2009/02/22 by Casey O. Diekman, Kohinoor Dasgupta, Diekman, Casey +6
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Cell Image Analysis Techniques #Databases (cs.DB) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Methodology (stat.ME) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Neuroscience and Neural Engineering #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.0902.3725
openalex publication_date 2009/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying the spatio-temporal network structure of brain activity from multi-neuronal data streams is one of the biggest challenges in neuroscience. Repeating patterns of precisely timed activity across a group of neurons is potentially indicative of a microcircuit in the underlying neural tissue. Frequent episode discovery, a temporal data mining framework, has recently been shown to be a computationally efficient method of counting the occurrences of such patterns. In this paper, we propose a framework to determine when the counts are statistically significant by modeling the counting process. Our model allows direct estimation of the strengths of functional connections between neurons with improved resolution over previously published methods. It can also be used to rank the patterns discovered in a network of neurons according to their strengths and begin to reconstruct the graph structure of the network that produced the spike data. We validate our methods on simulated data and present analysis of patterns discovered in data from cultures of cortical neurons.