Publications by authors named "L A Smirnov"
We study a bifurcation scenario that corresponds to the transition from bursting activity to spiking in a phenomenological model of neuron-astrocyte interaction in neuronal populations. In order to do this, we numerically obtain one-dimensional Poincaré return map and highlight its bifurcation structure using an analytically constructed piecewise smooth model map. This map reveals the existence of a cascade of period-adding bifurcations, leading to a bursting-spiking transition via blue sky catastrophe.
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- The study demonstrates the application of machine learning for classifying topological phases in finite leaky photonic lattices using minimal measurement data.
- The proposed method relies on a single real-space bulk intensity image, avoiding complex phase retrieval techniques.
- A fully connected neural network is designed to identify topological properties from intensity distributions in waveguide arrays after a localized excitation, mimicking realistic experimental conditions.
Article Synopsis
- * The authors transform a complex network of integrate-and-fire neurons into a simplified model (Kuramoto-Sakaguchi), allowing them to analyze how synaptic characteristics influence neuron firing patterns.
- * They identify conditions for synchronous and partially synchronous firing based on synaptic activation rates and delays, and their findings suggest potential for further research on rhythm generation in adaptive neural networks.
We explore large populations of phase oscillators interacting via random coupling functions. Two types of coupling terms, the Kuramoto-Daido coupling and the Winfree coupling, are considered. Under the assumption of statistical independence of the phases and the couplings, we derive reduced averaged equations with effective non-random coupling terms.
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- - Cyclops states are unique patterns found in oscillator networks, where two coherent clusters exist alongside a single oscillator, as introduced by Munyaev et al. in 2023.
- - The study extends previous work to analyze how cyclops states can be disrupted and transformed into new dynamic patterns known as breathing and switching cyclops states, using Kuramoto-Sakaguchi networks as a model.
- - Results show that these new states are common across various coupling conditions and can withstand significant frequency differences, suggesting they may also occur in other biological and physical systems like coupled theta neurons.