Computational Neuroscience

I develop quantitative methods on neurophysiological large datasets to investigate brain information processing during cognition and disease. In particular, I am interested in the mechanisms by which stimuli, behavioral responses, and pathological states are encoded and distributed through the simultaneous activity of multiple brain areas. For my investigations, I mainly analyze single-cell (spike train) and neural population (human intracranial EEG) data. 

 

  • Neural coding and communication (spike train data)
    I am interested in the neural coding problem and in particularly, in relating this problem to that of information transmission. I have mainly studied both problems in the context of reward-driven perceptual tasks in monkeys [1,2] to characterize the thalamo-cortical and cortical-cortical directed functional paths that are activated during these tasks. 
     

  • Epilepsy (human intracranial EEG data)
    I study the emergence and maintenance of the pre-ictal state (the brain state prior to epileptic seizures) by means of dynamic functional connectivity analysis of long-lasting periods (~12 hours) of intracranial data from epileptic patients [3]. I am also interested in spatially determining the main brain areas where seizures begin ("focus"). With this regard, we have recently developed quantitative tools to predict seizure focus localization for pre-surgical diagnosis [4,5]. 

  • Computational models and methods
    I have contributed to develop non-parametric statistical methods for non-linear [1] and linear models [6] for the inference of functional connectivity pathways using multiple and simultaneous brain area recordings. 

 

[1] "Task-driven intra- and interarea communications in primate cerebral cortex". A. Tauste Campo, M. Martinez-Garcia, V. Nácher, R. Luna, R. Romo and G. Deco. PNAS, 112(15): 4761-6, 2015.

[2] "Feedforward information and zero-lag synchronization in the sensory thalamo-cortical circuit are modulated during stimulus perception". A. Tauste Campo, Y. Vázquez, M. Àlvarez, A. Zainos, R. Rossi-Pool, G. Deco, R Romo. PNAS, 116(15): 7513-22, 2019.

[3] "Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain", A. Tauste Campo, A. Principe, M. Ley, R. Rocamora and G. Deco. PLoS Biology, 6(4): e2002580, 2018.

[4] "Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification". M. Vila-Vidal, A. Principe, M. Ley, G. Deco, A. Tauste Campo* and R. Rocamora*. Clinical Neurophysiology, 128:977-85, 2017.

[5] "Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction". M. Vila-Vidal, C. Pérez-Enríquez, A. Principe, R. Rocamora, G. Deco, A. Tauste Campo. Neuroimage, vol. 208, 116410, 2020.

[6] "Non-parametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data", M. Gilson*, A. Tauste Campo*, X. Chen, A. Thiele, G. Deco, Network Neuroscience, 1(4): 357-80, 2017.

 

Information Theory

I am interested in the fundamental limits of information transmission when the duration of the transmitted sequences is not necessarily infinite. Within this topic, I have done research in the mathematical derivation of tight error exponents for joint source-channel coding [1] and converse bounds based on hypothesis testing [2]. I have also worked in other information-theoretic topics such as multiuser detection [3] and network coding.

 

[1] "A derivation of the source-channel error exponent using nonidentical product distributions", A. Tauste Campo, G. Vazquez-Vilar, A. Guillén i Fàbregas, T. Koch, A. Martinez. IEEE Trans. Inf. Theory, 60(6): 3209-3217, 2014.

[2] "Bayesian M-ary hypothesis testing: The meta-converse and Verdú-Han bounds are tight", G. Vazquez-Vilar, A. Tauste Campo, A. Guillén i Fàbregas and A. Martinez, IEEE Trans. Inf. Theory, 62(5): 2324 - 2333, 2016

[3] "Large-system analysis of multiuser detection with an unknown number of users: A high SNR approach", A. Tauste Campo, A. Guillén i Fàbregas and E. Biglieri. IEEE Trans. Inf. Theory,  57(6): 3416-3428, 2011.