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Found 4 papers, 1 papers with code
Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e. g., from sensory systems).
Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks
In computational models, self-coordination in swarms has been implemented using probabilistic or simple action rules to shape the decision of each agent and the collective behavior.
Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest.
Generalised learning of time-series: Ornstein-Uhlenbeck processes
The performance of the primary models are evaluated simultaneously bycomputing the deviations from the originally removed data points and out-of-sample (OSS) data. Full cross-validation in time-series models can be practiced with rCV along with generating learning curves.
