Eduardo J. Izquierdo
Associate Professor
Electrical and Computer Engineering
Rose-Hulman Institute of Technology
Contact:
Office: F121 Moench Hall
Phone: 812-877-8357
Email: izquierd@rose-hulman.edu
PhD Students
Lindsay Stolting
Zachary Laborde
Andrew Claros
Josh Nunley
Haily Merritt
Dr. Madhavun Candadai
Dr. Jason Yoder
Research Interests
My research interest is to better understand living organisms through the development of artificial systems that can match their robustness, flexibility, adaptability, and intelligence. An important part of my approach is to understand how organisms operate as integrated wholes, with a particular focus on how behavior arises from the interaction between brains, bodies, and environments. Toward this end, I develop and analyze computational models of complete brain-body-environment systems for idealized model agents as well as for neuromechanical models of animals. My approach integrates research on artificial neural networks, lifetime learning techniques, evolutionary algorithms, information theory, and dynamical systems theory.
Areas of interest: Computational Neuroethology, Bio-Inspired Artificial Intelligence, Embodied Cognition, Computational Neuroscience, Evolutionary Robotics, Artificial Life, Complex Systems, Cognitive Science, and Evolutionary Computation.
Most recent Publications
(Complete publications here Publication list in Google Scholar)
Merritt H, Severino GJ, Izquierdo EJ (Submitted) The Dynamics of Social Interaction among Evolved Model Agents. Journal of Artificial Life.
Gaul TM, Izquierdo EJ (Submitted) Cognitive distinctions and minimal cognition: Analysis of a referential communication task without pregiven distinctions. Journal of Artificial Life.
Yao S, Nunley J, Izquierdo EJ (2023) Go by Its Name: Evolution and Analysis of Conceptual Referential Communication. Artificial Life Conference 2023. MIT Press. doi: 10.1162/isal_a_00669.
Laborde Z, Izquierdo EJ (2023) Spatial Embedding of Edges in a Synaptic Generative Model of C. elegans. Artificial Life Conference 2023. MIT Press. doi: 10.1162/isal_a_00611
Severino GJ, Merritt H, Izquierdo EJ (2023) Between you and me: A systematic analysis of mutual social interaction in perceptual crossing agents. Artificial Life Conference 2023. MIT Press. doi: 10.1162/isal_a_00609
Stolting L, Beer RD, Izquierdo EJ (2023) Characterizing the Role of Homeostatic Plasticity in Central Pattern Generators. Artificial Life Conference 2023. MIT Press. doi: 10.1162/isal_a_00599
Iizuka H, Suzuki K, Uno R, Damiano L, Spychala N, Aguilera M, Izquierdo EJ, Suzuki R, Baltieri M (2023). Ghost in the machine. ALIFE 2023 Proceedings. MIT Press.
Izquierdo EJ, Severino G, Merrit H (2022) Perpetual crossers without sensory delay: revisiting the perceptual crossing simulation studies. Artificial Life Conference 2022.
Izquierdo EJ, Candadai, M (2022) What does functional connectivity tell us about the behaviorally functional connectivity of a multifunctional neural circuit? Artificial Life Conference 2022.
Yoder J, Cooper A, Cehong W, Izquierdo EJ (2022) Reinforcement learning for central pattern generation in dynamical recurrent neural networks. Frontiers in Computational Neuroscience. doi: 10.3389/fncom.2022.818985
Campbell CM, Izquierdo EJ, Goldstone RL (2022) Partial copying and the role of diversity in social learning performance. Collective Intelligence. doi: 10.1177/263391372210818
Olivares, E, Izquierdo EJ, Beer RD (2021) A neuromechanical model of multiple network rhythmic pattern generators for forward locomotion in C. elegans. Frontiers in Computational Neuroscience 15:572339. doi: 10.3389/fncom.2021.572339
Ikeda M, Matsumoto H, Izquierdo EJ (2021). Persistent thermal input controls steering behavior in Caenorhabditis elegans. PLOS Computational Biology 17(1), e1007916. doi:10.1371/journal.pcbi.1007916
Leite A, Izquierdo EJ (2021). Generating reward structures on a parameterized distribution of dynamics tasks. Artificial Life Conference Proceedings. doi: 10.1162/isal_a_00466.
Candadai M, Izquierdo EJ (2020). Sources of predictive information in dynamical neural networks. Nature Scientific Reports 10, 16901. doi:10.1038/s41598-020-73380-x. Winner of the 2021 ISAL (International Society of Artificial Life) Outstanding Student Paper Award.
Candadai MV, Izquierdo EJ (2020). infotheory: A C++/Python package for multivariate information theoretic analysis. Journal of Open Source Software 5(47), 1609. doi: 10.21105/joss.01609.
Leite A, Candadai M, Izquierdo EJ (2020). Reinforcement learning beyond the Bellman equation: Exploring critic objectives using evolution. Artificial Life Conference Proceedings, 441–449. doi: 10.1162/isal_a_00338
Benson, L., Candadai, M., Izquierdo EJ (2020). Neural reuse in multifunctional neural networks for control tasks. Artificial Life Conference Proceedings, 210–218. doi: 10.1162/isal_a_00319
Dahlberg, B., Izquierdo EJ (2020). Contributions from parallel strategies for spatial orientation in C. elegans. Artificial Life Conference Proceedings, 16–24. doi: 10.1162/isal_a_00346
Luthra, M. Izquierdo EJ, Todd P (2020). Cognition evolves with the emergence of environmental patchiness. Artificial Life Conference Proceedings, 450–458. doi: 10.1162/isal_a_00330
Todd G., Candadai M., Izquierdo EJ. (2020). Interaction between evolution and learning in NK fitness landscapes. Artificial Life Conference Proceedings, 751-767. doi: 10.1162/isal_a_00331
Sheybani S, Izquierdo EJ, Eatai, R. (2020). Exploring dyadic strategies for cooperative physical tasks. 2020 IEEE Haptics Symposium (HAPTICS), 684-689. doi: 10.1109/HAPTICS45997.2020.ras.HAP20.26.5d3bec79.
