Detecting multineuronal temporal patterns in parallel spike trains

We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept

Uncovering representations of sleep-associated hippocampal ensemble spike activity

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.Collaborative Research in Computational Neuroscience (Award IIS-1307645)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-10-1-0936)National Institutes of Health (U.S.) (Grant TR01-GM10498

Novel Machine Learning Approaches for Neurophysiological Data Analysis

Detecting repeating firing motifs of neuron groups (so-called neuronal assemblies) and cell segmentation in calcium imaging, a microscopy technique enabling the observation of neuronal activity, are two fundamental and challenging tasks in neurophysiological data analysis. In this thesis, three novel approaches are presented, which use machine learning to tackle both problems from different perspectives. First, SCC is presented for the detection of motifs in neuronal spike matrices, which are gained from calcium imaging data by cell segmentation. SCC uses sparse convolutional coding and outperforms established motif detection methods by leveraging sparsity constraints specifically designed for this data type combined with a method to avoid false-positive detections. Second, LeMoNADe is the first method ever to detect spatio-temporal motifs directly in calcium imaging videos, eliminating the cumbersome extraction of individual cells. It is a variational autoencoder framework tailored for the extraction of neuronal assemblies from videos and matches the performance of state-of-the-art detection methods requiring cell extraction. Although LeMoNADe enables the detection of neuronal assemblies without previous cell extraction, this step is still essential for a wide range of downstream analyses. Therefore, the third method, DISCo, combines a deep learning model with an instance segmentation algorithm to address this task from a new perspective and thereby outperforms similarly trained existing models