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Task-driven neural network models predict neural dynamics of proprioception
Related concepts (34)
Deep learning
Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Postcentral gyrus
In neuroanatomy, the postcentral gyrus is a prominent gyrus in the lateral parietal lobe of the human brain. It is the location of the primary somatosensory cortex, the main sensory receptive area for the sense of touch. Like other sensory areas, there is a map of sensory space in this location, called the sensory homunculus. The primary somatosensory cortex was initially defined from surface stimulation studies of Wilder Penfield, and parallel surface potential studies of Bard, Woolsey, and Marshall.
Neuronal ensemble
A neuronal ensemble is a population of nervous system cells (or cultured neurons) involved in a particular neural computation. The concept of neuronal ensemble dates back to the work of Charles Sherrington who described the functioning of the CNS as the system of reflex arcs, each composed of interconnected excitatory and inhibitory neurons. In Sherrington's scheme, α-motoneurons are the final common path of a number of neural circuits of different complexity: motoneurons integrate a large number of inputs and send their final output to muscles.
Motor cortex
The motor cortex is the region of the cerebral cortex involved in the planning, control, and execution of voluntary movements. The motor cortex is an area of the frontal lobe located in the posterior precentral gyrus immediately anterior to the central sulcus. The motor cortex can be divided into three areas: 1. The primary motor cortex is the main contributor to generating neural impulses that pass down to the spinal cord and control the execution of movement. However, some of the other motor areas in the brain also play a role in this function.
Models of neural computation
Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.
Trigeminal nerve
In neuroanatomy, the trigeminal nerve (lit. triplet nerve), also known as the fifth cranial nerve, cranial nerve V, or simply CN V, is a cranial nerve responsible for sensation in the face and motor functions such as biting and chewing; it is the most complex of the cranial nerves. Its name (trigeminal, ) derives from each of the two nerves (one on each side of the pons) having three major branches: the ophthalmic nerve (V_1), the maxillary nerve (V_2), and the mandibular nerve (V_3).
Sensory decussation
In neuroanatomy, the sensory decussation or decussation of the lemnisci is a decussation (i.e. crossover) of axons from the gracile nucleus and cuneate nucleus, which are responsible for fine touch, vibration, proprioception and two-point discrimination of the body. The fibres of this decussation are called the internal arcuate fibres and are found at the superior aspect of the closed medulla superior to the motor decussation. It is part of the second neuron in the posterior column–medial lemniscus pathway.
Brainstem
The brainstem (or brain stem) is the stalk-like part of the brain that interconnects the cerebrum and diencephalon with the spinal cord. In the human brain the brainstem is composed of the midbrain, the pons, and the medulla oblongata. The midbrain is continuous with the thalamus of the diencephalon through the tentorial notch. The brainstem is very small, making up around only 2.6 percent of the brain's total weight. It has the critical roles of regulating cardiac, and respiratory function, helping to control heart rate and breathing rate.
Brodmann area
A Brodmann area is a region of the cerebral cortex, in the human or other primate brain, defined by its cytoarchitecture, or histological structure and organization of cells. The concept was first introduced by the German anatomist Korbinian Brodmann in the early 20th century. Brodmann mapped the human brain based on the varied cellular structure across the cortex and identified 52 distinct regions, which he numbered 1 to 52. These regions, or Brodmann areas, correspond with diverse functions including sensation, motor control, and cognition.
Feedforward neural network
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.