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Your search keyword '"neural attractors"' showing total 4 results
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4 results on '"neural attractors"'

1. A dialectical approach for transcending structuralism

Author

Taxén, Lars and Taxén, Lars

Abstract

The purpose of this contribution is to explore how a dialectical approach may further Integrational Linguistics into transcending structuralism. The strategy employed is to articulate the biomechanical and macrosocial factors in the communicational infrastructure as neurobiological attractors and social anchors respectively. Hence, the inquiry focuses on the dialectical relation between an individual’s neurobiological structure and her ability to confer signhood onto activity-relevant phenomena in the social environment. This makes it possible to conceive new directions for Integrational Linguistics by rethinking central aspects such as contextuality, indeterminacy, cotemporality, integration, and coordination. Conjectural as these directions may be, I put forward that the dialectical approach provides a promising launchpad for further inquiries into transcending structuralism and beyond.

Published
2024

2. Velocity coupling of grid cell modules enables stable embedding of a low dimensional variable in a high dimensional neural attractor

Author

Noga Mosheiff and Yoram Burak

Subjects
neural networks, grid cells, entorhinal cortex, neural coding, neural attractors, short term memory, Medicine, Science, Biology (General), QH301-705.5
Abstract

Grid cells in the medial entorhinal cortex (MEC) encode position using a distributed representation across multiple neural populations (modules), each possessing a distinct spatial scale. The modular structure of the representation confers the grid cell neural code with large capacity. Yet, the modularity poses significant challenges for the neural circuitry that maintains the representation, and updates it based on self motion. Small incompatible drifts in different modules, driven by noise, can rapidly lead to large, abrupt shifts in the represented position, resulting in catastrophic readout errors. Here, we propose a theoretical model of coupled modules. The coupling suppresses incompatible drifts, allowing for a stable embedding of a two-dimensional variable (position) in a higher dimensional neural attractor, while preserving the large capacity. We propose that coupling of this type may be implemented by recurrent synaptic connectivity within the MEC with a relatively simple and biologically plausible structure.

Published
2019
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3. Velocity coupling of grid cell modules enables stable embedding of a low dimensional variable in a high dimensional neural attractor

Author

Yoram Burak and Noga Mosheiff

Subjects
Mouse, QH301-705.5, Computer science, Science, Models, Neurological, grid cells, neural coding, Topology, General Biochemistry, Genetics and Molecular Biology, short term memory, Position (vector), Attractor, Biological neural network, Biology (General), neural attractors, Modularity (networks), entorhinal cortex, Quantitative Biology::Neurons and Cognition, General Immunology and Microbiology, Artificial neural network, General Neuroscience, General Medicine, neural networks, Coupling (computer programming), Space Perception, Medicine, Rat, Embedding, Nerve Net, Neural coding, Research Article, Neuroscience
Abstract

Grid cells in the medial entorhinal cortex (MEC) encode position using a distributed representation across multiple neural populations (modules), each possessing a distinct spatial scale. The modular structure of the representation confers the grid cell neural code with large capacity. Yet, the modularity poses significant challenges for the neural circuitry that maintains the representation, and updates it based on self motion. Small incompatible drifts in different modules, driven by noise, can rapidly lead to large, abrupt shifts in the represented position, resulting in catastrophic readout errors. Here, we propose a theoretical model of coupled modules. The coupling suppresses incompatible drifts, allowing for a stable embedding of a two-dimensional variable (position) in a higher dimensional neural attractor, while preserving the large capacity. We propose that coupling of this type may be implemented by recurrent synaptic connectivity within the MEC with a relatively simple and biologically plausible structure.

Published
2019
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4. Velocity coupling of grid cell modules enables stable embedding of a low dimensional variable in a high dimensional neural attractor.

Author

Mosheiff N and Burak Y

Subjects
Grid Cells physiology, Models, Neurological, Nerve Net cytology, Space Perception
Abstract

Grid cells in the medial entorhinal cortex (MEC) encode position using a distributed representation across multiple neural populations (modules), each possessing a distinct spatial scale. The modular structure of the representation confers the grid cell neural code with large capacity. Yet, the modularity poses significant challenges for the neural circuitry that maintains the representation, and updates it based on self motion. Small incompatible drifts in different modules, driven by noise, can rapidly lead to large, abrupt shifts in the represented position, resulting in catastrophic readout errors. Here, we propose a theoretical model of coupled modules. The coupling suppresses incompatible drifts, allowing for a stable embedding of a two-dimensional variable (position) in a higher dimensional neural attractor, while preserving the large capacity. We propose that coupling of this type may be implemented by recurrent synaptic connectivity within the MEC with a relatively simple and biologically plausible structure., Competing Interests: NM, YB No competing interests declared, (© 2019, Mosheiff and Burak.)

Published
2019
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