The state of MIIND

MIIND (Multiple Interacting Instantiations of Neural Dynamics) is a highly modular multi-level C++ framework, that aims to shorten the development time for models in Cognitive Neuroscience (CNS). It offers reusable code modules (libraries of classes and functions) aimed at solving problems that occur repeatedly in modelling, but tries not to impose a specific modelling philosophy or methodology. At the lowest level, it offers support for the implementation of sparse networks. For example, the library SparseImplementationLib supports sparse random networks and the library LayerMappingLib can be used for sparse regular networks of filter-like operators. The library DynamicLib, which builds on top of the library SparseImplementationLib, offers a generic framework for simulating network processes. Presently, several specific network process implementations are provided in MIIND: the Wilson–Cowan and Ornstein–Uhlenbeck type, and population density techniques for leaky-integrate-and-fire neurons driven by Poisson input. A design principle of MIIND is to support detailing: the refinement of an originally simple model into a form where more biological detail is included. Another design principle is extensibility: the reuse of an existing model in a larger, more extended one. One of the main uses of MIIND so far has been the instantiation of neural models of visual attention. Recently, we have added a library for implementing biologically-inspired models of artificial vision, such as HMAX and recent successors. In the long run we hope to be able to apply suitably adapted neuronal mechanisms of attention to these artificial models

Efficient Numerical Population Density Techniques with an Application in Spinal Cord Modelling

MIIND is a neural simulator which uses an innovative numerical population density technique to simulate the behaviour of multiple interacting populations of neurons under the influence of noise. Recent efforts have produced similar techniques but they are often limited to a single neuron model or type of behaviour. Extensions to these require a great deal of further work and specialist knowledge. The technique used in MIIND overcomes this limitation by being agnostic to the underlying neuron model of each population. However, earlier versions of MIIND still required a high level of technical knowledge to set up the software and involved an often time-consuming manual pre-simulation process. It was also limited to only two-dimensional neuron models. This thesis presents the development of an alternative population density technique, based on that already in MIIND, which reduces the pre-simulation step to an automated process. The new technique is much more flexible and has no limit on the number of time-dependent variables in the underlying neuron model. For the first time, the population density over the state space of the Hodgkin-Huxley neuron model can be observed in an efficient manner on a single PC. The technique allows simulation time to be significantly reduced by gracefully degrading the accuracy without losing important behavioural features. The MIIND software itself has also been simplified, reducing technical barriers to entry, so that it can now be run from a Python script and installed as a Python module. With the improved usability, a model of neural populations in the spinal cord was simulated in MIIND. It showed how afferent signals can be integrated into common reflex circuits to produce observed patterns of muscle activation during an isometric knee extension task. The influence of proprioception in motor control is not fully understood as it can be both task and subject-specific. The results of this study show that afferent signals have a significant effect on sub-maximal muscle contractions even when the limb remains static. Such signals should be considered when developing methods to improve motor control in activities of daily living via therapeutic or mechanical means

Phase Transitions Induced by Diversity and Examples in Biological Systems

Tesis leída en l'Universitat de les Illes Balears en diciembre de 2010The present thesis covers various topics that range over di erent aspects of scientific research. On one end there is the specific analysis of a precise form that models some experimental observations. A good theoretical understanding of the mathematics that describe the observations can be a guide to the experimentalist and help estimate the validity of the measurements. On the other end there are abstract models whose relation to physical systems seem far but they are prototypic for a broad range of di erent systems and the drawn conclusions tend to be quite general. Depending on the abstraction and on the simplifications in use the distinction between both ends might not be sharp. The ordering of the research results presented in part II of this thesis somehow reflects the seamless transition from one end to the other. To introduce the reader into the context of the genuine results we provide introductory material in the chapters of the present part I.Peer reviewe

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010

Control of pattern formation in excitable systems

Pattern formation embodies the beauty and complexity of nature. Some patterns like traveling and rotating waves are dynamic, while others such as dots and stripes are static. Both dynamic and static patterns have been observed in a variety of physiological and biological processes such as rotating action potential waves in the brain during sleep, traveling calcium waves in the cardiac muscle, static patterns on the skins of animals, and self-regulated patterns in the animal embryo. Excitable systems represent a class of ultrasensitive systems that are capable of generating different kinds of patterns depending on the interplay between activator and inhibitor dynamics. Through manipulation of different excitable parameters, a diverse array of traveling wave and standing wave patterns can be obtained. In this thesis, I use pattern formation theory to control the excitable systems involved in cell migration and neuroscience to alter the observed phenotype, in an attempt to affect the underlying biological process. Cell migration is critical in many processes such as cancer metastasis and wound healing. Cells move by extending periodic protrusions of their cortex, and recent years have shown that the cellular cortex is an excitable medium where waves of biochemical species organize the cellular protrusion. Altering the protrusive phenotype can drastically alter cell migration — that can potentially affect critical physiological processes. In the first part of this thesis, I use excitable wave theory to model and predict wave pattern changes in amoeboid cells. Using theories of pattern formation, key nodes of the underlying excitable network governing cell migration are altered — to drastically change the cellular migratory phenotype, moving from amoeboid cells to oscillatory cells and from cells that extend long finger-like protrusions to cells that sustain stable rings on the cortex, potentially uncovering a novel method of pattern formation. Excitable systems originated in neuroscience, where different patterns of activity reflect different brain states. Sleep is associated with slow waves, while repeated high-frequency waves are associated with epileptic seizures. These patterns arise from the interplay between the cerebral cortex and the thalamus, which form a closed-loop architecture. In the second part of this thesis, I use a three-layer two-dimensional thalamocortical model, to explore the different parameters that may influence different spatio-temporal dynamics on the cortex. This study reveals that inter- and intra-cortical connectivity, excitation-inhibition balance and synaptic strengths can influence the wave activity patterns, to recreate different dynamic patterns observed in different brain states

Modelling Decision Making under Uncertainty: Machine Learning and Neural Population Techniques

This thesis investigates mechanisms of human decision making, building on the fields of psychology and computational neuroscience. I focus on human decision making measured in a psychological task with probabilistic rewards. I examine the fit of different styles of computational models to human behaviour in the task. I show that my modification to reinforcement learning, using parameters based on whether the previous trial resulted in a win or a loss, is a better fit to behaviour than my Bayesian models. Considering the task from a machine learning perspective, with the goal of gaining as many rewards as possible rather than modelling human behaviour, the performance of my modified reinforcement learning model is similar to that of my Bayesian learner and superior to that of a standard reinforcement learning model. Using population density techniques to simulate neural interactions, I confirm earlier research that demonstrates conditions which induce oscillations in a system consisting of just two nodes. I extend those findings by showing how the underlying states of the neurons contribute to complex patterns of activity. The basal ganglia form part of the brain known to be important in decision making. I create a computational model of the basal ganglia to simulate decision making. As oscillatory neural activity is known to occur in the basal ganglia, I add such activity to the model and study its impact on the decisions made. I use the time that activation first falls below a threshold as a criterion for decision making. This alternative approach allows oscillatory activity to have advantages for decision processes. Having tested my basal ganglia model on individual decisions, I extend the model to incorporate parameters related to my modified reinforcement learning model. I propose a mechanism by which the trial to trial variability observed in human responses could be implemented neurally

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task