Learning procedure in a neural control model for the urinary bladder

Bastiaanssen, Erica H. C.; Vanderschoot, J.; Leeuwen, J.L. van

doi:10.1109/iembs.1992.5761900

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…What started out as a single switch controller, 40 has evolved to better represent (at least qualitatively) the neuroanatomical organization of LUT controllers in the central nervous system (Figure 4). 13 The de Groat model used a different approach (similar to Bastiaanssen et al 42 ), where more connections were used and specific details about individual neurons were incorporated into their design (e.g., in vivo data were used to tune the weights of interactions between specific neurons) 41 . The advantage to the de Groat model compared to the other control models, 13,18 lies in the modularity of the model's neurons; however the model is more focused on conceptually demonstrating that a switching circuit could be constructed from known neuron types rather than elaborating a physiologically accurate connectome.…”

Section: Neural Controlmentioning

confidence: 99%

Mathematical modeling of the lower urinary tract: A review

Jaskowak

Núñez

Ramachandran

et al. 2022

Neurourology and Urodynamics

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Aims Understand what progress has been made toward a functionally predictive lower urinary tract (LUT) model, identify knowledge gaps, and develop from them a path forward. Methods We surveyed prominent mathematical models of the basic LUT components (bladder, urethra, and their neural control) and categorized the common modeling strategies and theoretical assumptions associated with each component. Given that LUT function emerges from the interaction of these components, we emphasized attempts to model their connections, and highlighted unmodeled aspects of LUT function. Results There is currently no satisfactory model of the LUT in its entirety that can predict its function in response to disease, treatment, or other perturbations. In particular, there is a lack of physiologically based mathematical descriptions of the neural control of the LUT. Conclusions Based on our survey of the work to date, a potential path to a predictive LUT model is a modular effort in which models are initially built of individual tissue‐level components using methods that are extensible and interoperable, allowing them to be connected and tested in a common framework. A modular approach will allow the larger goal of a comprehensive LUT model to be in sight while keeping individual efforts manageable, ensure new models can straightforwardly build on prior research, respect potential interactions between components, and incentivize efforts to model absent components. Using a modular framework and developing models based on physiological principles, to create a functionally predictive model is a challenge that the field is ready to undertake.

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Section: Neural Controlmentioning

confidence: 99%

Mathematical modeling of the lower urinary tract: A review

Jaskowak

Núñez

Ramachandran

et al. 2022

Neurourology and Urodynamics

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“…Depending on the purpose of the model, neural circuitry can be simplified to focus on specific components or groups of components. For example, a model of the electrical behavior of a single external urethral sphincter motoneurone has been developed, based on morphological parameters such as soma size, dendritic diameter and configuration, and several electrical parameters 35. The model simulates the electrical responses of motoneurones supplying the external urethral sphincter motoneurone after injection of electric current.…”

Section: Introductionmentioning

confidence: 99%

“…para: parasympathetic motoneurons; symp: sympathetic motoneurons; inter: intermediolateral cell group of the spinal cord. Adapted from Ref 35…”

Section: Introductionmentioning

confidence: 99%

“…Some models take into consideration involvement of the cortex, such as one consisting of five ‘groups’ of paired neurones (Fig. 2B) to represent parasympathetic and sympathetic neurons, the spinal cord, brainstem, (M‐region) and the cortex 35. This model studied detrusor muscle relaxation or contraction as the sole outcome of simulations, ignoring any actions on urethral function, and demonstrated the desired behavior of filling and voiding.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the urinary tract—computational, physical, and biological methods

Fry

Sadananda

Wood

et al. 2011

Neurourology and Urodynamics

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Models of the lower urinary tract are used to understand better the physiological and pathological functions of the tract and to gain insight into the relative importance of different components. The key requirement of a model is described, namely: to involve a continuous iteration with experiment; whereby experiments provide parameters and validation for components of the model, which is then used to generate hypotheses, which are tested experimentally. Different types of models are described: computational models that describe mathematically the whole urinary tract or components; physical models useful especially in testing medical devices; and tissue-engineered models. The purpose of modeling is first described in terms of the ability of models to predict the properties of the system of interest, using components that have a physiological interpretation, and to gain insight into the relative importance of different components. Examples are used to illustrate the use of modeling the urinary tract with reference to the different categories listed above.

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Nested subsystems in a control model for the urinary bladder

Bastiaanssen

Leeuwen

Vanderschoot

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan)

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Learning procedure in a neural control model for the urinary bladder

Cited by 3 publications

References 3 publications

Mathematical modeling of the lower urinary tract: A review

Mathematical modeling of the lower urinary tract: A review

Modeling the urinary tract—computational, physical, and biological methods

Nested subsystems in a control model for the urinary bladder

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