Inferring Neuronal Network Connectivity from Spike Data: A Temporal Datamining Approach

TL;DR

This paper introduces a novel data mining approach using frequent episode discovery to infer neuronal connectivity patterns from multi-neuronal spike train data, demonstrating its effectiveness through simulation studies.

Contribution

It applies temporal data mining techniques, specifically frequent episode discovery under temporal constraints, to the problem of inferring neural network connectivity from spike data.

Findings

Effective identification of connectivity patterns from simulated data

Demonstrated utility of temporal constraints in episode mining

Algorithms for serial and parallel episode discovery

Abstract

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.