Neural Attractors and Phonological Grammar

This volume collects three articles which constitute the bulk of my PhD research. The overarching theme of the volume is the role of attractors - a concept from dynamical systems theory – in the neural realization of phonological grammar. The motivation for this line of inquiry begins with the claim that the study of language should provide some insight into the workings of the human mind/brain. Indeed this is one of few mantras shared by linguists of the seemingly irreconcilable “Generative” and “Cognitive” schools (e.g. Chomsky 2002; Lakoff 1988). Given this apparent consensus then, it is perhaps surprising that no breakthrough in our understanding of the brain can yet be attributed to some insight from the study of language. An analysis and critique of this state of affairs is given by Poeppel & Embick (2005), who identify (amongst other things) that we currently have no way of relating the ontologies of linguistics and neuroscience. This Ontological Incommensurability Problem (OIP) can be resolved, they argue, by the use of a Linking Hypothesis, which spells out linguistic computations at the relevant level of algorithmic abstraction, such that the neuroscientist need only find the exact implementations of those algorithms in the brain. If such a hypothesis were sufficiently complete then it could, in principle, predict the kinds of neural configurations required for natural language processing, using linguistic theories as their starting point. In this way, we could finally realize the long sought-after goal of cashing in theories of language for understanding of the human brain. Simultaneously, a Linking Hypothesis also has the potential to unearth lower-level explanations for linguistic phenomena, for example where those explanations might depend on purely neurobiological notions (e.g. neuronal morphology, synaptic density, metabolic efficiency, etc.)

Dlaczego reprezentacje nie trzymają się modeli dynamicznych?

Why representations don't stick with dynamic models?In this paper I investigate thesis, embraced by proponents of dynamicism in cognitive science, that mind is not representational and explanation of cognition can go without representations. This claim has received serious criticism from cognitive scientists and philosophers of mind, who accuse dynamical explanation of being satisfying only for a narrow class of simple cognitive phenomena. Thus, genuine, representation-free explanation of cognition will always be incomplete. I espouse another strategy and present two arguments saying that the language of pure dynamical systems theory is not rich enough to define any nontrivial notion of representation. If I am right, then at least these phenomena dynamical explanation deals well with are not representational and representation talk can in no way help us understand the

The Dynamical Renaissance in Neuroscience

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin-Huxley and FitzHugh-Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience

The Dynamical Renaissance in Neuroscience

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin-Huxley and FitzHugh-Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience

Cybernetic automata: An approach for the realization of economical cognition for multi-robot systems

The multi-agent robotics paradigm has attracted much attention due to the variety of pertinent applications that are well-served by the use of a multiplicity of agents (including space robotics, search and rescue, and mobile sensor networks). The use of this paradigm for most applications, however, demands economical, lightweight agent designs for reasons of longer operational life, lower economic cost, faster and easily-verified designs, etc. An important contributing factor to an agent’s cost is its control architecture. Due to the emergence of novel implementation technologies carrying the promise of economical implementation, we consider the development of a technology-independent specification for computational machinery. To that end, the use of cybernetics toolsets (control and dynamical systems theory) is appropriate, enabling a principled specifi- cation of robotic control architectures in mathematical terms that could be mapped directly to diverse implementation substrates. This dissertation, hence, addresses the problem of developing a technologyindependent specification for lightweight control architectures to enable robotic agents to serve in a multi-agent scheme. We present the principled design of static and dynamical regulators that elicit useful behaviors, and integrate these within an overall architecture for both single and multi-agent control. Since the use of control theory can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior. The proposed scheme is highly decentralized, requiring only local sensing and no inter-agent communication. Beyond several simulation-based studies, we provide experimental results for a two-agent system, based on a custom implementation employing field-programmable gate arrays