A survey of robotics control based on learning-inspired spiking neural networks
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 …
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 …
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
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 …
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
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …
achieving remarkable success across diverse domains, including image and speech …
Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle
Abstraction and hierarchical information processing are hallmarks of human and animal
intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving …
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
In this paper, a Consecutive Hybrid Spiking-Convolutional (CHSC) neural controller is
proposed by integrating Convolutional Neural Networks (CNNs) and Spiking Neural …
proposed by integrating Convolutional Neural Networks (CNNs) and Spiking Neural …
Local dynamics in trained recurrent neural networks
Learning a task induces connectivity changes in neural circuits, thereby changing their
dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural …
dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural …
A neural model of hierarchical reinforcement learning
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 …
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 …
controlling external devices and restoring motor function for people with disabilities. To …
Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis
Biomimetic simulation permits neuroscientists to better understand the complex neuronal
dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis …
dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis …