Time series: data analysis and theory
Time series: data analysis and theory
Combining frequency and time domain approaches to systems with multiple spike train input and output
Biological Cybernetics - Special Issue on Neuronal Dynamics of Sensory Coding
Discrete-Time Signal Processing
Discrete-Time Signal Processing
Toeplitz and Hankel kernels for estimating time-varying spectra ofdiscrete-time random processes
IEEE Transactions on Signal Processing
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The coherence between neural spike trains and local-field potential recordings, called spike-field coherence, is of key importance in many neuroscience studies. In this work, aside from questions of estimator performance, we demonstrate that theoretical spike-field coherence for a broad class of spiking models depends on the expected rate of spiking. This rate dependence confounds the phase locking of spike events to field-potential oscillations with overall neuron activity and is demonstrated analytically, for a large class of stochastic models, and in simulation. Finally, the relationship between the spike-field coherence and the intensity field coherence is detailed analytically. This latter quantity is independent of neuron firing rate and, under commonly found conditions, is proportional to the probability that a neuron spikes at a specific phase of field oscillation. Hence, intensity field coherence is a rate-independent measure and a candidate on which to base the appropriate statistical inference of spike field synchrony.