A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents
Neural network simulation is an important tool for generating and evaluating hypotheses on the structure, dynamics, and function of neural circuits. For scientific questions addressing organisms operating autonomously in their environments, in particular where learning is involved, it is crucial to...
Main Authors: | Jakob Jordan, Philipp Weidel, Abigail Morrison |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2019-08-01
|
Series: | Frontiers in Computational Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2019.00046/full |
Similar Items
-
Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks
by: Philipp Weidel, et al.
Published: (2021-03-01) -
Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS
by: Philipp Weidel, et al.
Published: (2016-08-01) -
Editorial: Closed-Loop Systems for Next-Generation Neuroprostheses
by: Timothée Levi, et al.
Published: (2018-02-01) -
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
by: Bruno Golosio, et al.
Published: (2023-08-01) -
Closed-loop control of a noisy qubit with reinforcement learning
by: Yongcheng Ding, et al.
Published: (2023-01-01)