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Register a new userIllusions - a model of mind
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Authors
M. Maniatis
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Recognizing that all mental processes have to be unfree and passive, we develop a model of behavior and perceptions. We shall see how misleading our intuition is and shall understand how consciousness arises.
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The paper discusses relationships between aesthetics theory and mathematical models of mind. Mathematical theory describes abilities for concepts, emotions, instincts, imagination, adaptation, learning, cognition, language, approximate hierarchy of the mind and evolution of these abilities. The knowledge instinct is the foundation of higher mental abilities and aesthetic emotions. Aesthetic emotions are present in every act of perception and cognition, and at the top of the mind hierarchy they become emotions of the beautiful. The learning ability is essential to everyday perception and cognition as well as to the historical development of understanding of the meaning of life. I discuss a controversy surrounding this issue. Conclusions based on cognitive and mathematical models confirm that judgments of taste are at once subjective and objective, and I discuss what it means. The paper relates cognitive and mathematical concepts to those of philosophy and aesthetics, from Plato to our days, clarifies cognitive mechanisms and functions of the beautiful, and resolves many difficulties of contemporary aesthetics.
The dominant modeling framework for understanding cortical computations are heuristic firing rate models. Despite their success, these models fall short to capture spike synchronization effects, to link to biophysical parameters and to describe finite-size fluctuations. In this opinion article, we propose that the refractory density method (RDM), also known as age-structured population dynamics or quasi-renewal theory, yields a powerful theoretical framework to build rate-based models for mesoscopic neural populations from realistic neuron dynamics at the microscopic level. We review recent advances achieved by the RDM to obtain efficient population density equations for networks of generalized integrate-and-fire (GIF) neurons -- a class of neuron models that has been successfully fitted to various cell types. The theory not only predicts the nonstationary dynamics of large populations of neurons but also permits an extension to finite-size populations and a systematic reduction to low-dimensional rate dynamics. The new types of rate models will allow a re-examination of models of cortical computations under biological constraints.
Mathematical approaches to modeling the mind since the 1950s are reviewed. Difficulties faced by these approaches are related to the fundamental incompleteness of logic discovered by K. Godel. A recent mathematical advancement, dynamic logic (DL) overcame these past difficulties. DL is described conceptually and related to neuroscience, psychology, cognitive science, and philosophy. DL models higher cognitive functions: concepts, emotions, instincts, understanding, imagination, intuition, consciousness. DL is related to the knowledge instinct that drives our understanding of the world and serves as a foundation for higher cognitive functions. Aesthetic emotions and perception of beauty are related to everyday functioning of the mind. The article reviews mechanisms of human symbolic ability, language and cognition, joint evolution of the mind, consciousness, and cultures. It touches on a manifold of aesthetic emotions in music, their cognitive function, origin, and evolution. The article concentrates on elucidating the first principles and reviews aspects of the theory proven in laboratory research.
Understanding how brain functions has been an intriguing topic for years. With the recent progress on collecting massive data and developing advanced technology, people have become interested in addressing the challenge of decoding brain wave data into meaningful mind states, with many machine learning models and algorithms being revisited and developed, especially the ones that handle time series data because of the nature of brain waves. However, many of these time series models, like HMM with hidden state in discrete space or State Space Model with hidden state in continuous space, only work with one source of data and cannot handle different sources of information simultaneously. In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms. We apply this model to decode the mind state of students during lectures based on their brain waves and reach a significant better results compared to traditional methods.
A powerful experimental approach for investigating computation in networks of biological neurons is the use of cultured dissociated cortical cells grown into networks on a multi-electrode array. Such preparations allow investigation of network development, activity, plasticity, responses to stimuli, and the effects of pharmacological agents. They also exhibit whole-culture pathological bursting; understanding the mechanisms that underlie this could allow creation of more useful cell cultures and possibly have medical applications.
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