ArticleNeural computation2015
Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter.
Article in Neural computation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Review of Recent Advances in Implantable Brain-Computer Interfaces for the Restoration of Motor Function in Patients With Paralysis.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Dynamical modulation of hippocampal replay through firing rate adaptation.Nature communications · 2026Article
- A State-Space Framework for Causal Detection of Hippocampal Ripple-Replay Events.IEEE transactions on bio-medical engineering · 2026Article
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- Individualized temporal patterns drive human sleep spindle timing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Marked point process variational autoencoder with applications to unsorted spiking activities.PLoS computational biology · 2024Article
- Closed-loop modulation of remote hippocampal representations with neurofeedback.bioRxiv : the preprint server for biology · 2024Article
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach.PLoS computational biology · 2024Article
- Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing.bioRxiv : the preprint server for biology · 2024Article
- Volitional activation of remote place representations with a hippocampal brain-machine interface.Science (New York, N.Y.) · 2023Article
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach.bioRxiv : the preprint server for biology · 2023Article
- Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.bioRxiv : the preprint server for biology · 2023Article
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- Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.Advances in neural information processing systems · 2023Article
- Imagination as a fundamental function of the hippocampus.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- A Variable Clock Underlies Internally Generated Hippocampal Sequences.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials.Cell reports methods · 2021Article
- Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice.Neuron · 2021Article
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5 authors.
Funding
Abstract
Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision, such as real-time decoding for brain-computer interfaces. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes's rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat's position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalent to or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.