EECS500 Fall 2015 Department Colloquium

Roberto F. Galán, PhD
Stochastic Neural Dynamics and Information Processing in the Brain
White 411
November 10, 2015

Neural activity in the intact brain as well as in vitro is highly variable even in the absence of stimulation. The biophysical mechanisms of this physiologic “noise” are incompletely understood, and so is their functional role in normal and pathological brain function. I will present several results from my lab that shed light onto these questions at different levels of complexity from molecules, to neurons and brain circuits. I will start by briefly introducing the stochastic-shielding approximation of Markov chains which enables the most efficient and accurate simulations to date of random ion-channel currents underlying stochastic neuronal activity [1]. I will then show how this tool can be applied to quantify the amount of information that cortical microcircuits can process in physiologic and pathologic conditions [2, 3].Moving
to the mesoscopic level, I will demonstrate that the stochastic theory developed by Stratonovich in the 50’s and 60’s to understand the effects of thermal noise on electronic oscillators [4] also provides a realistic description of respiratory variability, and predicts the response to vagal nerve stimulation of normal and pathological breathing patterns [5, 6]. I will finally move to the macroscopic level to demonstrate that large-scale brain dynamics at rest can be accurately modeled with a multivariate Ornstein-Uhlenbeck process [7-9]. Applying this model to magnetoencephalography (MEG) data from children with and without autism I found that the spatial distribution of background noise is significantly more homogeneous and less complex in the autistic brain than in age-matched controls. The use of this model allows me to quantify the information gain, or the amount of information that the brain creates
spontaneously at rest. The information gain is significantly higher in children with autism. I will then discuss the relevance of this finding and its interpretation from the perspective of cognitive neuroscience.

1. Schmandt, N.T. and R.F. Galán, Stochastic-Shielding Approximation of Markov Chains and  its Application to Efficiently Simulate Random Ion-Channel Gating. Physical Review Letters, 2012. 109: p. 118101.
2. Puzerey, P.A. and R.F. Galán, On how correlations between excitatory and inhibitory synaptic inputs maximize the information rate of neuronal firing. Front Comput Neurosci, 2014. 8: p. 59.
3. Puzerey, P.A., M.J. Decker, and R.F. Galán, Elevated serotonergic signaling amplifies synaptic noise and facilitates the emergence of epileptiform network oscillations. J Neurophysiol, 2014. 112(10): p. 2357-73.
4. Stratonovich, R.L., Topics in the theory of random noise. Rev. English ed. Mathematics and its applications,. 1963, New York,:Gordon and Breach.
5. Dhingra, R.R., Y. Zhu, F.J. Jacono, D.M. Katz, R.F. Galán, and T.E. Dick, Decreased Hering-Breuer input-output entrainment in a mouse model of Rett syndrome. Front Neural Circuits, 2013. 7: p. 42.
6. Dick, T.E., Y.H. Hsieh, R.R. Dhingra, D.M. Baekey, R.F. Galán, E. Wehrwein, and K.F. Morris, Cardiorespiratory coupling:common rhythms in cardiac, sympathetic, and respiratory activities. Prog Brain Res, 2014. 209: p. 191-205.
7. Antony, A.R., A.V. Alexopoulos, J.A. Gonzalez-Martinez, J.C. Mosher, L. Jehi, R.C. Burgess, N.K. So, and R.F. Galán, Functional connectivity estimated from intracranial EEG predicts surgical outcome in intractable temporal lobe epilepsy. PLoS One, 2013. 8(10): p. e77916.
8. García Domínguez, L., J.L. Pérez Velázquez, and R.F. Galán, A model of functional brain connectivity and background noise as a biomarker for cognitive phenotypes: application to autism. PLoS One, 2013. 8(4): p. e61493.
9. Pérez Velázquez, J.L. and R.F. Galán, Information Gain in the Brain's Resting State: A New Perspective on Autism. Front. Neuroinform., 2013. 7(37).


Dr. Galán received a Master’s degree in Fundamental Physics from the Universidad Autónoma de Madrid, Spain in 1999. He then went to Berlin with a grant of the European Union to pursue graduate studies. In 2003 he received a PhD degree in Computational Neuroscience from the Institute for Theoretical Biology at the Humboldt Universität zu Berlin, Germany. In his doctoral dissertation, Dr. Galán used support-vector classifiers to investigate patterns of neural activity in the olfactory system of an insect and their role in sensory coding and short-term memory. He then moved to Pittsburgh as a research associate to work on theoretical neuroscience with Prof. Bard Ermentrout at the University of Pittsburgh and at the same time, to learn electrophysiology with Prof. Nathan Urban at Carnegie Mellon University. In four years he published ten papers, two in Physical Review Letters, one of which was chosen by Scientific American as one of fifty emerging trends in research, business and policy in 2005. In other papers, he reported a new biophysical phenomenon, “stochastic synchronization” and demonstrated its relevance for brain function. Since 2008 Dr. Galán is an assistant professor in the Department of Neurosciences at Case Western Reserve University where the main focus of his research is in theoretical and computational biology, and the analysis of physiologic signals such as EEG, MEG, ECG and fMRI. Dr. Galán also performs electrophysiological studies on biological neuronal networks with patch-clamp techniques and multielectrode arrays. 

Dr. Galán’s research has been mainly funded by three prestigious foundations: The Alfred P. Sloan Foundation, The Mt. Sinai Health Care Foundation, and The Hartwell Foundation. He has been nominated twice for a Diekhoff Graduate Student Mentorship Award at CWRU in 2011 and 2015.