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Found 9 papers, 3 papers with code
The role of gap junctions and clustered connectivity in emergent synchronisation patterns of inhibitory neuronal networks
Conversely, the strength of inhibitory synapses affects the population firing rate, suggesting that electrical and chemical coupling strengths act as complementary mechanisms by which networks can tune synchronous oscillatory behavior.
Modelling crypto markets by multi-agent reinforcement learning
Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent
Homeostasis is a biological process by which living beings maintain their internal balance.
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective
Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess the benefits of these developments.
A new generation of reduction methods for networks of neurons with complex dynamic phenotypes
Collective dynamics of spiking networks of neurons has been of central interest to both computation neuroscience and network science.
Activity stabilization in a population model of working memory by sinusoidal and noisy inputs
If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase.
Adjoint Method for Macroscopic Phase-Resetting Curves of Generic Spiking Neural Networks
In the thermodynamic limit, the network dynamics are captured by a continuity equation known as the refractory density equation.
Linear encoding of the spatiotemporal cat
The dynamics of an extended, spatiotemporally chaotic system might appear extremely complex.
Chaotic Dynamics Statistical Mechanics High Energy Physics - Lattice Mathematical Physics Mathematical Physics 28D05, 60J10, 70K43, 70S05, 76F20, 82C20
Spikebench: An open benchmark for spike train time-series classification
Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks.