Rodriguez, N., Izquierdo, E.J., Ahn, Y.Y. (2019) Optimal modularity and memory capacity of neural reservoirs. Network Neuroscience3(2):551-566. doi: 10.1162/netn_a_00082.
Candadai MV, Setzler M, Izquierdo EJ, Froese T. (2019) Embodied dyadic interaction increases complexity of neural dynamics: A minimal agent-based simulation model. Frontiers in Psychology 21;10:540. doi:10.3389/fpsyg.2019.00540
Izquierdo EJ (2019) Role of simulation models in understanding the generation of behavior in C. elegans. Special Issue: Systems biology of model organisms. Current Opinion in Systems Biology 13:93-101 doi:10.1016/j.coisb.2018.11.003
Izquierdo EJ, Beer RD (2018) From head to tail: An integrated neuromechanical model of forward locomotion in C. elegans. Special Issue: From Connectome to Behavior. Philos Trans R Soc Lond B Biol Sci. 373(1758): 20170374. doi: 10.1098/rstb.2017.0374
Froese T, Izquierdo EJ. (2018) A dynamical approach to the phenomenology of body memory: Past interactions can shape present capacities without neuroplasticity. Journal of Consciousness Studies 25(7-8):20-46.
Siqueiros JM, Froese T, Gerhenson C, Aguilar W, Sayama H, Izquierdo EJ (2018) ALife and Society: Editorial Introduction to the Artificial Life Conference 2016 Special Issue. Artificial Life 24(1):1-4. MIT Press.
Olivares, E., Izquierdo, E.J., and Beer, R.D. (2018) Potential role of a ventral nerve cord central pattern generator in forward and backward locomotion in Caenorhabditis elegans. Network Neuroscience 2(3): 323–343. doi: 10.1162/netn_a_00036
Aguilera, M., Alquezar, C., and Izquierdo, E.J. (2017) Signatures of criticality in a maximum entropy model of the C. elegans brain during free behaviour. Proceedings of the 14th European Conference of Artificial Life. Lyon, France.
Candadai M.V., and Izquierdo, E.J. (2017) Information Bottleneck in Control Tasks with Recurrent Spiking Neural Networks. Proceedings of the 26th International Conference on Artificial Neural Networks. Sardinia, Italy.
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.
Teaching
ECE233: Introduction to Digital Systems (syllabus)
C105: Mind as Machines: Brains & Minds, Robots & Computers. (syllabus) [materials]
Q530: Programming Methods for Cognitive Science (syllabus) [materials]
Q260: Introduction to Programming in Cognitive Science (syllabus) [materials]
Q320: Applications of Programming in Cognitive Science (syllabus) [materials]
Q700: Modeling Evolutionary and Adaptive Systems (syllabus) [materials]
Service
Diversity and Outreach
Diversity and outreach statement
Public Software
Minimal C. elegans connectome circuit search [Mathematica]
C. elegans connectome explorer (in collaboration with Aditya Ramesh) [Python]
Neuromechanical model of locomotion in C. elegans (in collaboration with Dr. Erick Olivares and Prof. Randall Beer) [C++]
Neural model of spatial orientation in C. elegans (in collaboration with Prof. Shawn Lockery and Prof. Randall Beer) [C++]
Neural network: Continuous-Time Recurrent Neural Network with Gap Junctions (CTRNN+GJ) (in collaboration with Prof. Randall Beer) [C++]
Neural network: Homeostatic Plasticity CTRNN (CTRNN+HP) (in collaboration with Lindsay Stolting) [C++]
Neural network: Reward-modulated CTRNN (CTRNN+RL) (in collaboration with Dr. Jason Yoder) [C++]
Evolutionary algorithm: Microbial Genetic Algorithm (adapted from Prof. Inman Harvey) [Python]
Tools of analysis: C++ and Python package for multivariate information theoretic analyses on discrete and continuous data (in collaboration with Dr. Madhavun Candadai) [Python]
Tools of analysis: Network Neuroethology Analysis (in collaboration with Dr. Madhavun Candadai) [Python]
Tasks: Visually guided agent (in collaboration with Randall Beer and Matt Setzler) [C++]
Tasks: Perceptual crossing [C++]
Evolution and Learning in NK Fitness Landscapes (in collaboration with Graham Todd and Dr. Madhavun Candadai) [Python]
Social Learning in NK Fitness Landscapes (in collaboration with Chelsea Campbell and Prof. Rob Goldstone) [Python]
Meta-Learning by the Baldwin Effect in NK Fitness Landscapes [Python]
Tool for teaching dynamical systems: Lotka-Volterra [Python]
Tool for teaching dynamical systems: Izhikevich Neuron and Network of Neurons [Python]
Tool for teaching neural networks: Perceptron, Feedforward Neural Network, and Backpropagation from Scratch [Python (2-layer)] [Python (N-layer)]
Tool for teaching agent-based modeling: Schelling Model [Python]
Tool for teaching about Dynamic Recurrent Neural Networks: Continuous-Time Recurrent Neural Network (CTRNN) [Python]
Agent-based model: Spatial SIR [Python]
Agent-based model: Opinion Dynamics [Python]
Agent-based model: Mycorrhizal Network Dynamics [Python]