Abstract
With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.
Highlights
Synchronous input spikes to a receiving neuron are considered most effective in generating an output spike, as predicted by theoretical studies coining the term coincidence detector [1]
Animal studies provide experimental evidence that spike synchronization occurs in relation to behavior
The long-standing lack of simultaneous recordings of large populations of neurons, which only recently were enabled by advances in recording technology
Summary
Synchronous input spikes to a receiving neuron are considered most effective in generating an output spike, as predicted by theoretical studies coining the term coincidence detector [1]. Experimental studies provide evidence for the existence of coincidence detectors (e.g., [2]) and relate them to various mechanisms of spike-timing dependent plasticity [3, 4] as well as to different encoding and decoding schemes [5, 6]. [7]) and project to a similar number of other cells Such a connectivity structure combined with suitable synaptic delays may produce spatio-temporal spike patterns, as proposed in [8,9,10,11]. A simple case is represented by a temporal sequence of synchronous events (SSE), each event consisting of synchronous spikes from a group of neurons
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