Deep Reinforcement Learning and its Neuroscientific Implications

Deep Reinforcement Learning and its Neuroscientific Implications

7 July 2020

Kevin J. Miller

Z. Kurth-Nelson

Papers citing "Deep Reinforcement Learning and its Neuroscientific Implications"

12 / 12 papers shown

Title
A recurrent vision transformer shows signatures of primate visual attention Jonathan Morgan Badr Albanna James P. Herman 34 0 0 16 Feb 2025
Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding S. Harnad 19 12 0 03 Feb 2024
Brain-inspired learning in artificial neural networks: a review Samuel Schmidgall Jascha Achterberg Thomas Miconi Louis Kirsch Rojin Ziaei S. P. Hajiseyedrazi Jason Eshraghian 28 52 0 18 May 2023
Rejecting Cognitivism: Computational Phenomenology for Deep Learning P. Beckmann G. Köstner Ines Hipólito 27 4 0 16 Feb 2023
A Rubric for Human-like Agents and NeuroAI Ida Momennejad 52 14 0 08 Dec 2022
Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations Baihan Lin AI4CE 23 8 0 24 Oct 2022
What deep reinforcement learning tells us about human motor learning and vice-versa Michele Garibbo Casimir J. H. Ludwig Nathan Lepora Laurence Aitchison 16 0 0 23 Aug 2022
Improving the Level of Autism Discrimination through GraphRNN Link Prediction Haonan Sun Q. He Shouliang Qi Yudong Yao Yueyang Teng 17 0 0 19 Feb 2022
Successor Feature Neural Episodic Control David Emukpere Xavier Alameda-Pineda Chris Reinke BDL 14 4 0 04 Nov 2021
Meta-learning in natural and artificial intelligence Jane X. Wang 19 110 0 26 Nov 2020
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks Wei Fang Zhaofei Yu Yanqing Chen T. Masquelier Tiejun Huang Yonghong Tian 47 503 0 11 Jul 2020
Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers Baihan Lin 16 2 0 27 Feb 2019