Abstract
Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.
Highlights
Neural oscillations have generated considerable interest for their roles in cognition and as indicators for disease [1,2,3,4,5,6,7,8,9,10,11,12,13,14]
Using data simulated from theoretical neurons and real data recorded from cortical motor neurons, we demonstrate the method’s ability to track changes in the modulatory structure of the oscillation across experimental trials
To tease apart multiple factors that affect spiking activity, statisticians have suggested the use of point process models together with modern regression methods associated with generalized linear model (GLM) technology [20, 21]
Summary
Neural oscillations have generated considerable interest for their roles in cognition and as indicators for disease [1,2,3,4,5,6,7,8,9,10,11,12,13,14]. Groups of neurons that are locked to a common oscillation, and are active in tightly confined temporal windows, may define a cell assembly whose synchronous spiking could select and activate afferent structures [15,16,17]. Such transient cell assemblies may form and disband as objects are attended to in the visual scene [3, 6,7,8, 11], as preparation for movements are made [2, 13], or during choice points for reward [9]. In this paper we develop a method that can find oscillations in spike trains even when the signal is comparatively weak, and can track changes in oscillatory behavior across trials
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