Donald S. Borrett

Evolutionary autonomous agents and the nature of apraxia

BackgroundEvolutionary autonomous agents are robots or robot simulations whose controller is a dynamical neural network and whose evolution occurs autonomously under the guidance of a fitness function without the detailed or explicit direction of an external programmer. They are embodied agents with a simple neural network controller and as such they provide the optimal forum by which sensorimotor interactions in a specified environment can be studied without the computational assumptions inherent in standard neuroscience.MethodsEvolutionary autonomous agents were evolved that were able to perform identical movements under two different contexts, one which represented an automatic movement and one which had a symbolic context. In an attempt to model the automatic-voluntary dissociation frequently seen in ideomotor apraxia, lesions were introduced into the neural network controllers resulting in a behavioral dissociation with loss of the ability to perform the movement which had a symbolic context and preservation of the simpler, automatic movement.ResultsAnalysis of the changes in the hierarchical organization of the networks in the apractic EAAs demonstrated consistent changes in the network dynamics across all agents with loss of longer duration time scales in the network dynamics.ConclusionThe concepts of determinate motor programs and perceptual representations that are implicit in the present day understanding of ideomotor apraxia are assumptions inherent in the computational understanding of brain function. The strength of the present study using EAAs to model one aspect of ideomotor apraxia is the absence of these assumptions and a grounding of all sensorimotor interactions in an embodied, autonomous agent. The consistency of the hierarchical changes in the network dynamics across all apractic agents demonstrates that this technique is tenable and will be a valuable adjunct to a computational formalism in the understanding of the physical basis of neurological disorders.

Neural Networks and Parkinson's Disease

A closed-loop or recurrent neural network was taught to generate output discharges to reproduce the prototypical activations in agonist and antagonist muscles which produce the displacement of a limb about a single joint. By introducing a generalized decrease in the excitability of the pre-output layer in the network, the network made the displacement more slowly and also showed an inability to maintain a repetitive movement. These concepts can be applied to the human nervous system in the understanding of the physical basis of movement and its disorders. It is suggested that a movement represents the output of a closed-loop network, such as the cortical-basal ganglia-thalamic-cortical motor loop, which iterates repetitively to its end point or attractor. The model provides an explanation of how the state of thalamic inhibition seen in Parkinsons disease physically may produce bradykinesia and the inability to maintain a repetitive movement.

Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents

The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.

Near-Infrared Spectroscopy Study of Language Activated Hyper- and Hypo-Oxygenation in Human Prefrontal Cortex

Oxygenation changes in the left prefrontal cortex during language processing were assessed with near-infrared spectroscopy (NIRS). Oxyhemoglobin and deoxyhemoglobin concentrations at the Fp1 site during 5 min of resting with eyes closed (control), followed by 5 min of reading aloud, were monitored. A statistically significant change in the oxyhemoglobin concentration was observed by NIRS in all the subjects after execution of the experimental task. The observations of hyper-oxygenation as well as hypo-oxygenation in the present investigation extend past studies and suggest a complex phenomenon of activation that may be the result of a vascular steal mechanism, attenuated activation baselines, or active cortical deactivation.

Phenomenology, dynamical neural networks and brain function

Current cognitive science models of perception and action assume that the objects that we move toward and perceive are represented as determinate in our experience of them. A proper phenomenology of perception and action, however, shows that we experience objects indeterminately when we are perceiving them or moving toward them. This indeterminacy, as it relates to simple movement and perception, is captured in the proposed phenomenologically based recurrent network models of brain function. These models provide a possible foundation from which predicative structures may arise as an emergent phenomenon without the positing of a representing subject. These models go some way in addressing the dual constraints of phenomenological accuracy and neurophysiological plausibility that ought to guide all projects devoted to discovering the physical basis of human experience.

Heidegger, Gestell and rehabilitation of the biomedical model.

