BRAHMS: Novel middleware for integrated systems computation

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time) - challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS. that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system: its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause and continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools. (C) 2009 Elsevier Ltd. All rights reserved

Cortical control of forelimb movement

Cortical control of movement is mediated by wide-spread projections impacting many nervous system regions in a top-down manner. Although much knowledge about cortical circuitry has been accumulated from local cortical microcircuits, cortico-cortical and cortico-subcortical networks, how cortex communicates to regions closer to motor execution, including the brainstem, is less well understood. In this dissertation, we investigate the organization of cortico-medulla projections and their roles in controlling forelimb movement. We focus on anatomical and functional relationships between cortex and lateral rostral medulla (LatRM), a region in caudal brainstem which is shown to be key in the control of forelimb movement. Our findings reveal the precise anatomical and functional organization between different cortical regions and matched postsynaptic neurons in the caudal brainstem, tuned to different phases of one carefully orchestrated behavior, which advance the our knowledge on circuit mechanisms involved in the control of body movements, and unravel the logic of how the top-level control region in the mammalian nervous system – the cortex – intersects with a high degree of specificity with command centers in the brainstem and beyond

Exploring the psychobiology of emotions and motivations through computational models

This thesis investigates emotions and motivations on the basis of an operational approach. This approach has both computational and psychobiological roots. Three main directions of research are followed: (1) investigation on the neural substrates of emotional systems though the exploration of the literature about comparative functional anatomy and physiology; (2) definition the relationship between emotion, cognition and behaviour through the exploration of the psychobiological literature about animal models; (3) building of computational models constrained by the sources of information 1 and 2; (4) testing the behaviour of such models within simulated robots acting in simulated environments. The main focus will be on the interaction between the emotional and motivational systems and high level cognitive processes behind adaptive behaviour. The whole study will be informed by the current psychobiological knowledge about the functioning of the neural systems pivoting on amygdala, given that this is considered to be one of the major nodes of interaction between the processing of internal values and the processing about the past, current and future world outside the organism in superior vertebrates

An investigation into the neural substrates of virtue to determine the key place of virtues in human moral development

Virtues, as described by Aristotle and Aquinas, are understood as dispositions of character to behave in habitual, specific, positive ways; virtue is a critical requirement for human flourishing. From the perspective of Aristotelian-Thomistic anthropology which offers an integrated vision of the material and the rational in the human person, I seek to identify the neural bases for the development and exercise of moral virtue. First I review current neuroscientific knowledge of the capacity of the brain to structure according to experience, to facilitate behaviours, to regulate emotional responses and support goal election. Then, having identified characteristics of moral virtue in the light of the distinctions between cardinal virtues, I propose neural substrates by mapping neuroscientific knowledge to these characteristics. I then investigate the relationship between virtue, including its neurobiological features, and human flourishing. This process allows a contemporary and evidence-based corroboration for a model of moral development based on growth in virtue as understood by Aristotle and Aquinas, and a demonstration of a biological aptitude and predisposition for the development of virtue. Conclusions are drawn with respect to science, ethics, and parenting

Spatial navigation in geometric mazes:a computational model of rodent behavior

Navigation is defined as the capability of planning and performing a path from the current position towards a desired location. Different types, or strategies, of navigation are used by animals depending on the task they are trying to solve. Visible goals can be approached directly, while navigation to a hidden goal usually requires a memorized representation of relative positions of the goal and surrounding landmarks. Neurophysiological and behavioral experiments on rodents suggest that different brain areas are responsible for the expression of different navigation strategies. Specifically, dorsal striatum has been related to storage and recall of stimulus-response associations underlying simple goal-approaching behaviors, whereas hippocampus is thought to store the spatial representation of the environment. Such a representation is built during an unrewarded spatial exploration and appears to be employed in cases when simple stimulus-response strategies fail. Discovery of neurons with spatially correlated activity, i.e. place cells and grid cells, in the hippocampal formation complements behavioral and lesion data suggesting its role for spatial orientation. The overall objective of this work is to study the neurophysiological mechanisms underlying rodent spatial behavior, in particular those that are responsible for the implementation of different navigational strategies. Special attention is devoted to the question of how various types of sensory cues influence goal-oriented behavior. The model of a navigating rat described in this work is based on functional and anatomical properties of brain regions involved in encoding and storage of space representation and action generation. In particular, place and grid cells are modeled by two interconnected populations of artificial neurons. Together, they form a network for spatial learning, capable of combining different types of sensory inputs to produce a distributed representation of location. Goal-directed actions can be generated in the model via two different neural pathways: the first one drives stimulus-response behavior and associates visual input directly to motor responses; the second one associates motor actions with places and hence depends on the representation of location. The visual input is represented by responses of a large number of orientation-sensitive filters to visual images generated according to the position and orientation of the simulated rat in a virtual three-dimensional world. The model was tested in a large array of tasks designed by analogy to experimental studies on animal behavior. Results of several experimental studies, behavioral as wells as neurophysiological, were reproduced. Based on these results we formulated a hypothesis about the influence that the rat's perception of surrounding environment exerts on goal-oriented behavior. This hypothesis may provide an insight into several issues in animal behavior research that were not addressed by theoretical models until now

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience

Spatial learning and navigation in the rat:a biomimetic model

Animals behave in different ways depending on the specific task they are required to solve. In certain cases, if a cue marks the goal location, they can rely on simple stimulusresponse associations. In contrast, other tasks require the animal to be endowed with a representation of space. Such a representation (i.e. cognitive map) allows the animal to locate itself within a known environment and perform complex target-directed behaviour. In order to efficiently perform, the animal not only should be able to exhibit these types of behaviour, but it should be able to select which behaviour is the most appropriate at any given task conditions. Neurophysiological and behavioural experiments provide important information on how such processes may take place in the rodent's brain. Specifically, place- and orientation sensitive cells in the rat Hippocampus have been interpreted as a neural substrate for spatial abilities related to the theory of the cognitive map proposed in the late 1940s by Tolman. Moreover, recent dissociation experiments using selectively located lesions, as well as pharmacological studies have shown that different brain regions may be involved in different types of behaviour. Accordingly, one memory system involving the hippocampus and the ventral striatum would be responsible for cognitive navigation, while navigation based on stimulus-response associations would be mediated by the dorsolateral striatum. Based on these studies, the aim of this work is to develop a neural network model of the spatial abilities of the rat. The model, based on functional properties and anatomical inter-connections of the brain areas involved in spatial learning should be able to establish a distributed representation of space composed of place-sensitive units. Such a representation takes into account both internal and external sensory information, and the model reproduces physiological properties of place cells such as changes in their directional dependence. Moreover, the spatial representation may be used to perform cognitive navigation. Modelled place cells drive an extra-hippocampal population of action-coding cells, allowing the establishment of place-response associations. These associations encoded in synaptic connections between place- and action-cells are modified by means of reinforcement learning. In a similar way, simple sensory input can be used to establish stimulus-response associations. These associations are encoded in a different set of action cells which corresponds to a different neural substrate encoding for non-cognitive navigation strategies (i.e. taxon or praxic). Both cognitive and non-cognitive navigation strategies compete for action control to determine the actual behaviour of the agent. Tests of the performance of the model show that it is able to establish a representation of space, and modelled place cells reproduce some physiological properties of their biological counterparts. Furthermore, the model reproduces goal-based behaviour based on both cognitive and non-cognitive strategies as well as behaviour in conflicting situations reported in experimental studies in animals