Detecting Multineuronal Temporal Patterns in Parallel Spike Trains

Gansel, Kai S.; Singer, Wolf

doi:10.3389/fninf.2012.00018

Cited by 19 publications

(30 citation statements)

References 70 publications

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“…Lopes-dos- Santos et al, 2011;Peyrache et al, 2010), independent component analysis (Laubach et al, 1999), shuffling methods for detecting repeated firing sequences (Abeles and Gat, 2001;Abeles and Gerstein, 1988;Berger et al, 2010;Gansel and Singer, 2012), and methods based on information theory (Arabzadeh et al, 2004;Quiroga and Panzeri, 2009), among others. In this review we focus on linear methods for detecting and tracking the activity of cell assemblies embedded in large neuronal populations.…”

Section: Lopes-dos-santos Et Al / Journal Of Neuroscience Methodsmentioning

confidence: 99%

Detecting cell assemblies in large neuronal populations

Lopes‐dos‐Santos

Ribeiro

Tort

2013

Journal of Neuroscience Methods

166

264

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a b s t r a c tRecent progress in the technology for single unit recordings has given the neuroscientific community the opportunity to record the spiking activity of large neuronal populations. At the same pace, statistical and mathematical tools were developed to deal with high-dimensional datasets typical of such recordings. A major line of research investigates the functional role of subsets of neurons with significant co-firing behavior: the Hebbian cell assemblies. Here we review three linear methods for the detection of cell assemblies in large neuronal populations that rely on principal and independent component analysis. Based on their performance in spike train simulations, we propose a modified framework that incorporates multiple features of these previous methods. We apply the new framework to actual single unit recordings and show the existence of cell assemblies in the rat hippocampus, which typically oscillate at theta frequencies and couple to different phases of the underlying field rhythm.

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Section: Lopes-dos-santos Et Al / Journal Of Neuroscience Methodsmentioning

confidence: 99%

Detecting cell assemblies in large neuronal populations

Lopes‐dos‐Santos

Ribeiro

Tort

2013

Journal of Neuroscience Methods

166

264

View full text Add to dashboard Cite

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“…[27,26,21]) or assume a more or less noiseless scenario, seeking to classify exactly recurring STP in neuronal activity (apart from allowing some jitter, see e.g. [9]). Fitting Hopfield networks on windowed spike train data, we obtain networks that encode denoised versions of salient temporal spike patterns present in the data as memories.…”

Section: Understanding the Activity Of Large Collections Of Neurons Umentioning

confidence: 99%

Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks

Hillar

Effenberger

2015

Procedia Computer Science

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We present here a novel method for the classical task of extracting reoccurring spatiotemporal patterns from spiking activity of large populations of neurons. In contrast to previous studies that mainly focus on synchrony detection or exactly recurring binary patterns, we perform the search in an approximate way that clusters together nearby, noisy network states in the data. Our approach is to use minimum probability flow (MPF) parameter estimation to deterministically fit very large Hopfield networks on windowed spike trains obtained from recordings of spontaneous activity of neurons in cat visual cortex. Examining the structure of the network memories over the spiking activity after training, we find that the networks robustly discover long-range temporal correlations. Specifically, the recurrent network dynamics denoise and group together windowed spike patterns, revealing underlying structure such as cell assemblies. We first demonstrate this by computing an analogy to spike triggered averages that we call memory triggered averages (MTAs). MTAs are obtained by averaging raw spike train windows that converge under the network dynamics to the same memory. The MTAs reveal prominent repeating patterns in the data that are difficult to detect with standard methods such as PCA. Additionally, when memories are collected over eight disjoint epochs in 280 seconds of windowed spiking activity from 50 neurons, their counts are nearly identical and the networks store significantly more memories than would be possible if trained on random patterns.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Meij

Voytek

2018

eNeuro

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It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning.

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Detecting Multineuronal Temporal Patterns in Parallel Spike Trains

Cited by 19 publications

References 70 publications

Detecting cell assemblies in large neuronal populations

Detecting cell assemblies in large neuronal populations

Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks

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

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