The biomedical model is the foundation upon which current evaluations in clinical practice are based. In the quest for objective evidence to support clinical interventions, the patient is reduced to a number of technologically generated variables that serve as a surrogate for the patient herself. The biomedical model, as a reflection of Gestell or the essence of technology, carries with it the danger that it may overwhelm the practitioners perspective so that other perspectives that may include the lived experience of the patient are actively suppressed. An ontology of the patient based on a Heideggerian exegesis is developed as a response to this concern. Morris has suggested that the most fundamental disturbance in the lived experience of the patient is an alteration in the patients relationship to ecstatic temporality. In ecstatic temporality, the past, present and future are experienced as a unity in which the patient sees herself as her own possibility. Access to this experience is disturbed in illness; the future is no longer experienced as the patients own possibility but rather as a series of predetermined external events that dictate the patients affairs. By developing a biologically plausible model of ecstatic temporality, the lived experience of the patient does not have to be considered separate from the physical mechanisms involved in the disease state. As a consequence, the biomedical model cannot suppress the practitioners humane perspective since the latter is explicitly brought under its purview.The biomedical model is the foundation upon which current evaluations in clinical practice are based. In the quest for objective evidence to support clinical interventions, the patient is reduced to a number of technologically generated variables that serve as a surrogate for the patient herself. The biomedical model, as a reflection of Gestell or the essence of technology, carries with it the danger that it may overwhelm the practitioners perspective so that other perspectives that may include the lived experience of the patient are actively suppressed. An ontology of the patient based on a Heideggerian exegesis is developed as a response to this concern. Morris has suggested that the most fundamental disturbance in the lived experience of the patient is an alteration in the patients relationship to ecstatic temporality. In ecstatic temporality, the past, present and future are experienced as a unity in which the patient sees herself as her own possibility. Access to this experience is disturbed in illness; the future is no longer experienced as the patients own possibility but rather as a series of predetermined external events that dictate the patients affairs. By developing a biologically plausible model of ecstatic temporality, the lived experience of the patient does not have to be considered separate from the physical mechanisms involved in the disease state. As a consequence, the biomedical model cannot suppress the practitioners humane perspective since the latter is explicitly brought under its purview.

Human prefrontal cortical response to the meditative state: a spectroscopy study.

ABSTRACT The effect of Qigong meditation on the hemodynamics of the prefrontal cortex was investigated by spectroscopy with a single-wavelength probe (650 nm) and confirmed by standard near-infrared spectroscopy with a dual-wavelength probe. Deoxyhemoglobin changes were recorded with the single-wavelength probe over the left prefrontal cortex during meditation by Qigong practitioners, and non-practitioners instructed in the technique. Practitioners showed a significant decrease in deoxyhemoglobin levels suggesting an increase in prefrontal activation during meditation. The results were confirmed in a second set of experiments with the standard dual-wavelength probe, in which significant differences in the decrease in deoxyhemoglobin and increase in oxyhemoglobin concentrations were observed in practitioners as compared with non-practitioners. The study thus provides evidence that Qigong meditation has a significant effect on prefrontal activation.

Bridging embodied cognition and brain function: The role of phenomenology

Both cognitive science and phenomenology accept the primacy of the organism-environment system and recognize that cognition should be understood in terms of an embodied agent situated in its environment. How embodiment is seen to shape our world, however, is fundamentally different in these two disciplines. Embodiment, as understood in cognitive science, reduces to a discussion of the consequences of having a body like ours interacting with our environment and the relationship is one of contingent causality. Embodiment, as understood phenomenologically, represents the condition of intelligibility of certain terms in our experience and, as such, refers to one aspect of that background which presupposes our understanding of the world. The goals and approach to modeling an embodied agent in its environment are also fundamentally different dependent on which relationship is addressed. These differences are highlighted and are used to support our phenomenologically based approach to organism-environment interaction and its relationship to brain function.

HEGELIAN PHENOMENOLOGY AND ROBOTICS

A formalism is developed that treats a robot as a subject that can interpret its own experience rather than an object that is interpreted within our experience. A regulative definition of a meaningful experience in robots is proposed in which the present sensible experience is considered meaningful to the agent, as the subject of the experience, if it can be related to the agents temporal horizons. This definition is validated by demonstrating that such an experience in evolutionary autonomous agents is embodied, contextual and normative, as is required for the maintenance of phenomenological accuracy. With this formalism it is shown how a dialectic similar to that described in Hegelian phenomenology can emerge in the robotic experience and why the presence of such a dialectic can serve as a constraint in the further development of cognitive agents.

Chaos game representation of human pallidal spike trains.

Many studies have demonstrated the presence of scale invariance and long-range correlation in animal and human neuronal spike trains. The methodologies to extract the fractal or scale-invariant properties, however, do not address the issue as to the existence within the train of fine temporal structures embedded in the global fractal organisation. The present study addresses this question in human spike trains by the chaos game representation (CGR) approach, a graphical analysis with which specific temporal sequences reveal themselves as geometric structures in the graphical representation. The neuronal spike train data were obtained from patients whilst undergoing pallidotomy. Using this approach, we observed highly structured regions in the representation, indicating the presence of specific preferred sequences of interspike intervals within the train. Furthermore, we observed that for a given spike train, the higher the magnitude of its scaling exponent, the more pronounced the geometric patterns in the representation and, hence, higher probability of occurrence of specific subsequences. Given its ability to detect and specify in detail the preferred sequences of interspike intervals, we believe that CGR is a useful adjunct to the existing set of methodologies for spike train analysis.