- Chicharro, D., Panzeri, S. and Haefner, R.M. (2021). Stimulus-dependent relationships between behavioral choice and sensory neural responses. eLife, 10. doi:10.7554/elife.54858.
- Pashkovski, S.L., Iurilli, G., Brann, D., Chicharro, D., Drummey, K., Franks, K.M. … Datta, S.R. (2020). Structure and flexibility in cortical representations of odour space. Nature, 583(7815), pp. 253–258. doi:10.1038/s41586-020-2451-1.
- Makkeh, A., Chicharro, D., Theis, D.O. and Vicente, R. (2019). MAXENT3D_PID: An Estimator for the Maximum-Entropy Trivariate Partial Information Decomposition. Entropy, 21(9). doi:10.3390/e21090862.
- Sanayei, M., Chen, X., Chicharro, D., Distler, C., Panzeri, S. and Thiele, A. (2018). Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4. Nature Communications, 9(1). doi:10.1038/s41467-018-06698-w.
- Chicharro, D., Pica, G. and Panzeri, S. (2018). The Identity of Information: How Deterministic Dependencies Constrain Information Synergy and Redundancy. Entropy, 20(3), pp. 169–169. doi:10.3390/e20030169.
- Pica, G., Piasini, E., Chicharro, D. and Panzeri, S. (2017). Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables. Entropy, 19(9). doi:10.3390/e19090451.
- Chicharro, D. and Panzeri, S. (2017). Synergy and Redundancy in Dual Decompositions of Mutual Information Gain and Information Loss. Entropy, 19(2), pp. 71–71. doi:10.3390/e19020071.
- Thiele, A., Brandt, C., Dasilva, M., Gotthardt, S., Chicharro, D., Panzeri, S. … Distler, C. (2016). Attention Induced Gain Stabilization in Broad and Narrow-Spiking Cells in the Frontal Eye-Field of Macaque Monkeys. Journal of Neuroscience, 36(29), pp. 7601–7612. doi:10.1523/jneurosci.0872-16.2016.
- Brovelli, A., Chicharro, D., Badier, J.-.M., Wang, H. and Jirsa, V. (2015). Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping. The Journal of Neuroscience, 35(37), pp. 12643–12658. doi:10.1523/jneurosci.4892-14.2015.
- Chicharro, D. and Panzeri, S. (2014). Algorithms of causal inference for the analysis of effective connectivity among brain regions. Frontiers in Neuroinformatics, 8. doi:10.3389/fninf.2014.00064.
- Chicharro, D. (2014). A Causal Perspective on the Analysis of Signal and Noise Correlations and Their Role in Population Coding. Neural Computation, 26(6), pp. 999–1054. doi:10.1162/neco_a_00588.
- Chicharro, D. (2014). Parametric and Non-parametric Criteria for Causal Inference from Time-Series. pp. 195–219. doi:10.1007/978-3-642-54474-3_8.
- Kreuz, T., Chicharro, D., Houghton, C., Andrzejak, R.G. and Mormann, F. (2013). Monitoring spike train synchrony. Journal of Neurophysiology, 109(5), pp. 1457–1472. doi:10.1152/jn.00873.2012.
- Chicharro, D. and Ledberg, A. (2012). Framework to study dynamic dependencies in networks of interacting processes. Physical Review E, 86(4). doi:10.1103/physreve.86.041901.
- Chicharro, D. and Ledberg, A. (2012). When Two Become One: The Limits of Causality Analysis of Brain Dynamics. PLoS ONE, 7(3). doi:10.1371/journal.pone.0032466.
- Chicharro, D. (2011). On the spectral formulation of Granger causality. Biological Cybernetics, 105(5-6), pp. 331–347. doi:10.1007/s00422-011-0469-z.
- Chicharro, D., Kreuz, T. and Andrzejak, R.G. (2011). What can spike train distances tell us about the neural code? Journal of Neuroscience Methods, 199(1), pp. 146–165. doi:10.1016/j.jneumeth.2011.05.002.
- Andrzejak, R.G., Chicharro, D., Lehnertz, K. and Mormann, F. (2011). Using bivariate signal analysis to characterize the epileptic focus: The benefit of surrogates. Physical Review E, 83(4). doi:10.1103/physreve.83.046203.
- Kreuz, T., Chicharro, D., Greschner, M. and Andrzejak, R.G. (2011). Time-resolved and time-scale adaptive measures of spike train synchrony. Journal of Neuroscience Methods, 195(1), pp. 92–106. doi:10.1016/j.jneumeth.2010.11.020.
- Kreuz, T., Chicharro, D., Andrzejak, R.G., Haas, J.S. and Abarbanel, H.D.I. (2009). Measuring multiple spike train synchrony. Journal of Neuroscience Methods, 183(2), pp. 287–299. doi:10.1016/j.jneumeth.2009.06.039.
- Chicharro, D. and Andrzejak, R.G. (2009). Reliable detection of directional couplings using rank statistics. Physical Review E, 80(2). doi:10.1103/physreve.80.026217.
- Andrzejak, R.G., Chicharro, D., Elger, C.E. and Mormann, F. (2009). Seizure prediction: Any better than chance? Clinical Neurophysiology, 120(8), pp. 1465–1478. doi:10.1016/j.clinph.2009.05.019.
Dr Daniel Chicharro
Contact details
Address
Dr Daniel Chicharro
City, University of London
Northampton Square
London EC1V 0HB
United Kingdom
Northampton Square
London EC1V 0HB
United Kingdom
About
Overview
Daniel is a lecturer in the Computer Science Department at City and a member of the Artificial Intelligence Research Centre (CitAI). Previously, he was a postdoctoral researcher at Harvard Medical School in the Neurobiology Lab of Dr. John Assad and a postdoctoral researcher at the Italian Institute of Technology, in the Neural Computation Lab of Dr. Stefano Panzeri. Daniel obtained a PhD in Information and Communication Technologies, with specialization on data analysis for neuroscience, at the University Pompeu Fabra, Barcelona, under the supervision of Dr. Ralph G. Andrzejak.
The core of his research has been the development of machine learning and statistical methods to infer the representation of informative features in high-dimensional data, and the application of these methods to understand how the brain represents and processes sensory stimuli and behavioral decisions in the activity of populations of neurons.
More broadly, Daniel is mostly interested in understanding how the causal structure and generative mechanisms of complex systems can be learned from data and exploited to create useful representations of high-dimensional data that are more robust and generalizable across domains. Currently, he is studying how the structure of causal mechanisms is reflected in distinctive sets of independencies between different sources of variability in a system, as well as in its invariance properties. He then exploits these causal trademarks for statistical modeling and to infer the effect of external perturbations or modifications of those mechanisms. The objective of this research is to better characterize complex real-life systems (e.g. neuroscience, genomics) and to improve data representations with causal inference methods also for technological applications.
Languages
English (can read, write, speak and understand spoken).
Research
2nd supervisor
- Giorgio Cavallazzi, Research Student
Publications
Publications by category
Chapter
- Andrzejak, R.G., Chicharro, D. and Mormann, F. (2016). Impact of biases in the false-positive rate on null hypothesis testing. Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics (pp. 241–248). ISBN 978-1-138-07416-3.