Eduardo J. Izquierdo
Cognitive Science Program
Program in Neuroscience
School of Informatics and Computing
Member, Indiana University Network Science Institute
Member, Center for Complex Networks and Systems Research
Member, Center for the Integrative Study of Animal Behavior
Cognitive Science Program
1900 E. 10th St.
Bloomington, IN 47406
Office: (812) 856-3371
My research interest is in understanding the neural basis of behavior, as it arises from the interaction between the organism’s nervous system, its body, and its environment. I combine connectome graph analysis, neural network simulations, evolutionary algorithms for optimization, taking into account experimental observations, and mathematical analysis, including information theory and dynamical systems theory, to generate and understand complete brain-body-environment models of simple but biologically and cognitively interesting behaviors. (Full research statement).
Areas of interest: Embodied Cognition, Computational Neuroscience, Evolutionary Robotics, Artificial Life, Complex Systems.
If you are interested in this area of research and want to do an independent study or a project with me, let me know.
Izquierdo, E.J., and Beer, R.D. (2016) The whole worm: brain–body–environment models of C. elegans. Current Opinion in Neurobiology 40:23–30. doi:10.1016/j.conb.2016.06.005
Roberts WM, Augustine SB, Lawton KJ, Lindsay TH, Thiele TR, Izquierdo EJ, Faumont S, Lindsay RA, Britton MC, Pokala N, Bargmann CI, Lockery SR (2016) A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans. eLife 2016;10.7554/eLife.12572.
Izquierdo, E.J., Williams, P. and Beer, R.D. (2015) Information flow through the C. elegans klinotaxis circuit. PLoS ONE 10(10):e0140397. doi:10.1371/journal.pone.0140397.
Izquierdo, E.J. and Beer, R.D. (2015). An integrated neuromechanical model of steering in C. elegans. In the Proceedings of ECAL 2015 (pp. 199-206). MIT Press.
Izquierdo, E.J., Aguilera, M. and Beer, R.D. (2013). Analysis of ultrastability in small dynamical recurrent neural networks. In P. Lio, O. Miglino, G. Nicosia, S. Nolfi & M. Pavone (Eds.), Advances in Artificial Life: ECAL 2013 (pp. 51-58).
Izquierdo, E.J., and Beer, R.D. (2013) Connecting a connectome to behavior: An ensemble of neuroanatomical models of C. elegans klinotaxis. PLoS Computational Biology.
Izquierdo, E.J., and Lockery, S.R. (2010) Evolution and analysis of minimal neural circuits for klinotaxis in C. elegans. Journal of Neuroscience 30:12908-12817.
Izquierdo, E.J., Harvey, I. and Beer, R.D. (2008) Associative learning on a continuum in evolved dynamical neural networks. Journal of Adaptive Behavior. Adaptive Behavior 16, 361-384. [Preprint]
Computation in Cognitive Science (Q260/Q320). Spring 2016. Canvas. Syllabus.
Brains & Minds, Robots & Computers (C105). Spring 2016. Canvas. Syllabus. Schedule.