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Somatosensory, Motor, and Association Cortex

Somatosensory, Motor, and Association Cortex

The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...

Somatosensation

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.

Motor and Sensory Areas of the Cortex

Motor and Sensory Areas of the Cortex

The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor...

Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

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Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.

Max Grogan¹, Kyle P Blum², Yufei Wu¹

¹Department of Bioengineering, Imperial College London, London, United Kingdom.

Plos Computational Biology

|December 4, 2024

Summary

Researchers created a novel computational model to understand proprioception, the sense of body position. The topographic variational autoencoder (topo-VAE) successfully predicted how the brain maps movement, offering insights into sensorimotor control.

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Published on: February 3, 2015

Area of Science:

Neuroscience
Computational Biology
Robotics

Background:

Proprioception, the sense of limb position, is crucial for movement control but poorly understood.
Existing models lack clarity on how the somatosensory cortex represents limb pose during natural movements.

Purpose of the Study:

To develop a computational model that generates a putative cortical map of proprioception from natural movement data.
To investigate the representation of limb pose in the somatosensory cortex.

Main Methods:

Development of a topographic variational autoencoder with lateral connectivity (topo-VAE).
Utilizing a large dataset of natural movement data for model computation.
Comparing model outputs to existing neurophysiological data from monkey reaching tasks.

Main Results:

The topo-VAE model accurately reproduced the velocity-dependent characteristics of proprioceptive receptive fields in hand-centered coordinates without prior kinematic knowledge.
The model's predicted distribution of neuronal preferred directions (PDs) aligned with empirical recordings from monkey brains.
The model predicts a blob-and-pinwheel geometry for PDs and suggests few neurons encode single joints.

Conclusions:

The topo-VAE provides a principled framework for understanding sensorimotor representations and neural manifolds.
The model's success offers a basis for applications in restoring sensory feedback for brain-computer interfaces and controlling humanoid robots.