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

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.

New Trends in Biologically-Inspired Audio Coding

“…Patnaik et al (Patnaik et al, 2008) extended previous results to the processing of neurophysiological data. The frequent episode discovery fits in the general paradigm of temporal data mining.…”

“…However, for the time being we proposed a solution to a simplified version of the aforementioned problem in (Pichevar & Najaf-Zadeh, 2009). In fact, we propose to extract "channel-based or frequency-domain patterns" in our generated spikegrams using temporal data mining (Mannila et al, 1997) (Patnaik et al, 2008). Since these patterns are repeated frequently in the signal and are the building blocks of the audio signal, we may call them auditory objects (Bregman, 1994).…”

Temporal data mining approaches for sustainable chiller management in data centers

“…In a refinement step all candidate motif subsequences are compared using a distance metric to find the set of motifs with the highest number of nontrivial matches. A contrasting framework, referred to as frequent episode discovery, is an event-based framework that is most applicable to symbolic data that is not uniformly sampled [Laxman et al 2005[Laxman et al , 2008Mannila et al 1997;Patnaik et al 2008]. This enables the introduction of junk, or "don't care" states, into the definition of what constitutes a frequent episode.…”

Accelerator-Oriented Algorithm Transformation for Temporal Data Mining

“…The datasets used here are generated from the nonhomogeneous Poisson process model for inter-connected neurons described in [2]. This simulation model generates fairly realistic spike train data.…”

“…Algorithms such as frequent episode discovery [1], [2], in particular, are adept at discovering patterns in neuronal spike train data using multi-electrode arrays (MEAs; shown in Fig. 1).…”