A survey of robotics control based on learning-inspired spiking neural networks

Z Bing, C Meschede, F Röhrbein, K Huang… - Frontiers in …, 2018 - frontiersin.org
Biological intelligence processes information using impulses or spikes, which makes those
living creatures able to perceive and act in the real world exceptionally well and outperform …

[HTML][HTML] DARPA-funded efforts in the development of novel brain–computer interface technologies

RA Miranda, WD Casebeer, AM Hein, JW Judy… - Journal of neuroscience …, 2015 - Elsevier
Abstract The Defense Advanced Research Projects Agency (DARPA) has funded innovative
scientific research and technology developments in the field of brain–computer interfaces …

Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework

HF Song, GR Yang, XJ Wang - PLoS computational biology, 2016 - journals.plos.org
The ability to simultaneously record from large numbers of neurons in behaving animals has
ushered in a new era for the study of the neural circuit mechanisms underlying cognitive …

[HTML][HTML] Brain-inspired learning in artificial neural networks: a review

S Schmidgall, R Ziaei, J Achterberg, L Kirsch… - APL Machine …, 2024 - pubs.aip.org
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle

T Genewein, F Leibfried, J Grau-Moya… - Frontiers in Robotics …, 2015 - frontiersin.org
Abstraction and hierarchical information processing are hallmarks of human and animal
intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving …

A consecutive hybrid spiking-convolutional (CHSC) neural controller for sequential decision making in robots

V Azimirad, MT Ramezanlou, SV Sotubadi… - Neurocomputing, 2022 - Elsevier
In this paper, a Consecutive Hybrid Spiking-Convolutional (CHSC) neural controller is
proposed by integrating Convolutional Neural Networks (CNNs) and Spiking Neural …

Local dynamics in trained recurrent neural networks

A Rivkind, O Barak - Physical review letters, 2017 - APS
Learning a task induces connectivity changes in neural circuits, thereby changing their
dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural …

A neural model of hierarchical reinforcement learning

D Rasmussen, A Voelker, C Eliasmith - PloS one, 2017 - journals.plos.org
We develop a novel, biologically detailed neural model of reinforcement learning (RL)
processes in the brain. This model incorporates a broad range of biological features that …

Review of closed-loop brain–machine interface systems from a control perspective

H Pan, H Song, Q Zhang, W Mi - IEEE Transactions on Human …, 2022 - ieeexplore.ieee.org
In recent years, brain–machine interface (BMI) technology has made great progress in
controlling external devices and restoring motor function for people with disabilities. To …

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

S Dura-Bernal, SA Neymotin, CC Kerr… - IBM Journal of …, 2017 - ieeexplore.ieee.org
Biomimetic simulation permits neuroscientists to better understand the complex neuronal
dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis …