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Latency correction in sparse neuronal spike trains. J Neurosci Methods 2022; 381:109703. [PMID: 36075286 PMCID: PMC9554712 DOI: 10.1016/j.jneumeth.2022.109703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/25/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. NEW METHOD We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. RESULTS We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. COMPARISON WITH EXISTING METHOD(S) Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. CONCLUSIONS For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.
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Hermiz J, Hossain L, Arneodo EM, Ganji M, Rogers N, Vahidi N, Halgren E, Gentner TQ, Dayeh SA, Gilja V. Stimulus Driven Single Unit Activity From Micro-Electrocorticography. Front Neurosci 2020; 14:55. [PMID: 32180695 PMCID: PMC7059620 DOI: 10.3389/fnins.2020.00055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 01/14/2020] [Indexed: 12/18/2022] Open
Abstract
High-fidelity measurements of neural activity can enable advancements in our understanding of the neural basis of complex behaviors such as speech, audition, and language, and are critical for developing neural prostheses that address impairments to these abilities due to disease or injury. We develop a novel high resolution, thin-film micro-electrocorticography (micro-ECoG) array that enables high-fidelity surface measurements of neural activity from songbirds, a well-established animal model for studying speech behavior. With this device, we provide the first demonstration of sensory-evoked modulation of surface-recorded single unit responses. We establish that single unit activity is consistently sensed from micro-ECoG electrodes over the surface of sensorimotor nucleus HVC (used as a proper name) in anesthetized European starlings, and validate responses with correlated firing in single units recorded simultaneously at surface and depth. The results establish a platform for high-fidelity recording from the surface of subcortical structures that will accelerate neurophysiological studies, and development of novel electrode arrays and neural prostheses.
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Affiliation(s)
- John Hermiz
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Lorraine Hossain
- Department of Materials Science and Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Ezequiel M Arneodo
- Biocircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Nicholas Rogers
- Department of Physics, University of California, San Diego, La Jolla, CA, United States
| | - Nasim Vahidi
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Eric Halgren
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States.,Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Timothy Q Gentner
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States.,Kavli Institute for Brain and Mind, La Jolla, CA, United States.,Neurobiology Section, University of California, San Diego, La Jolla, CA, United States
| | - Shadi A Dayeh
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States.,Department of Materials Science and Engineering, University of California, San Diego, La Jolla, CA, United States.,Department of Nanoengineering, University of California, San Diego, La Jolla, CA, United States
| | - Vikash Gilja
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States
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Sihn D, Kim SP. A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover's Distance. Front Comput Neurosci 2020; 13:82. [PMID: 31920607 PMCID: PMC6914768 DOI: 10.3389/fncom.2019.00082] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 11/25/2019] [Indexed: 11/25/2022] Open
Abstract
Neural spike train analysis methods are mainly used for understanding the temporal aspects of neural information processing. One approach is to measure the dissimilarity between the spike trains of a pair of neurons, often referred to as the spike train distance. The spike train distance has been often used to classify neuronal units with similar temporal patterns. Several methods to compute spike train distance have been developed so far. Intuitively, a desirable distance should be the shortest length between two objects. The Earth Mover’s Distance (EMD) can compute spike train distance by measuring the shortest length between two spike trains via shifting a fraction of spikes from one spike train to another. The EMD could accurately measure spike timing differences, temporal similarity, and spikes time synchrony. It is also robust to firing rate changes. Victor and Purpura (1996) distance measures the minimum cost between two spike trains. Although it also measures the shortest path between spike trains, its output can vary with the time-scale parameter. In contrast, the EMD measures distance in a unique way by calculating the genuine shortest length between spike trains. The EMD also outperforms other existing spike train distance methods in measuring various aspects of the temporal characteristics of spike trains and in robustness to firing rate changes. The EMD can effectively measure the shortest length between spike trains without being considerably affected by the overall firing rate difference between them. Hence, it is suitable for pure temporal coding exclusively, which is a predominant premise underlying the present study.
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Affiliation(s)
- Duho Sihn
- Department of Human Factors Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea
| | - Sung-Phil Kim
- Department of Human Factors Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea
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Satuvuori E, Kreuz T. Which spike train distance is most suitable for distinguishing rate and temporal coding? J Neurosci Methods 2018; 299:22-33. [PMID: 29462713 DOI: 10.1016/j.jneumeth.2018.02.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 01/17/2018] [Accepted: 02/15/2018] [Indexed: 11/20/2022]
Abstract
BACKGROUND It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. NEW METHOD We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? RESULTS Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. COMPARISON WITH EXISTING METHODS While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates. CONCLUSIONS The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data.
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Affiliation(s)
- Eero Satuvuori
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; MOVE Research Institute, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, The Netherlands.
| | - Thomas Kreuz
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
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