Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

Evolutionary robotics and neuroscience

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A bottom-up approach to emulating emotions using neuromodulation in agents

A bottom-up approach to emulating emotions is expounded in this thesis. This is intended to be useful in research where a phenomenon is to be emulated but the nature of it can not easily be defined. This approach not only advocates emulating the underlying mechanisms that are proposed to give rise to emotion in natural agents, but also advocates applying an open-mind as to what the phenomenon actually is. There is evidence to suggest that neuromodulation is inherently responsible for giving rise to emotions in natural agents and that emotions consequently modulate the behaviour of the agent. The functionality provided by neuromodulation, when applied to agents with self-organising biologically plausible neural networks, is isolated and studied. In research efforts such as this the definition should emerge from the evidence rather than postulate that the definition, derived from limited information, is correct and should be implemented. An implementation of a working definition only tells us that the definition can be implemented. It does not tell us whether that working definition is itself correct and matches the phenomenon in the real world. If this model of emotions was assumed to be true and implemented in an agent, there would be a danger of precluding implementations that could offer alternative theories as to the relevance of neuromodulation to emotions. By isolating and studying different mechanisms such as neuromodulation that are thought to give rise to emotions, theories can arise as to what emotions are and the functionality that they provide. The application of this approach concludes with a theory as to how some emotions can operate via the use of neuromodulators. The theory is explained using the concepts of dynamical systems, free-energy and entropy.EPSRC Stirling University, Computing Science departmental gran

Neuromodulation Based Control of Autonomous Robots on a Cloud Computing Platform

In recent years, the advancement of neurobiologically plausible models and computer networking has resulted in new ways of implementing control systems on robotic platforms. The work presents a control approach based on vertebrate neuromodulation and its implementation on autonomous robots in the open-source, open-access environment of robot operating system (ROS). A spiking neural network (SNN) is used to model the neuromodulatory function for generating context based behavioral responses of the robots to sensory input signals. The neural network incorporates three types of neurons- cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for rewards- and curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behaviors. This model depicts neuron activity that is biologically realistic but computationally efficient to allow for large-scale simulation of thousands of neurons. The model is implemented using graphics processing units (GPUs) for parallel computing in real-time using the ROS environment. The model is implemented to study the risk-taking, risk-aversive, and distracted behaviors of the neuromodulated robots in single- and multi-robot configurations. The entire process is implemented in a cloud computing environment using ROS where the robots communicate wirelessly with the computing nodes through the on-board laptops. However, unlike the traditional neural networks, the neuromodulatory models do not need any pre-training. Instead, the robots learn from the sensory inputs and follow the behavioral facets of living organisms. The details of algorithm development, the experimental setup and implementation results under different conditions, in both single- and multi-robot configurations, are presented along with a discussion on the scope of further work

Applying Spiking Neural Network Simulation to Neuromodulatory Autonomous Robot Control

In this paper, simulation of the brain based on an artificial spiking neuron model is used to create a self-learning algorithm. The spiking neuron simulation is used to demonstrate a neuromodulation program in which the reward seeking properties of dopamine, the risk-adverse effects of serotonin, and the attention-focusing effects of the cholinergic and noradrenergic systems are applied to a mobile robotic platform as it moves autonomously throughout an environment. External stimuli is recorded by the program as spiking “events” that result in corresponding amounts of dopamine and serotonin influenced spiking patterns. These spiking patterns affect how the robot adapts to its surroundings depending on what type of “mood” is set in the internal programming. Also, alternate hardware platforms are analyzed to see how the neural model can be expanded to possibly include cloud computing as a method of control

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Exploring Neuromodulatory Systems for Dynamic Learning

In a continual learning system, the network has to dynamically learn new tasks from few samples throughout its lifetime. It is observed that neuromodulation acts as a key factor in continual and dynamic learning in the central nervous system. In this work, the neuromodulatory plasticity is embedded with dynamic learning architectures. The network has an inbuilt modulatory unit that regulates learning depending on the context and the internal state of the system, thus rendering the networks with the ability to self modify their weights. In one of the proposed architectures, ModNet, a modulatory layer is introduced in a random projection framework. This layer modulates the weights of the output layer neurons in tandem with hebbian learning. Moreover, to explore modulatory mechanisms in conjunction with backpropagation in deeper networks, a modulatory trace learning rule is introduced. The proposed learning rule, uses a time dependent trace to automatically modify the synaptic connections as a function of ongoing states and activations. The trace itself is updated via simple plasticity rules thus reducing the demand on resources. A digital architecture is proposed for ModNet, with on-device learning and resource sharing, to facilitate the efficacy of dynamic learning on the edge. The proposed modulatory learning architecture and learning rules demonstrate the ability to learn from few samples, train quickly, and perform one shot image classification in a computationally efficient manner. The ModNet architecture achieves an accuracy of ∼91% for image classification on the MNIST dataset while training for just 2 epochs. The deeper network with modulatory trace achieves an average accuracy of 98.8%±1.16 on the omniglot dataset for five-way one-shot image classification task. In general, incorporating neuromodulation in deep neural networks shows promise for energy and resource efficient lifelong learning systems

The morphofunctional approach to emotion modelling in robotics

In this conceptual paper, we discuss two areas of research in robotics, robotic models of emotion and morphofunctional machines, and we explore the scope for potential cross-fertilization between them. We shift the focus in robot models of emotion from information-theoretic aspects of appraisal to the interactive significance of bodily dispositions. Typical emotional phenomena such as arousal and action readiness can be interpreted as morphofunctional processes, and their functionality may be replicated in robotic systems with morphologies that can be modulated for real-time adaptation. We investigate the control requirements for such systems, and present a possible bio-inspired architecture, based on the division of control between neural and endocrine systems in humans and animals. We suggest that emotional epi- sodes can be understood as emergent from the coordination of action control and action-readiness, respectively. This stress on morphology complements existing research on the information-theoretic aspects of emotion

Flexible couplings: diffusing neuromodulators and adaptive robotics

Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions¿here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of neurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one ¿chemical¿ and one ¿electrical.