Dataset Information

A Mathematical Theory of Cortex-Receptor Artificial Extension.

ABSTRACT: Many physiology experiments demonstrate that an organism's cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensory pathways to be added directly to the computational model itself in an online manner. A synapse expandable artificial neuron model that can grow new synapses, forming a bridge between the novel perceptual information and the existing neural network is introduced to absorb the novel sensory pathway. The experimental results show that the computational model can effectively integrate sudden emerged sensory channels and the neural circuits in the computational model can be reused for novel modalities without influencing the original modality.

SUBMITTER: Xing YL

PROVIDER: S-EPMC6972759 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Mathematical Theory of Cortex-Receptor Artificial Extension.

Xing You-Lu YL

Scientific reports 20200121 1

Many physiology experiments demonstrate that an organism's cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensory pathways to be added directly to the computational model itself in an online manner. A synapse expandable artificial neuron model that can grow new synapses, forming a bridge between the novel perce ...[more]

PMID: 31964907

Similar Datasets

Project description:The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Dataset Information

A Mathematical Theory of Cortex-Receptor Artificial Extension.

Publications

A Mathematical Theory of Cortex-Receptor Artificial Extension.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets