Eduardo J. Izquierdo

Assistant Professor
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
Indiana University

Address:
Cognitive Science Program
841 Eigenmann
1900 E. 10th St.
Indiana University
Bloomington, IN 47406

Office: (812) 856-3371
Email: edizquie@indiana.edu

Research Interests

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.

Curriculum Vitae

Recent Publications

(Complete publications here, Publication list in Google Scholar)

Rodriguez, N., Izquierdo, E.J., and Ahn, Y.Y. (In preparation) Optimal modularity arbitrates memory capacity of reservoir computers.

Olivares, E., Izquierdo, E.J., and Beer, R.D. (Submitted) A ventral nerve cord CPG may underlie locomotion in C. elegans.

Aguilera, M., Alquezar, C., and Izquierdo, E.J. (Submitted to ECAL 2017) Signatures of criticality in a maximum entropy model of the C. elegans brain during free behaviour.

Fields, A., and Izquierdo, E.J. (Submitted to ECAL 2017) Reward-Sensitive Preferences Lead to Cooperative Hunting. Extended abstract.

Candadai M.V., and Izquierdo, E.J. (Submitted to ICANN 2017) Information encoding in recurrent spiking neural networks for non-Markovian Pole Balancing.

Setzler M., and Izquierdo, E.J. (2017) Adaptability and Neural Reuse in Minimally Cognitive Agents. Proceedings of the 39th Annual Conference of the Cognitive Science Society. London, UK: Cognitive Science Society.

Candadai M.V., and Izquierdo, E.J. (2017) Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks. Proceedings of The Genetic and Evolutionary Computation Conference. Berlin, Germany: ACM.

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]

Courses

2016-2017

C105: Mind as Machines (Fall 2016).

Q530: Programming Methods for Cognitive Science (Fall 2016).

Q260/Q320: Computation in Cognitive Science (Spring 2017).

2015-2016

Computation in Cognitive Science (Q260/Q320). Spring 2016. Canvas. Syllabus.

Brains & Minds, Robots & Computers (C105). Spring 2016. Canvas. Syllabus. Schedule.

Modeling Evolutionary, Adaptive and Cognitive Systems (Q700). Fall 2015. Canvas. Syllabus.

2012-2013

Computation in Cognitive Science (Q260/Q320). Spring 2013.

Programming Methods for Cognitive Science (Q530). Fall 2012.

Math & Logic for Cognitive Science (Q250). Fall 2012.

Page updated April 2017.