC was proposed as a top-down generative model, where the brai…
Learning grid cells by predictive coding
Mufeng Tang, Helen Barron, Rafal Bogacz
Grid cells in the medial entorhinal cortex (MEC) of the mammalian brain exhibit a strikingly regular hexagonal firing field over space. These cells are learned after birth and are…
Benchmarking Predictive Coding Networks -- Made Simple
Luca Pinchetti, Chang Qi, Oleh Lokshyn +9
In this work, we tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. To do so, we propose a library, called PCX, that focus…
Sequential Memory with Temporal Predictive Coding
Mufeng Tang, Helen Barron, Rafal Bogacz
Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains…
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks
Mufeng Tang, Yibo Yang, Yali Amit
We develop biologically plausible training mechanisms for self-supervised learning (SSL) in deep networks. Specifically, by biological plausible training we mean (i) All updates of…