Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure

Grossberger, Lukas; Battaglia, Francesco P.; Vinck, Martin

doi:10.1101/252791

Cited by 11 publications

(29 citation statements)

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“…Temporal sequences may also be critical for memory formation, because the plasticity rules in the brain are highly sensitive to the temporal order in which neurons fire spikes [12][13][14].We have previously developed a dissimilarity measure between multi-neuron temporal spiking patterns called SPOTDis (Spike Pattern Optimal Transport Dissimilarity). This measure is defined as the minimum energy (optimal transport) that is needed to transform all pairwise cross-correlations of one epoch k into the pairwise cross-correlations of another epoch m. We showed that, due to employment of optimal transport, SPOTDis has several attractive features (see Results) [15], and can be combined with unsupervised dimensionality-reduction techniques like t-SNE or clustering techniques like HDBSCAN to characterize the state-space of multi-neuron temporal sequences [15]. However, the SPOTDis dissimilarity measure has two principal weaknesses: (1) Its computational complexity, for comparing two time epochs, is O(N 2 ), where N is the number of neurons.…”

mentioning

confidence: 89%

“…trials (e.g. stimulus presentations) or sliding windows [15]. The problem is to find a dissimilarity measure with the following properties [15]:…”

Section: Introductionmentioning

confidence: 99%

“…Previous work: SPOTDis [15] developed a dissimilarity measure called SPOTDis, which is based on optimal transport theory. We briefly summarize the computing steps of SPOTDis (see Methods):…”

Section: Introductionmentioning

confidence: 99%

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SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns

Sotomayor-Gómez

Battaglia

Vinck

2020

Preprint

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Neuronal coding and memory formation depend on temporal activation patterns spanning high-dimensional ensembles of neurons. To characterize these high-dimensional spike sequences, it is critical to measure their dissimilarity across different epochs (e.g. stimuli, brain states) in terms of all the relative spike-timing relationships. Such a dissimilarity measure can then be used for dimensionality reduction, clustering of sequences or decoding. Here, we present a new measure of dissimilarity between multi-neuron spike sequences based on optimal transport theory, called SpikeShip. SpikeShip computes the optimal transport cost (Earth Mover's Distance) to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. It achieves this by computing the optimal transport of spikes across time, per neuron separately, and then decomposing this transport cost in a temporal rigid translation term and a vector of neuron-specific transport flows. This yields a suitable geometry for spike sequences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles and has linear O(N ) cost. Furthermore, it is explicitly based on the higher-order structure in the spiking patterns. SpikeShip opens new avenues for studying neural coding and memory consolidation by finding patterns in high-dimensional neural ensembles.June 4, 2020 1/15 spiking patterns and sensory or internal variables. They also pose unique mathematical challenges and opportunities for the unsupervised discovery of the "dictionary" of neuronal "code-words". For instance, how do we measure the similarity between two multi-neuron spiking patterns in terms of their temporal structure (spike sequence)?In general, any notion of information encoding relies on the construction of a distance or dissimilarity measure in an N-dimensional space. For example, the distance between binary strings can be measured using the Hamming distance, which is an essential mathematical construct in error-correcting coding. In the brain, the distance between two multi-neuron spiking patterns is conventionally measured in two steps: (1) By computing the number of spikes/sec (firing rates) for each neuron in a certain time window; and (2) computing e.g. the angle or Euclidean distance between multi-neuron rate vectors. Using this method, it has been shown for example that high-dimensional neural ensembles span a low-dimensional manifold that relates to stimulus or behavioral variables in a meaningful way [9,10]. However, the computation of firing rates disregards potentially rich information contained by the precise temporal order in which spikes are fired by multiple neurons (e.g. neuron i firing at time t and neuron j firing at t + τ ). For instance, we expect that any time-varying sensory stimulus (motion) or action sequence may be encoded by a unique multi-neuron temporal pattern of spiking. It has been shown that multi-neuron temporal sequences encode information about sensory stimuli or are required for the generation of complex m...

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confidence: 89%

“…trials (e.g. stimulus presentations) or sliding windows [15]. The problem is to find a dissimilarity measure with the following properties [15]:…”

Section: Introductionmentioning

confidence: 99%

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SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns

Sotomayor-Gómez

Battaglia

Vinck

2020

Preprint

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“…We thus developed a new model-based 90 approach using a template-matching procedure to classify these border cells in RSC ( Fig. 91 1d-1f), based on (Grossberger, Battaglia, & Vinck, 2018). 92…”

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confidence: 99%

“…2, 3), adding additional weight in the 587 location of placed objects/walls, or removing it in the absence of an outer wall. The EMD 588 distance between a ratemap and a template represents the minimal cost that must be paid to 589 transform one distribution into another, and is thus a normalized metric of dissimilarity 590 (Grossberger et al, 2018). 591…”

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confidence: 99%

Representation of Distance and Direction of Nearby Boundaries in Retrosplenial Cortex

Wijngaarden

Babl

2019

Preprint

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11 Borders and edges are salient and behaviourally relevant features for navigating the 12 environment. The brain forms dedicated neural representations of environmental 13 boundaries, which are assumed to serve as a reference for spatial coding. Here we 14 expand this border coding network to include the retrosplenial cortex (RSC) in which 15 we identified neurons that increase their firing near all boundaries of an arena. RSC 16 border cells specifically encode walls, but not objects, and maintain their tuning in the 17 absence of direct sensory detection. Unlike border cells in the medial entorhinal 18 cortex (MEC), RSC border cells are sensitive to the animal's direction to nearby walls 19 located contralateral to the recorded hemisphere. Pharmacogenetic inactivation of 20 MEC led to a disruption of RSC border coding, but not vice versa, indicating network 21 directionality. Together these data shed light on how information about distance and 22direction of boundaries is generated in the brain for guiding navigation behaviour. 23

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Do not waste your electrodes—principles of optimal electrode geometry for spike sorting

et al. 2021

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Objective: This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance. Approach: The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semiartificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampus in vivo. Main results: Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance. Significance: Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one-and two-dimensional electrode systems, targeting specific brain areas.

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Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure

Cited by 11 publications

References 80 publications

SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns

SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns

Representation of Distance and Direction of Nearby Boundaries in Retrosplenial Cortex

Do not waste your electrodes—principles of optimal electrode geometry for spike sorting

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