Evolutionary & Adaptive Systems Research Group at IU Bloomington


We are trying to understand how adaptive behavior arises from the interaction of networks of neurons, bodies, and environments.

Research

We are motivated by one central question: 

Neural basis of behaviors in C. elegans: Spatial orientation

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Neuromechanical basis of behaviors in C. elegans: Locomotion

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Neural reuse in embodied multifunctional circuits

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Interaction between evolution and learning

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Members

Eduardo J. Izquierdo

Principal Investigator
Eigenman 543
edizquie@iu.edu
Website

Madhavun Candadai

Graduate student
madcanda@iu.edu
Website

Person 2

Role / Position
Email[at]iu.edu
Website

Person 3

Role / Position
Email[at]iu.edu
Website

Person 4

Role / Position
Email[at]iu.edu
Website

Person 4

Role / Position
Email[at]iu.edu
Website

Person 5

Role / Position
Email[at]iu.edu
Website

Past members

Publications

See Google Scholar for a complete list of publications.

Dahlberg, B. and Izquierdo, E.J. (Submitted) Environmental specialization of dual strategies in chemotaxis of Caenorhabditis elegans. ALife2020. [pdf] [github]
Yoder, J. and Izquierdo, E.J. (Submitted) Interaction between development and evolution in NK fitness landscapes. ALife2020. [pdf] [github]
Leite, L., Candadai, M.V. and Izquierdo, E.J. (Submitted) Reinforcement learning beyond the Bellman equation: Exploring critic objectives using evolution. ALife2020. [pdf] [github]
Todd, G., Candadai, M.V. and Izquierdo, E.J. (Submitted) Interaction between evolution and learning in NK fitness landscapes. ALife2020. [pdf] [github]
Luthra, M., Izquierdo, E.J. and Todd, T. (Submitted) Cognition evolves with the emergence of environmental patchiness. Alife2020. [pdf] [github]
Khare, S., and Izquierdo, E.J. (Submitted) From dynamics to structure: Parameter estimation for continuous-time recurrent neural networks. Alife2020. [pdf] [github]
Benson, L., Candadai, M.V. and Izquierdo, E.J. (Submitted) Neural reuse in multifunctional circuits for control tasks. Alife2020. [pdf] [github]
Ikeda, M., Matsumoto, H., and Izquierdo, E.J. (Submitted) Persistent thermal input controls steering behavior in Caenorhabditis elegans . PLoS Comp Bio. [pdf] [github]
Candadai, M.V, and Izquierdo, E.J. (Under review) Sources of predictive information in dynamical neural networks. Scientific Reports. [pdf] [github]
Campbell, C. Izquierdo, E.J. (2020) How much to copy from others? The role of partial copying in social learning. Proceedings of the 41st Annual Conference of the Cognitive Science Society. [pdf] [github]
Rodriguez, N., Izquierdo, E.J., and Ahn, Y.Y. (2019) Optimal modularity and memory capacity of neural reservoirs. Network Neuroscience 3(2):551-566. doi:10.1162/netn_a_00082. [pdf] [github]
Izquierdo, E.J. and Beer, R.D. (2018) From head to tail: A neuromechanical model of forward locomotion in C. elegans. Philos Trans R Soc Lond B Biol Sci. 373(1758):20170374. doi:10.1098/rstb.2017.0374. [pdf] [github]
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. doi:10.1162/ARTL_e_00256 [pdf] [github]
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. [pdf] [github]
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. [pdf] [github]
Candadai M.V., and Izquierdo, E.J. (2017) Information bottleneck in control tasks with recurrent spiking neural networks. In: Lintas A., Rovetta S., Verschure P., Villa A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. Lecture Notes in Computer Science, vol 10613. pp:243-244. Springer, Cham. doi:10.1007/978-3-319-68600-4_28 [pdf] [github]
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. [pdf] [github]
Candadai, M.V., and Izquierdo, E.J. (2017) Evolution and analysis of embodied spiking neural networks reveals task-specific clusters of efective networks. Proceedings of the Genetic and Evolutionary Computation Conference. pp: 75–82. doi:10.1145/3071178.3071336 [pdf] [github]

Software

See our Github page for a complete list of software and data from publications.

Neural network models

CTRNN: Continuous-time recurrent neural network (Python). Simple, nonlinear, continuous dynamical neural network model. For more detail see Beer (1995).
Izhikevich: Izhikevich single neuron model (Python). Simple spiking neuron model of cortical neurons. For more detail see Izhikevich (2003).
IzhikevichNN: A network of Izhikevich neurons (Python).
Perceptron: Perceptron (Python). Used primarily for teaching. For more detail see Wikipedia.
FANN: Multilayer feedforward neural network (Python).

Embodied control tasks

Cartpole: Continuous-action cartpole balancer (Python). A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a continuous force between [-1,1] to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. For more detail see Barto, Sutton, and Anderson (1983).
InvPen: Continuous-action inverted pendulum swingup (Python). The inverted pendulum swingup problem is a classic problem in the control literature. In this version of the problem, the pendulum starts in a random position, and the goal is to swing it up so it stays upright. Adapted from Open AI.
SLWalker: Single-legged walker (Python). A body with a single leg with a foot, and effectors to move them. The leg is connected to the body by a joint which allows effectors to apply clockwise and counterclockwise torques. For more detail see Beer and Gallagher (1992).

Embodied cognitive tasks

ShapeDiscriminator: Circle-line discriminator.
PerceptualCrosser: Perceptual crossing task.
TBD: description

Evolutionary algorithms

MGA: Microbial Genetic Algorithm for stochastic fitness functions (Python). A stripped down minimal version of an evolutionary algorithm with horizontal gene transfer and tournament selection. For more detail see Harvey (2009).
MGA-Det: Microbial Genetic Algorithm for deterministic fitness function (Python)
EA: Rank-based generational algorithm (Python)

Agent-based models and other Alife models

Segregation: Schelling’s Segregation Model (Python)
Braitenberg: Braitenberg vehicles (Python)
Lotka-Volterra: Lotka-Volterra (Python)
NLV: N-species competitive Lotka-Volterra (Python)
NK: Kauffman’s NK fitness landscapes adapted from Kauffman and Levin (1987) (Python).
WealthDynamics: Minimal model of wealth dynamics adapted from Scheffer et al., (2017) Supp. Mat. Sect. 4 (Python).
VirusBeliefSpread: Model of virus and belief spread (Python)

Teaching

Q700 - Modeling evolutionary and adaptive systems(Fall 2015, Spring 2018, Spring 2019). Graduate seminar designed to provide a hands-on introduction to different approaches to modeling and understanding adaptive processes underlying cognition in living organisms. Adaptation is studied at both the evolutionary and the lifetime scales. The seminar explore how simulation models can inform our understanding of cognition and adaptive behavior.
C105 - Brains, minds, robots, and computers (Fall 2016, Fall 2017, Fall 2019, Fall 2020). Course desgined to explore the main thrusts in cognitive science and artificial intelligence. The topics will general questions about intelligence and the mechanistic view of cognition. We compare and constrast the abilities of the humans and animals with the current capabilities of machines and robots.
Q530 - Introduction to programming for Cognitive Scientists (Fall 2012, Fall 2016, Y, Z, Spring 2021)
Q320 - Programming methods for Cognitive Scientists (Spring 2013, Spring 2016, Z, Spring 2020). Course designed to refine your computer programming and problem-solving skills, and to acquaint you with applications of programming in cognitive science.
Q260 - Introduction to programming for Cognitive Scientists (Spring 2013, Spring 2016).