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Found 6 papers, 4 papers with code
Measuring Exploration in Reinforcement Learning via Optimal Transport in Policy Space
Exploration is the key ingredient of reinforcement learning (RL) that determines the speed and success of learning.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Dynamical learning of dynamics
The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification.
Neurons and Cognition
Non-linear motor control by local learning in spiking neural networks
Here, we employ a supervised scheme, Feedback-based Online Local Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking neurons with hidden layers, to control a two-link arm so as to reproduce a desired state trajectory.
Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory
Learning and memory are intertwined in our brain and their relationship is at the core of several recent neural network models.
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics, while an online and local rule changes the weights.
