Cognitive Science Program

Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans (2010)
Eduardo J Izquierdo and Shawn R Lockery
Journal of Neuroscience, 30 (39), 12908-12917

Abstract

Chemotaxis during sinusoidal locomotion in nematodes captures in simplified form the general problem of how dynamical interactions between the nervous system, body, and environment are exploited in the generation of adaptive behavior. We used an evolutionary algorithm to generate neural networks that exhibit klinotaxis, a common form of chemotaxis in which the direction of locomotion in a chemical gradient closely follows the line of steepest ascent. Sensory inputs and motor outputs of the model networks were constrained to match the inputs and outputs of the <i>Caenorhabditis elegans</i> klinotaxis network. We found that a minimalistic neural network, comprised of an ON-OFF pair of chemosensory neurons and a pair of neck muscle motor neurons, is sufficient to generate realistic klinotaxis behavior. Importantly, emergent properties of model networks reproduced two key experimental observations that they …

Autonomy: a review and a reappraisal (2007)
Tom Froese, Nathaniel Virgo and Eduardo Izquierdo
Springer, Berlin, Heidelberg. 455-464

Abstract

In the field of artificial life there is no agreement on what defines ‘autonomy’. This makes it difficult to measure progress made towards understanding as well as engineering autonomous systems. Here, we review the diversity of approaches and categorize them by introducing a conceptual distinction between <i>behavioral</i> and <i>constitutive</i> autonomy. Differences in the autonomy of artificial and biological agents tend to be marginalized for the former and treated as absolute for the latter. We argue that with this distinction the apparent opposition can be resolved.

Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis (2013)
Eduardo J Izquierdo and Randall D Beer
PLoS computational biology, 9 (2), e1002890

Abstract

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives …

A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans (2016)
William M Roberts, Steven B Augustine, Kristy J Lawton, Theodore H Lindsay, Tod R Thiele, Eduardo J Izquierdo ...
Elife, 5 e12572

Abstract

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode <i>Caenorhabditis elegans</i>, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the <i>C. elegans</i> connectome and fit to a large-scale kinematic analysis of <i>C. elegans</i> behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in <i>C. elegans</i> can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding <i>C. elegans</i> locomotion and a testable neuronal model of random search that can be applied to other organisms.

Associative learning on a continuum in evolved dynamical neural networks (2008)
Eduardo Izquierdo, Inman Harvey and Randall D Beer
Adaptive Behavior, 16 (6), 361-384

Abstract

This article extends previous work on evolving learning without synaptic plasticity from discrete tasks to continuous tasks. Continuous-time recurrent neural networks without synaptic plasticity are artificially evolved on an associative learning task. The task consists in associating paired stimuli: temperature and food. The temperature to be associated can be either drawn from a discrete set or allowed to range over a continuum of values. We address two questions: Can the learning without synaptic plasticity approach be extended to continuous tasks? And if so, how does learning without synaptic plasticity work in the evolved circuits? Analysis of the most successful circuits to learn discrete stimuli reveal finite state machine (FSM) like internal dynamics. However, when the task is modified to require learning stimuli on the full continuum range, it is not possible to extract a FSM from the internal dynamics. In this case, a …

Analysis of a Dynamical Recurrent Neural Network Evolved for Two Qualitatively Different Tasks: Walking and Chemotaxis (2008)
Eduardo Izquierdo and Thomas Bührmann
257-264

Abstract

Living organisms perform a broad range of different behaviours during their lifetime. It is important that these be coordinated such as to perform the appropriate one at the right time. This paper extends previous work on evolving dynamical recurrent neural networks by synthesizing a single circuit that performs two qualitatively different behaviours: orientation to sensory stimuli and legged locomotion. We demonstrate that small fully interconnected networks can solve these two tasks without providing a priori structural modules, explicit neural learning mechanisms, or an external signal for when to switch between them. Dynamical systems analysis of the best-adapted circuit explains the agent’s ability to switch between the two behaviours from the interactions of the circuit’s neural dynamics, its body and environment.

Is an embodied system ever purely reactive? (2005)
Eduardo Izquierdo-Torres and Ezequiel Di Paolo
Springer, Berlin, Heidelberg. 252-261

Abstract

This paper explores the performance of a simple model agent using a reactive controller in situations where, from an external perspective, a solution that relies on internal states would seem to be required. In a visually-guided orientation task with sensory inversion and an object discrimination task a study of the instantaneous response properties and time-extended dynamics explain the non-reactive performance. The results question common intuitions about the capabilities of reactive controllers and highlight the significance of the agent’s recent history of interactions with its environment in generating behaviour. This work reinforces the idea that embodied behaviour exhibits properties that cannot be deduced directly from those of the controller by itself.

