Developing a mathematical model to predict energy expenditure while bouncing on a trampoline

The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.

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Unveiling the kinematics of the avoidance response in maize (Zen mays) primary roots

Biologia ◽

10.1515/biolog-2016-0022 ◽

2016 ◽

Vol 71 (2) ◽

Cited By ~ 1

Author(s):

Liyana Popova ◽

Alice Tonazzini ◽

Federica Di Michele ◽

Andrea Russino ◽

Ali Sadeghi ◽

...

Keyword(s):

Mathematical Model ◽

Energy Expenditure ◽

Avoidance Response ◽

Characteristic Time ◽

Time Lapse ◽

Root Tip ◽

Heterogeneous Environment ◽

Primary Roots ◽

Physical Barriers ◽

Time Lapse Imaging

AbstractLiving roots grow in soil, which is a heterogeneous environment containing a wide variety of physical barriers. Roots must avoid these barriers to grow: first, they adopt a characteristic S-shape that can be described by the angle between the root tip and the barrier (i.e., the tip-to-barrier angle); then, they move parallel to the barrier by keeping the sensitive tip in contact with the barrier until it has been circumvented. We investigated this avoidance response in the primary roots of maize (We measured the root tip orientation during growth by using time-lapse imaging and specially developed tip-tracking software (9 trials for each value of the barrier orientation). Remarkably, we found that the S-shapes formed by the roots were characterized by the same tip-to-barrier angle regardless of the barrier orientation: namely, 21.96 ± 2.97, 21.48 ± 4.75 and 20.81 ± 9.39 degrees for barriers oriented at 45, 60 and 90 degrees, respectively. We also considered the root growth after bypassing the barrier; for the barrier at 90 degrees, we observed a gravitropic recovery. Furthermore, we used a mathematical model to quantify the characteristic time of S-shape formation (95 min on average) and gravitropic recovery (approximately 42 min); the obtained values are consistent with those of previous studies.Our results suggest that the avoidance response develops with respect to a reference frame associated with the barrier. From a biological viewpoint, the reason the root adopts the specifically observed tip-to-barrier angle is unclear, but we speculate that maize root optimizes energy expenditure during the penetration of a medium.

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Exploration-based learning of a step to step controller predicts locomotor adaptation

10.1101/2021.03.18.435986 ◽

2021 ◽

Author(s):

Nidhi Seethapathi ◽

Barrett Clark ◽

Manoj Srinivasan

Keyword(s):

Mathematical Model ◽

Nervous System ◽

Energy Expenditure ◽

Lower Energy ◽

Movement Patterns ◽

Treadmill Walking ◽

Stabilizing Controller ◽

Locomotor Adaptation ◽

Theoretical Account ◽

Time Periodic

Humans are able to adapt their locomotion to a variety of novel circumstances, for instance, walking on diverse terrain and walking with new footwear. During locomotor adaptation, humans have been shown to exhibit stereotypical changes in their movement patterns. Here, we provide a theoretical account of such locomotor adaptation, positing that the nervous system prioritizes stability in the short timescale and improves energy expenditure over a longer timescale. The resulting mathematical model has two processes: a stabilizing controller which is gradually changed by a reinforcement learner that exploits local gradients to lower energy expenditure, estimating gradients indirectly via intentional exploratory noise. We consider this model walking and adapting under three novel circumstances: walking on a split-belt treadmill (walking with each foot on a different belt, each belt at different speeds), walking with an exoskeleton, and walking with an asymmetric leg mass. This model predicts the short and long timescale changes observed in walking symmetry on the split-belt treadmill and while walking with the asymmetric mass. The model exhibits energy reductions with exoskeletal assistance, as well as entrainment to time-periodic assistance. We show that such exploration-based learning is degraded in the presence of large sensorimotor noise, providing a potential account for some impairments in learning.

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A compendium of G-protein–coupled receptors and cyclic nucleotide regulation of adipose tissue metabolism and energy expenditure

Clinical Science ◽

10.1042/cs20190579 ◽

2020 ◽

Vol 134 (5) ◽

pp. 473-512 ◽

Cited By ~ 5

Author(s):

Ryan P. Ceddia ◽

Sheila Collins

Keyword(s):

Adipose Tissue ◽

Energy Expenditure ◽

Signaling Pathways ◽

G Protein ◽

Uncoupling Protein ◽

Beige Adipocytes ◽

Nucleotide Regulation ◽

Ucp1 Expression ◽

Thermogenic Adipocytes ◽

G Protein Coupled

Abstract With the ever-increasing burden of obesity and Type 2 diabetes, it is generally acknowledged that there remains a need for developing new therapeutics. One potential mechanism to combat obesity is to raise energy expenditure via increasing the amount of uncoupled respiration from the mitochondria-rich brown and beige adipocytes. With the recent appreciation of thermogenic adipocytes in humans, much effort is being made to elucidate the signaling pathways that regulate the browning of adipose tissue. In this review, we focus on the ligand–receptor signaling pathways that influence the cyclic nucleotides, cAMP and cGMP, in adipocytes. We chose to focus on G-protein–coupled receptor (GPCR), guanylyl cyclase and phosphodiesterase regulation of adipocytes because they are the targets of a large proportion of all currently available therapeutics. Furthermore, there is a large overlap in their signaling pathways, as signaling events that raise cAMP or cGMP generally increase adipocyte lipolysis and cause changes that are commonly referred to as browning: increasing mitochondrial biogenesis, uncoupling protein 1 (UCP1) expression and respiration.

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