Theory of the Snowflake Plot and Its Relations to Higher-Order Analysis Methods

DOI10.1162/0899766053723041zbMath1064.92007OpenAlexW2151694082WikidataQ81759546 ScholiaQ81759546MaRDI QIDQ3025066

Satish Iyengar, Gabriella Czanner, Sonja Grün

Publication date: 4 July 2005

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/0899766053723041

Mathematics Subject Classification ID

Neural biology (92C20) Graphical methods in statistics (62A09)

Related Items (8)

NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events ⋮ Information-Geometric Measures as Robust Estimators of Connection Strengths and External Inputs ⋮ Exact Solutions for Rate and Synchrony in Recurrent Networks of Coincidence Detectors ⋮ First-Spike Latency in the Presence of Spontaneous Activity ⋮ Influence of External Inputs and Asymmetry of Connections on Information-Geometric Measures Involving Up to Ten Neuronal Interactions ⋮ Methods for identification of spike patterns in massively parallel spike trains ⋮ Unnamed Item ⋮ Information-Geometric Measures for Estimation of Connection Weight Under Correlated Inputs

Cites Work

• Analysis of higher-order neuronal interactions based on conditional inference
• Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance
• Higher-Order Statistics of Input Ensembles and the Response of Simple Model Neurons
• Information-Geometric Measure for Neural Spikes

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