Neurons encode and transmit information in spike sequences. However, despite the effort devoted to quantify their information content, little progress has been made in this regard. Here we use a nonlinear method of time-series analysis (known as ordinal analysis) to compare the statistics of spike sequences generated by applying an input signal to the neuronal model of Morris-Lecar. In particular we consider two different regimes for the neurons which lead to two classes of excitability: class I, where the frequency-current curve is continuous and class II, where the frequency-current curve is discontinuous.
By applying ordinal analysis to sequences of inter-spike-intervals (ISIs) our goals are (1) to investigate if different neuron types can generate spike sequences which have similar symbolic properties;
(2) to get deeper understanding on the effects that electrical (diffusive) and excitatory chemical (i.e., excitatory synapse) couplings have; and (3) to compare, when a small--amplitude periodic signal is applied to one of the neurons, how the signal features (amplitude and frequency) are encoded and transmitted in the generated ISI sequences for both class I and class II type neurons and electrical or chemical couplings. We find that depending on the frequency, specific combinations of neuron/class and coupling-type allow a more effective encoding, or a more effective transmission of the signal.
Phase-based spatial ordinal patterns for characterizing oscillatory dynamics
University of Lethbridge（莱斯布里奇大学）；Federal University of Parana（巴拉那联邦大学）；Interdisciplinary Center for Science, Technology and Innovation (CICTI), Federal University of Parana（巴拉那联邦大学跨学科科学技术创新中心（CICTI））；Institute of Science and Technology, Federal University of São Paulo (UNIFESP)（圣保罗联邦大学科学与技术研究所）；Fields Institute（菲尔兹研究所）
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
Institute of Technologies and Management of Digital Transformation（数字化转型技术与管理研究所）；Institute of Information Management in Mechanical Engineering（机械工程信息管理研究所）
Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows
Institute for Advanced Simulation (IAS-6), Jülich Research Centre（先进模拟研究所）；RWTH Aachen University（亚琛工业大学）；Rappaport Faculty of Medicine and Network Biology Research Laboratory, Technion - Israel Institute of Technology（拉帕波特医学院及网络生物学研究实验室，海法理工学院）；Department of Physics, Faculty 1, RWTH Aachen University（亚琛工业大学第一物理系）；International School of Advanced Studies (SISSA)（高等国际研究学院）；Department of Physics, University of Ottawa（渥太华大学物理系）；Department of Computer Science, RWTH Aachen University（亚琛工业大学计算机科学系）
