Abstract
Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.
Highlights
An open question in neuroscience is whether neuronal activity is organized in spatio-temporal patterns (STPs) of millisecondprecise spikes to represent and process information
We presented above two different techniques to distinguish between chance patterns and selected STPs, based on stability measures and based on statistical significance of signatures (SPADE), respectively
We first compare the computational efficiency of the components of the method by Yegenoglu et al (2016) to the proposed components introduced in the section above (FP-growth, approximate stability, pattern spectrum filtering (PSF))
Summary
An open question in neuroscience is whether neuronal activity is organized in spatio-temporal patterns (STPs) of millisecondprecise spikes to represent and process information. If a group of neurons is activated simultaneously, synchronous activity is elicited and propagates to the group of neurons, where it arrives simultaneously due to identical propagation delays This group in turn sends synchronous spikes to the and so on, such that volleys of synchronous spikes travel through the chain-like structure. In an electrophysiological study on few simultaneously recorded neurons, Prut et al (1998) showed the occurrence of millisecond-precise STPs beyond the level expected on the basis of the neuronal firing rates, computed instead on a slower time scale of tens or hundreds of milliseconds. The existence of time-coding schemes in networks of several tens to hundreds of neurons remains debated due to the long-standing lack of data and of analysis tools suited for this investigation
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