Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Automatization of Control: Complex Visuomotor Mapping

Description

This lecture explores the automatization of control under a complex visuomotor mapping, focusing on the effects of practicing motor tasks. It delves into how the features of automaticity arise, the emergence of skill and habit, and the neural representations supporting them.

Official source

Related concepts (16)

Motor skill

A motor skill is a function that involves specific movements of the body's muscles to perform a certain task. These tasks could include walking, running, or riding a bike. In order to perform this skill, the body's nervous system, muscles, and brain have to all work together. The goal of motor skill is to optimize the ability to perform the skill at the rate of success, precision, and to reduce the energy consumption required for performance. Performance is an act of executing a motor skill or task.

Fine motor skill

Fine motor skill (or dexterity) is the coordination of small muscles in movement with the eyes, hands and fingers. The complex levels of manual dexterity that humans exhibit can be related to the nervous system. Fine motor skills aid in the growth of intelligence and develop continuously throughout the stages of human development. Motor skills are movements and actions of the bone structures. Typically, they are categorised into two groups: gross motor skills and fine motor skills.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Motor control

Motor control is the regulation of movement in organisms that possess a nervous system. Motor control includes reflexes as well as directed movement. To control movement, the nervous system must integrate multimodal sensory information (both from the external world as well as proprioception) and elicit the necessary signals to recruit muscles to carry out a goal. This pathway spans many disciplines, including multisensory integration, signal processing, coordination, biomechanics, and cognition, and the computational challenges are often discussed under the term sensorimotor control.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Related lectures (35)

Understanding Cortical Contributions to Motor Adaptation

Explores using mice and machine learning to understand cortical contributions to motor adaptation in intelligent systems.

Optimization of Neuroprosthetic Systems

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Motor Neuroprosthetics: Brain-Machine Interfaces

Explores Brain-Machine Interfaces for motor control and communication using neural signals.

Neural Signals and Signal Processing

Explores neural signals, EMG control, muscle synergies, and HD-EMG for prosthetic devices.

Modules and Community Structure of Brain Networks