The whole worm: brain–body–environment models of C. elegans (2016)
Eduardo J Izquierdo and Randall D Beer
Elsevier Current Trends. 40 23-30

Abstract

Brain, body and environment are in continuous dynamical interaction, and it is becoming increasingly clear that an animal's behavior must be understood as a product not only of its nervous system, but also of the ongoing feedback of this neural activity through the biomechanics of its body and the ecology of its environment. Modeling has an essential integrative role to play in such an understanding. But successful whole-animal modeling requires an animal for which detailed behavioral, biomechanical and neural information is available and a modeling methodology which can gracefully cope with the constantly changing balance of known and unknown biological constraints. Here we review recent progress on both optogenetic techniques for imaging and manipulating neural activity and neuromechanical modeling in the nematode worm <i>Caenorhabditis elegans</i>. This work demonstrates both the feasibility and …

Information flow through a model of the C. elegans klinotaxis circuit (2015)
Eduardo J Izquierdo, Paul L Williams and Randall D Beer
PloS one, 10 (10), e0140397

Abstract

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit’s state-dependent response. (4) The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally …

An integrated neuromechanical model of steering in C. elegans (2015)
Eduardo J Izquierdo and Randall D Beer
Artificial Life Conference Proceedings 13, 199-206

Abstract

In this paper, we extend our previous model circuit for steering in C. elegans to control a more realistic biomechanical model of forward locomotion. We show that the identified steering circuit is sufficient to steer the full body during forward locomotion while only innervating a few of the anterior most neck muscles. Analysis of the sensorimotor transformation and phasic stimulation experiments provides evidence that the principles of operation for steering discussed in the model are relevant for steering in the worm. Finally, the integration of the steering circuit in a physical model of the full body allows us to compare more closely the properties of the evolved solutions with those of the worm.

Potential role of a ventral nerve cord central pattern generator in forward and backward locomotion in Caenorhabditis elegans (2018)
Erick O Olivares, Eduardo J Izquierdo and Randall D Beer
Network Neuroscience, 2 (3), 323-343

Abstract

<i>C. elegans</i> locomotes in an undulatory fashion, generating thrust by propagating dorsoventral bends along its body. Although central pattern generators (CPGs) are typically involved in animal locomotion, their presence in <i>C. elegans</i> has been questioned, mainly because there has been no evident circuit that supports intrinsic network oscillations. With a fully reconstructed connectome, the question of whether it is possible to have a CPG in the ventral nerve cord (VNC) of <i>C. elegans</i> can be answered through computational models. We modeled a repeating neural unit based on segmentation analysis of the connectome. We then used an evolutionary algorithm to determine the unknown physiological parameters of each neuron so as to match the features of the neural traces of the worm during forward and backward locomotion. We performed 1,000 evolutionary runs and consistently found configurations of the …

Collective intelligence in multi-agent robotics: Stigmergy, self-organization and evolution (2004)
Eduardo Izquierdo-Torres
University of Sussex,

Abstract

Nature has been able to evolve (several times) natural systems which produce complex spatio-temporal patterns from agents with very simple behaviours by exploiting the interactions between the agents and their environment. Surprisingly, the systematic use of these principles have been mostly neglected within the field of collective robotics. We study the use of these biological principles in nature and their application in artificial systems and hypothesize on the role between such principles and evolution. We conclude with future directions towards collective intelligence in multi-agent robotics.

Hebbian Learning using Fixed Weight Evolved Dynamical 'Neural' Networks (2007)
Eduardo Izquierdo-Torres and Inman Harvey
IEEE. 394-401

Abstract

We evolve small continuous-time recurrent neural networks with fixed weights that perform Hebbian learning behavior. We describe the performance of the best and smallest successful system, providing an in-depth analysis of its evolved mechanisms. Learning is shown to arise from the interaction between the multiple timescale dynamics. In particular, we show how the fast-time dynamics alter the slow-time dynamics, which in turn shapes the local behavior around the equilibrium points of the fast components by acting as a parameter to them

Analysis of ultrastability in small dynamical recurrent neural networks (2013)
Eduardo Izquierdo, Miguel Aguilera and Randall Beer
Artificial Life Conference Proceedings 13, 51-58

Abstract

This paper reconsiders Ashby’s framework of adaptation within the context of dynamical neural networks. Agents are evolved to behave as an ultrastable dynamical system, without imposing a priori the nature of the behavior-changing mechanisms, or the strategy to explore the space of possible dynamics in the system. We analyze the resulting networks using dynamical systems theory for some of the simplest conditions. The picture that emerges from our analysis generalizes the idea of ultrastable mechanisms.

The dynamics of learning behaviour: a situated, embodied, and dynamical systems approach (2008)
Eduardo J Izquierdo