My interest is to understand how does the brain process the stream of information it constantly acquires. To this end, I'm looking at groups of neurons in visual cortex and trying to make sense of how they represent visual information, particularly via spatio-temporal spike patterns. My work follows two main directions. One deals with the development of tools for analyzing and visualizing high dimensional neuronal data. The other identifies which properties of the neuronal activity carry most information and how does the representation change over time, as a function of learning.
Visualizing Multineuronal Activity Patterns
Spike rastergrams are an efficient tool for visualizing the activity of simultaneously recorded neurons.
However, the visual detection of spike patterns across multiple neurons is largely dependent on the arrangement of neurons in the rastergram.
The Gestalt principles that govern our visual system dictate that spike patterns distributed across neurons that are not neighbors in the rastergram will most likely be missed.
The labeling of spike patterns with colors enables the visualization of each trial as a sequence of colors. Grouping several color sequences by a certain criterion (e.g., stimulus, time of recording) can reveal regions in which similar patterns (colors) occur consecutively along the trial and/or consistently around the same time points across different trials.
Figure. Color sequences corresponding to trials recorded with the same stimulus
This method enables the intuitive visualization of neuronal population dynamics and enables the identification of periods of interest, which can be further subjected to more quantitative analyses. Although it was designed for the visualization of multielectrode spike trains, the method could be applied also to simultaneously recorded continuous signals (e.g., LFP, EEG, MEG).
Timescale of Informative Multineuronal Activity Patterns
A fundamental and much debated problem in neuroscience is how neurons in the visual system work together to encode information that is sampled by our eyes.
Figure. Discriminating a visual stimulus from a set based on the activity of 22 neurons at various timescales. Stimuli were movies showing natural scenes. The colored arrows indicate moments in time where one timescale was more informative than the others in discriminating the stimulus identity.
Our results indicate that the internal timescale of the brain, i.e., the time window required by neurons to encode a given aspect of the visual stimulus, is tightly correlated to the external timescale of the visual stimulus, i.e., the speed with which visual images on the retina change. Thus, when quick responses are needed as a reaction to the attack of a predator, rapid changes in the field of vision can trigger fast neuronal responses that convey information rapidly, on timescales of a few milliseconds. This suggests that the brain is well adapted to the environment, matching the speed of its internal activity to the constrains imposed by the environment.