Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands

Abstract

Studies analyzing sensory cortical processing or trying to decode brain activity often rely on a combination of different electrophysiological signals, such as local field potentials (LFPs) and spiking activity. Understanding the relation between these signals and sensory stimuli and between different components of these signals is hence of great interest. We here provide an analysis of LFPs and spiking activity recorded from visual and auditory cortex during stimulation with natural stimuli. In particular, we focus on the time scales on which different components of these signals are informative about the stimulus, and on the dependencies between different components of these signals. Addressing the first question, we find that stimulus information in low frequency bands (<12 Hz) is high, regardless of whether their energy is computed at the scale of milliseconds or seconds. Stimulus information in higher bands (>50 Hz), in contrast, is scale dependent, and is larger when the energy is averaged over several hundreds of milliseconds. Indeed, combined analysis of signal reliability and information revealed that the energy of slow LFP fluctuations is well related to the stimulus even when considering individual or few cycles, while the energy of fast LFP oscillations carries information only when averaged over many cycles. Addressing the second question, we find that stimulus information in different LFP bands, and in different LFP bands and spiking activity, is largely independent regardless of time scale or sensory system. Taken together, these findings suggest that different LFP bands represent dynamic natural stimuli on distinct time scales and together provide a potentially rich source of information for sensory processing or decoding brain activity.

Fig. 1

Information theoretic analysis of field potentials (a) For the information analysis the time axis during stimulus (movie or sound) presentation was divided into non-overlapping windows (S_i) of length T, within which the responses were characterized. In this formalism, a given response can be elicited by any sensory feature either occurring in the respective time window i, or in any previous time window. The window length T was varied systematically from 4 to 2048 ms. (b) Example data from auditory cortex illustrating the raw LFP from a single trial, together with the band-pass filtered signal in the 4–8 Hz band. The red line denotes the energy of the signal, extracted by the Hilbert transform. The lower panel displays the binned (n = 4) energy in several subsequent trials. Arrows indicate instances where the energy is highly consistent across trials

Fig. 2

Stimulus information on different time scales. (a) Stimulus information (units of bits) in the LFP energy of different frequency bands and for time windows T of different length. Each row displays the mean value for the respective dataset. White numbers in the lower panel indicate the number of cycles of the respective band included in one time window. Note that the scales for auditory and visual data differ. (b) Information for intermediate time windows (T = 128 msec). Red lines denote the mean, shading the s.e.m across recording sites. Blue lines show the power of the LFP signal as a function of frequency, averaged for the stimulation period (dark) and a pre-stimulus baseline period (light). (c) Coefficient of variation of the LFP energy across trials. The coefficient is defined as the ratio of the standard deviation (across trials) to the mean, and lower values indicate a lower variability. Graphs display the mean for each dataset. (d) Example data from visual cortex the 4–8 Hz band. a) Single trial band-pass filtered signal together with envelope. b) Two single trials. c) Envelope of multiple trials computed in 4 ms time windows. d) Envelope of multiple trials computed in 1024 ms windows. (e) Same as in D, but for the 56–60 Hz band

Fig. 3

Information synergy between LFP bands. (a) Joint information provided by the energy of pairs of LFP bands (mean values across sites). (b) Synergy between pairs of bands, expressed as percent of the sum of the information of both bands considered independently. Negative values (blue) indicate redundancy. (c) Example data from auditory cortex, displaying the mean LFP energy from one recording site and three different bands (sampled at T = 2048 ms)

Fig. 4

Signal and noise correlations. This figure displays the signal and noise correlations between pairs of frequency bands for both datasets (2048 ms time windows). Each panel shows the average across sites. Please refer to the Main Text or the Methods for a definition of signal and noise correlations

Fig. 5

Information synergy between LFP bands and MUA. (a) Synergy between LFP and MUA as a function of frequency band and time scale. Synergy is expressed as percent of the sum of the information of both bands considered independently. Negative values (blue) indicate redundancy. (b) Synergy between LFP bands and MUA when both are computed in 2048 ms windows. (c) Example data from visual cortex showing the similar (trial averaged) time courses of MUA and high frequency LFPs (60–64 Hz) and the different time course of a low frequency band (4–8 Hz, sampled at T = 1024 ms)