Spike-Train Communities: Finding Groups of Similar Spike Trains

Abstract

Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.

Detecting cell assemblies in large neuronal populations

“…The methods reviewed here can be applied to large neuronal populations, and neuronal activity is assessed as a whole through the use of eigenvalue analysis. Additionally, the Marčenko-Pastur distribution opens the possibility of using an analytical and reliable statistics instead of surrogate methods employed in previous frameworks (Abeles, 2009;Abeles and Gat, 2001;Abeles and Gerstein, 1988;Humphries, 2011;Maldonado et al, 2008;Shmiel et al, 2006). In addition to being computationally demanding, a problem inherent to the use of surrogates is the fact that there is no consensus about which statistical properties should be preserved in these control data (Berger et al, 2010;Grun, 2009).…”

“…By consequence, PCA-based methods would falsely identify two assemblies in the network, while only one pair of neurons is correlated. It should be noted that other methods, such as the one described in Humphries (2011), can be adapted to other measures of spike train similarity besides linear correlations.…”

Recent developments in VSD imaging of small neuronal networks

“…Many methods have been developed to partition data sets into ensembles of neurons having significantly correlated firing patterns (Feldt et al 2009;Humphries 2011;Lopes-dos-Santos et al 2011;Lyttle and Fellous 2011;Gerstein et al 2012). Our laboratory has used an unsupervised graph theoretic-based clustering approach to reveal the existence of physically segregated ensembles of neurons that are re-identifiable across preparations during the Aplysia locomotion motor program ( Fig.…”

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

“…The past decades have seen the arrival of many methods that can characterize spike timing networks (Abeles and Gerstein, 1988; Chapin and Nicolelis, 1999; Nádasdy et al, 1999; Tetko and Villa, 2001; Grün et al, 2002; Lee and Wilson, 2002; Schnitzer and Meister, 2003; Ikegaya et al, 2004; Okatan et al, 2005; Schneider et al, 2006; Nikolíc, 2007; Pipa et al, 2008; Schrader et al, 2008; Berger et al, 2010; Eldawlatly et al, 2010; Louis et al, 2010; Peyrache et al, 2010; Humphries, 2011; Lopes-dos-Santos et al, 2011; Gansel and Singer, 2012; Torre et al, 2016). Their application has led to important insights, yet they have several limitations, especially when it comes to their application on large scale neuronal recordings (Buzsáki, 2004).…”