US Motor Control Centers Market to Reach USD 1500 Million with CAGR of 6.5% By 2035, USA Motor Control Centers Market Analysis By Type, By Voltage, By Component, By End User and By Standard Segment | ...

MIPS has introduced advances in its Actus reference platform, an open, energy-efficient RISC-V architecture designed to combine real-time motor control, edge inference, and communications for robotics. Developed for compact robot joints, Actus addresses the requirements of humanoid and industrial robots, where precise movement must be achieved within strict space, weight, power, and thermal constraints. The Actus reference platform integrates two MIPS Atlas M8500 real-time RISC-V microcontrollers with dedicated motor-control acceleration and edge-processing capabilities. The processors use hardware multithreading to avoid context-switching overhead during interrupt servicing. Dedicated trigonometric acceleration provides more than 16 times the performance of standard trigonometric libraries when executing field-oriented control algorithms.

US Intelligent Motor Controller Market to Reach USD 150 Million with CAGR of 3.75% By 2035, USA Intelligent Motor Controller Market Analysis By Application, By Controller Type, By End Use and By Techn...

Combination Starter Market projected to reach USD 29.33 Billion, at a CAGR of 3.55% during 2026 to 2035, driven by Integration of advanced digital technologies enhances efficiency in the Combination S...

A groundbreaking study published in Communications Biology has uncovered how reinforcement learning shapes both the selection of motor actions and the timing of their execution in the Aplysia feeding circuit. Researchers led by Yuto Momohara, Curtis L. Neveu, and John H. Byrne from the University of Texas Health Science Center conducted experiments using an ex vivo operant conditioning paradigm on the Aplysia feeding circuit. Their findings reveal that reinforcement biases motor program selection toward ingestive behaviors while simultaneously shortening the duration of these actions by advancing the transition from protraction to retraction phases in feeding movements. The study identified specific neural mechanisms underlying this temporal compression, demonstrating that operant conditioning reduces the intrinsic electrical excitability of neuron B52, which weakens B52-mediated inhibition of the retraction-initiating interneuron B64. This adjustment lowers the threshold for triggering B64's plateau potential, effectively speeding up the feeding cycle. Crucially, phase-restricted co-manipulation of B52 and B64 activity successfully replicated the observed protraction shortening, confirming the circuit-level coordination between action selection and temporal structure. These results establish a fundamental framework for understanding how reinforcement learning optimizes behavioral efficiency through coordinated plasticity within identified neural circuits. The research, supported by National Institutes of Health grant R01 NS101356, provides critical insights into the neural basis of learning and motor control, with potential implications for understanding similar processes in more complex organisms. The findings contribute significantly to neuroscience by bridging the gap between behavioral outcomes and specific circuit-level changes, offering a model for how reinforcement learning might operate across different neural systems.

A groundbreaking study published in Communications Biology has uncovered how reinforcement learning shapes both the selection of motor actions and the timing of their execution in the Aplysia feeding circuit. Researchers led by Yuto Momohara, Curtis L. Neveu, and John H. Byrne from the University of Texas Health Science Center conducted experiments using an ex vivo operant conditioning paradigm on the Aplysia feeding circuit. Their findings reveal that reinforcement biases motor program selection toward ingestive behaviors while simultaneously shortening the duration of these actions by advancing the transition from protraction to retraction phases in feeding movements. The study identified specific neural mechanisms underlying this temporal compression, demonstrating that operant conditioning reduces the intrinsic electrical excitability of neuron B52, which weakens B52-mediated inhibition of the retraction-initiating interneuron B64. This adjustment lowers the threshold for triggering B64's plateau potential, effectively speeding up the feeding cycle. Crucially, phase-restricted co-manipulation of B52 and B64 activity successfully replicated the observed protraction shortening, confirming the circuit-level coordination between action selection and temporal structure. These results establish a fundamental framework for understanding how reinforcement learning optimizes behavioral efficiency through coordinated plasticity within identified neural circuits. The research, supported by National Institutes of Health grant R01 NS101356, provides critical insights into the neural basis of learning and motor control, with potential implications for understanding similar processes in more complex organisms. The findings contribute significantly to neuroscience by bridging the gap between behavioral outcomes and specific circuit-level changes, offering a model for how reinforcement learning might operate across different neural systems.

Muscle weakness is often misdiagnosed. The problem frequently isn't loss of strength capacity—it's dysfunction in motor control, alignment,… #Clinicalassessment #Motorcontrol #Movementdysfunction #Muscleweakness #Rehabilitation

A simple 10-second daily finger exercise may support cognitive function in older adults, according to emerging research. The intervention requires no eq… #Ageingbrain #Cognitivehealth #Motorcontrol #Neuroscience #Preventivehealth