Publication

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Related concepts (32)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Radial basis function network

In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control. They were first formulated in a 1988 paper by Broomhead and Lowe, both researchers at the Royal Signals and Radar Establishment.

Dorsal column–medial lemniscus pathway

The dorsal column–medial lemniscus pathway (DCML) (also known as the posterior column-medial lemniscus pathway, PCML) is a sensory pathway of the central nervous system that conveys sensations of fine touch, vibration, two-point discrimination, and proprioception (body position) from the skin and joints. It transmits information from the body to the primary somatosensory cortex in the postcentral gyrus of the parietal lobe of the brain.

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.

Medulla oblongata

The medulla oblongata or simply medulla is a long stem-like structure which makes up the lower part of the brainstem. It is anterior and partially inferior to the cerebellum. It is a cone-shaped neuronal mass responsible for autonomic (involuntary) functions, ranging from vomiting to sneezing. The medulla contains the cardiac, respiratory, vomiting and vasomotor centers, and therefore deals with the autonomic functions of breathing, heart rate and blood pressure as well as the sleep–wake cycle.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

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.

Spinocerebellar tract

The spinocerebellar tract is a nerve tract originating in the spinal cord and terminating in the same side (ipsilateral) of the cerebellum. Proprioceptive information is obtained by Golgi tendon organs and muscle spindles. Golgi tendon organs consist of a fibrous capsule enclosing tendon fascicles and bare nerve endings that respond to tension in the tendon by causing action potentials in type Ib afferents. These fibers are relatively large, myelinated, and quickly conducting.

Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Big data

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.

Physical neural network

A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.