Detection and evaluation of bursts in terms of novelty and surprise.

The detection of bursts and also of response onsets is often of relevance in understanding neurophysiological data, but the detection of these events is not a trivial task. We build on a method that was originally designed for burst detection using the so-called burst surprise as a measure. We extend this method and provide a proper significance measure. Our method consists of two stages. In the first stage we model the neuron's interspike interval (ISI) distribution and make an i.i.d. assumption to formulate our null hypothesis. In addition we define a set of 'surprising' events that signify deviations from the null hypothesis in the direction of 'burstiness'. Here the so-called (strict) burst novelty is used to measure the size of this deviation. In the second stage we determine the significance of this deviation. The (strict) burst surprise is used to measure the significance, since it is the negative logarithm of the significance probability. After showing the consequences of a non-proper null hypothesis on burst detection performance, we apply the method to experimental data. For this application the data are divided into a period for parameter estimation to express a proper null hypothesis (model of the ISI distribution), and the rest of the data is analyzed by using that null hypothesis. We find that assuming a Poisson process for experimental spike data from motor cortex is rarely a proper null hypothesis, because these data tend to fire more regularly and thus a gamma process is more appropriate. We show that our burst detection method can be used for rate change onset detection, because a deviation from the null hypothesis detected by (strict) burst novelty also covers an increase of firing rate.

[1]  Sonja Grün,et al.  Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task , 2016, The Journal of Neuroscience.

[2]  Maurice J. Chacron,et al.  Nonrenewal spike train statistics: causes and functional consequences on neural coding , 2011, Experimental Brain Research.

[3]  C R Legéndy.  Three principles of brain function and structure. , 1975, The International journal of neuroscience.

[4]  Christian Borgelt,et al.  Statistical evaluation of synchronous spike patterns extracted by frequent item set mining , 2013, Front. Comput. Neurosci..

[5]  G. Palm.  Novelty, Information and Surprise , 2012, Information Science and Statistics.

[6]  M. Nawrot,et al.  Serial correlation in neural spike trains: experimental evidence, stochastic modeling, and single neuron variability. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  A. Reyes,et al.  Relation between single neuron and population spiking statistics and effects on network activity. , 2006, Physical review letters.

[8]  DiesmannMarkus,et al.  Unitary events in multiple single-neuron spiking activity , 2002 .

[9]  Sonja Grün,et al.  Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task , 2018, Scientific Data.

[10]  Robert E. Kass,et al.  Detection of bursts in extracellular spike trains using hidden semi-Markov point process models , 2010, Journal of Computational Neuroscience.

[11]  J. Csicsvari,et al.  Reliability and State Dependence of Pyramidal Cell–Interneuron Synapses in the Hippocampus an Ensemble Approach in the Behaving Rat , 1998, Neuron.

[12]  Fleur Zeldenrust,et al.  Neural Coding With Bursts—Current State and Future Perspectives , 2018, Front. Comput. Neurosci..

[13]  Kikuro Fukushima,et al.  Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex , 2009, PLoS Comput. Biol..

[14]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[15]  Yutaka Sakai,et al.  Similarity in Neuronal Firing Regimes across Mammalian Species , 2016, The Journal of Neuroscience.

[16]  David C. Tam.  An alternate burst analysis for detecting intra-burst firings based on inter-burst periods , 2002, Neurocomputing.

[17]  A. Riehle,et al.  Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements , 2013, Front. Neural Circuits.

[18]  Sonja Grün,et al.  Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data , 2002, Neural Computation.

[19]  Sonja Grün,et al.  Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE , 2017, Front. Comput. Neurosci..

[20]  C. Shatz,et al.  A Burst-Based “Hebbian” Learning Rule at Retinogeniculate Synapses Links Retinal Waves to Activity-Dependent Refinement , 2007, PLoS biology.

[21]  Jari Hyttinen,et al.  Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy , 2017, Front. Comput. Neurosci..

[22]  S. Martinoiaa,et al.  Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons , 2005 .

[23]  F. Delcomyn,et al.  Identification of bursts in spike trains , 1992, Journal of Neuroscience Methods.

[24]  M. DeWeese,et al.  Binary Spiking in Auditory Cortex , 2003, The Journal of Neuroscience.

[25]  Boris Gourévitch,et al.  A nonparametric approach for detection of bursts in spike trains , 2007, Journal of Neuroscience Methods.

[26]  Mingzhou Ding,et al.  Trial-by-trial estimation of amplitude and latency variability in neuronal spike trains , 2007, Journal of Neuroscience Methods.

[27]  Adam Kepecs,et al.  Information encoding and computation with spikes and bursts , 2003, Network.

[28]  Stefan Rotter,et al.  Elimination of response latency variability in neuronal spike trains , 2003, Biological Cybernetics.

[29]  Ralph Neininger,et al.  A multiple filter test for the detection of rate changes in renewal processes with varying variance , 2013, 1303.3594.

[30]  Sergio Martinoia,et al.  A self-adapting approach for the detection of bursts and network bursts in neuronal cultures , 2010, Journal of Computational Neuroscience.

[31]  Sonja Grün,et al.  Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance , 2002, Neural Computation.

[32]  D. Ko,et al.  Detection of bursts and pauses in spike trains , 2012, Journal of Neuroscience Methods.

[33]  C. Legéndy,et al.  Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons. , 1985, Journal of neurophysiology.

[34]  Martin P. Nawrot,et al.  Analysis and Interpretation of Interval and Count Variability in Neural Spike Trains , 2010 .

[35]  Sonja Grün,et al.  Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex , 2017, bioRxiv.

[36]  George L. Gerstein,et al.  Determination of Response Latency and Its Application to Normalization of Cross-Correlation Measures , 2001, Neural Computation.

[37]  Lin Chen,et al.  Detection of bursts in neuronal spike trains by the mean inter-spike interval method , 2009 .

[38]  Petr Lánský,et al.  A review of the methods for neuronal response latency estimation , 2015, Biosyst..

[39]  J. Assad,et al.  Beyond Poisson: Increased Spike-Time Regularity across Primate Parietal Cortex , 2009, Neuron.

[40]  G. Buzsáki,et al.  Temporal Interaction between Single Spikes and Complex Spike Bursts in Hippocampal Pyramidal Cells , 2001, Neuron.

[41]  Stefan Rotter,et al.  Measurement of variability dynamics in cortical spike trains , 2008, Journal of Neuroscience Methods.

[42]  Alessandro Vato,et al.  Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons , 2005, Neurocomputing.

[43]  Hirokazu Takahashi,et al.  Parameters for burst detection , 2014, Front. Comput. Neurosci..

[44]  G. Palm.  Evidence, information, and surprise , 1981, Biological Cybernetics.