Research

I am interested in finding out how can brains process information through spatiotemporal spike patterns. To this end I am developing methods for the analysis and visualization of such spike patterns. My work follows two different, but complementary, approaches. The first has a more qualitative nature and aims to provide researchers with intuitive ways of visualizing multivariate data, in particular multielectrode spike trains. The second approach is more quantitative and tries to relate properties of spatiotemporal spike patterns to features of external stimuli, with the help of classifiers.

Visualizing Multineuronal Activity Patterns

Spike rastergrams are a simple and efficient tool for visualizing the activity of simultaneously recorded neurons. However, the visual detection of multineuronal spike patterns in these plots is largely dependent on the arrangement of the neurons in the rastergram. This happens because of Gestalt principles that govern our visual system. If neurons that form a particular spike pattern are not neighbors in the rastergram, the detection of that pattern can become difficult due to the activity of the other neurons.

To enable the visual identification of multineuronal spike patterns, defined within a time window, we devised a method to color them based on their reciprocal similarity. For example, if pattern A is similar to pattern B but different from pattern C, then pattern A and B will be assigned similar colors (e.g., red and orange) while pattern C will be assigned a different color (e.g., blue). This way, color becomes a signature of pattern identity and the occurrences of similar patterns can be easily visualized across entire recording sessions.

Figure. Transforming simultaneously recorded spike trains into color sequences. For each time window, the spiking activity of all neurons is assessed and then mapped onto a color space using Kohonen maps.

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 (a drifting sinusoidal grating). The three yellow stripes indicate the presence of similar activity patterns at specific time points across all trials.

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).

Article links:

[abstract]

[pdf]

[source code]

Timescale of Informative Multineuronal Activity Patterns

A fundamental and much debated problem in neuroscience is how multiple neurons in the visual system work together to accomplish the encoding of information that is sampled from the environment dynamically by our eyes.
To address this problem, we recorded spiking signals from the primary visual cortex of anesthetized cats while the animals were presented with stimuli of various temporal dynamics. These data were then fed to a set of classifiers that were able to identify and quantify the occurrence of multineuronal spike patterns on arbitrary timescales. By considering different properties of spike pattern occurrence (e.g. stimulus specificity or stimulus time-locking), classifiers could determine the timescales that were most informative for discriminating between visual stimuli.

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 point moments in time where one timescale was more informative than the others in discriminating the stimulus from the set.

The results of our analysis 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.

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. For example, when quick responses are needed as a reaction to the attack of a predator, the rapid changes in the field of vision can determine the brain to quickly respond with multiple neurons that convey information rapidly, on timescales of a few milliseconds. In contrast, when the visual images evolve slowly, neurons can afford to be “lazy” and encode the visual stimulus with slower changing activity patterns.

Article links:

[abstract]

[pdf]