- How the phase of slow LFPs enhances the information content of spikes. The top panel shows Delta band (1 – 4 Hz) local field potential (LFP) traces from the monkey primary visual cortex during five presentations of a naturalistic color movie. The line color denotes the instantaneous LFP phase (phase range divided in quarters: 0 - π/2, π/2 - π, π - 3 π/2, and 3π/2 - 2π). The central panel is a raster plot of spike times elicited with 30 repeated movie presentations (spike times indicated by dots and colored according to the concurrent LFP phase). The bottom panel plots the instantaneous spike rate (averaged over all trials). The movie scenes indicated by green and blue arrows can be much better discriminated from each other using the phase of firing (i.e. taking into account the color of the spikes in the raster) rather than by the spike rate. The extra information available in the phase of firing is crucial for the disambiguation between stimuli eliciting high spike rates of similar magnitude. From .
Responses of individual neurons to identical repeats of a sensory stimulus are highly variable. However, the brain can processes information and make decisions based on single events, and can thus make sense of the noisy messages of individual neurons by evaluating the activity of populations and by merging information carried by different aspects of neural activity and by different networks. How the brain achieves such stable representation of sensory events even with noisy computing elements is a central, yet unaddressed, question in neuroscience.
Neuroscientists can now record, simultaneously and from the same or different cortical regions, several types of neural signals, each reflecting different and complementary aspects of neural activity at different scales of its organization. Spiking activity captures the output of individual neurons. Local field potentials (LFPs) capture massed synaptic activity and other slow aspects of the activity of large local populations. Recording all these electrophysiological signals as well as the fMRI BOLD signal gives us an unprecedented opportunity to understand how the brain integrates all the information carried, at different spatial and temporal scales, by single neurons and by widespread networks. Yet progress in understanding how neural populations process information has been limited by a lack of analysis methods capable of comparing and merging the different types of information carried by different neural signals, and by the lack of realistic computational models of how integration of information can be achieved. Our aim is to develop the mathematical analysis and mathematical modeling tools to address these questions.
Using information theory, we develop new analytical methods that quantify the amount of information available in a single trial and for each type of neural signal. At the same time, we determine whether different signals carry information about the same or complementary sensory features [1, 2] and how different signals influence each other. The unique advantage of these methods is their objectiveness, because they do not rely on any assumptions about what is “signal” and what is “noise” in neural responses or on what features of the stimulus trigger neural responses.