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"Models, Neurological."
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Integrated models of cognitive systems
The field of cognitive modeling has progressed beyond modeling cognition in the context of simple laboratory tasks and begun to attack the problem of modeling cognition in more complex, realistic environments, such as those studied by researchers in the field of human factors. The problems that the human-factors community is tackling focus on modeling certain problems of communication and control that arise in the integration of implicit and explicit knowledge, emotion, and cognition, and the cognitive system with the external environment. These problems must be addressed in order to produce integrated cognitive models of moderately complex tasks. Architectures of cognition in these tasks focus on the control of a central system, which includes control of the central processor itself, initiation of functional processes, such as visual search and memory retrieval, and harvesting the results of functional processes. Because the control of the central system is conceptually different from the internal control required by individual functional processes, a complete architecture of cognition must incorporate two types of theories of control: type 1 theories of the structure, functionality, and operation of the controller, and type 2 theories of the internal control of functional processes, how, and what they communicate to the controller. This volume presents, for both types of theories, the current state of the art, as well as contrasts among current approaches to human-performance models.
eBook
Peripheral nervous system plasmalogens regulate Schwann cell differentiation and myelination
Rhizomelic chondrodysplasia punctata (RCDP) is a developmental disorder characterized by hypotonia, cataracts, abnormal ossification, impaired motor development, and intellectual disability. The underlying etiology of RCDP is a deficiency in the biosynthesis of ether phospholipids, of which plasmalogens are the most abundant form in nervous tissue and myelin; however, the role of plasmalogens in the peripheral nervous system is poorly defined. Here, we used mouse models of RCDP and analyzed the consequence of plasmalogen deficiency in peripheral nerves. We determined that plasmalogens are crucial for Schwann cell development and differentiation and that plasmalogen defects impaired radial sorting, myelination, and myelin structure. Plasmalogen insufficiency resulted in defective protein kinase B (AKT) phosphorylation and subsequent signaling, causing overt activation of glycogen synthase kinase 3β (GSK3β) in nerves of mutant mice. Treatment with GSK3β inhibitors, lithium, or 4-benzyl-2-methyl-1,2,4-thiadiazolidine-3,5-dione (TDZD-8) restored Schwann cell defects, effectively bypassing plasmalogen deficiency. Our results demonstrate the requirement of plasmalogens for the correct and timely differentiation of Schwann cells and for the process of myelination. In addition, these studies identify a mechanism by which the lack of a membrane phospholipid causes neuropathology, implicating plasmalogens as regulators of membrane and cell signaling.
Journal Article
Brain Computation as Hierarchical Abstraction
The vast differences between the brain's neural circuitry and a computer's silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its computation is factored into different levels of abstraction.Drawing on several decades of progress in computational neuroscience, together with recent results in Bayesian and reinforcement learning methodologies, Ballard factors the brain's principal computational issues in terms of their natural place in an overall hierarchy. Each of these factors leads to a fresh perspective. A neural level focuses on the basic forebrain functions and shows how processing demands dictate the extensive use of timing-based circuitry and an overall organization of tabular memories. An embodiment level organization works in reverse, making extensive use of multiplexing and on-demand processing to achieve fast parallel computation. An awareness level focuses on the brain's representations of emotion, attention and consciousness, showing that they can operate with great economy in the context of the neural and embodiment substrates.
eBook
Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms
This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions concerning both the presence and the quality of experience, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate formulation of the axioms as postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system’s irreducible cause–effect power, the distinctions and relations specified by a substrate can account for the quality of experience.
Journal Article
On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro
Synchronous spiking during cerebellar tasks has been observed across Purkinje cells: however, little is known about the intrinsic cellular mechanisms responsible for its initiation, cessation and stability. The Phase Response Curve (PRC), a simple input-output characterization of single cells, can provide insights into individual and collective properties of neurons and networks, by quantifying the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent action potentials, while a neuron is firing tonically. Recently, the PRC theory applied to cerebellar Purkinje cells revealed that these behave as phase-independent integrators at low firing rates, and switch to a phase-dependent mode at high rates. Given the implications for computation and information processing in the cerebellum and the possible role of synchrony in the communication with its post-synaptic targets, we further explored the firing rate dependency of the PRC in Purkinje cells. We isolated key factors for the experimental estimation of the PRC and developed a closed-loop approach to reliably compute the PRC across diverse firing rates in the same cell. Our results show unambiguously that the PRC of individual Purkinje cells is firing rate dependent and that it smoothly transitions from phase independent integrator to a phase dependent mode. Using computational models we show that neither channel noise nor a realistic cell morphology are responsible for the rate dependent shift in the phase response curve.
Journal Article