Homepage of Rodrigo Quian Quiroga

I am a Reader in Bioengineering at the University of Leicester, England. I am also a visiting associate in biology at Caltech, a visiting researcher at the department of Neurosurgery at UCLA and a visiting researcher at the Leibniz Institute of Neurobiology at the University of Magdeburg, Germany. My main research interest is in Computational Neuroscience and the analysis of electrophysiological data.

Before joining Leicester University, I was a Sloan post-doctoral fellow at the Sloan Swartz Center of Theoretical Neuroscience at Caltech. There I worked both at Richard Andersen's and Christof Koch's labs, with whom I keep an active collaboration. With Christof we study neural correlates of visual perception. For this we analyze single cell recordings in awake humans in collaboration with Itzhak Fried at UCLA. These recordings are from epileptic patients, which are studied with intracranial electrodes in order to localize the epileptic focus and evaluate the feasibility of epilepsy surgery. We found a remarkable type of cells responding selectively to different pictures of the same individual or object. For analyzing these data, it is crucial the use of an optimal spike sorting method in order to identify the firing of the different cells. I developed – wave_clus- an unsupervised algorithm for spike detection and sorting that seems to do a good job. These codes, some exemplary data and a tutorial on spike sorting are available here. One of Richard’s main interests is the function of the posterior parietal cortex (PPC) and its correlation with movement planning. We have been working on single-trial decoding of movement plans and showed that it is possible to reliably predict saccades and reach plans from two segregated areas in PCC. These results have implications for the development of neuronal prostheses, which is an active field of research at Richard’s lab.

Before joining Caltech, I was a post-doc at Peter Grassberger's group in Juelich, Germany, where I analyzed different type of EEG recordings and models with methods developed in the framework of non-linear dynamical systems ("Chaos methods"). These range from the calculation of Dimensions, Lyapunov Exponents and Entropies. We also studied synchronization of dynamical systems and developed new measures that are likely to be very suitable for real data, especially for brain signals and the study of epilepsy.

I have also been studying EEGs and evoked potentials (i.e. alterations of the EEG due to stimulation) with time-frequency methods. The time-frequency analysis ranges from elementary tools such as the Fourier Transform to more complex ones such as the Gabor Transform, Wavelets and further quantitative measures derived from them. With these methods it is possible to correlate spontaneous and evoked oscillations with different brain processes. I have also been working on the analysis of single-stimulus evoked potentials (without need of ensemble averaging of several trials), something very difficult to be done with previous approaches due to the low amplitude of the evoked responses in comparison with the ongoing EEG. This allows the study of functional processes, such as habituation, sensitization, learning, etc. and contributes new information that can have high clinical relevance (see the tutorial on wavelet decomposition and denoising).

Selected publications:

Invariant visual representation by single-neurons in the human brain.
R. Quian Quiroga, L. Reddy, G. Kreiman, C. Koch and I. Fried
Nature, 435: 1102-1107; 2005.

Unsupervised spike sorting with wavelets and superparamagnetic clustering.
R. Quian Quiroga, Z. Nadasdy and Y. Ben-Shaul
Neural Computation, 16: 1661-1687; 2004.

Single-trial event-related potentials with Wavelet Denoising.
R. Quian Quiroga and H. Garcia.
Clin. Neurophysiol. 114: 376-390, 2003.

Performance of different synchronization measures in real data: a case study on electroencephalographic signals.
Quian Quiroga R, Kraskov A, Kreuz T and Grassberger P.
Phys. Rev. E, 65: 041903; 2002.