ReviewNeural networks : the official journal of the International Neural Network Society2009
Methods for estimating neural firing rates, and their application to brain-machine interfaces.
Review in Neural networks : the official journal of the International Neural Network Society, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Soft monolithic infrared neural interface for simultaneous neurostimulation and electrophysiology.Light, science & applications · 2023Article
- Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.PLoS computational biology · 2021Article
- Spike Train Coactivity Encodes Learned Natural Stimulus Invariances in Songbird Auditory Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Power-saving design opportunities for wireless intracortical brain-computer interfaces.Nature biomedical engineering · 2020Article
- Fano Factor: A Potentially Useful Information.Frontiers in computational neuroscience · 2020Article
- Characterizing and dissociating multiple time-varying modulatory computations influencing neuronal activity.PLoS computational biology · 2019Article
- Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings.Scientific reports · 2019Article
- Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.Biomedical engineering online · 2019Article
- Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings.Journal of computational neuroscience · 2019Article
- Evaluation of Decoding Algorithms for Estimating Bladder Pressure from Dorsal Root Ganglia Neural Recordings.Annals of biomedical engineering · 2018Article
- Article
- Firing rate estimation using infinite mixture models and its application to neural decoding.Journal of neurophysiology · 2017Article
- Structure in neural population recordings: an expected byproduct of simpler phenomena?Nature neuroscience · 2017Article
- Distance-based permutation of inter-meal differences as a sensitive test of temporomandibular joint nociception in rats.Journal of applied biobehavioral research · 2017Article
- Closed-loop brain training: the science of neurofeedback.Nature reviews. Neuroscience · 2017Review
- Connexin-Mediated Signaling in Nonsensory Cells Is Crucial for the Development of Sensory Inner Hair Cells in the Mouse Cochlea.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Sums of Spike Waveform Features for Motor Decoding.Frontiers in neuroscience · 2017Article
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4 authors.
Funding
Abstract
Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.
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