Lecture

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

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Description

This lecture by Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on the scale and complexity of data, stimuli, and measured brain activity. The research overview delves into how the brain processes the external world, scene and object perception, and large-scale data analysis. The lecture discusses the representation of brain activity, the use of artificial intelligence to understand brain computations, and the application of probabilistic models and deep learning. It also covers the discovery of neural selectivities for faces, places, bodies, words, and food, and the development of computational models to predict brain responses to external stimuli.

Official source

Ontological neighbourhood

Biology

Neuroscience: Computational neuroscience

Anatomy: Neuroanatomy

Information engineering

Machine learning: Artificial neural networks

Related concepts (32)

Large-scale brain network

Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected".

Neural decoding

Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in the brain by networks of neurons. Reconstruction refers to the ability of the researcher to predict what sensory stimuli the subject is receiving based purely on neuron action potentials. Therefore, the main goal of neural decoding is to characterize how the electrical activity of neurons elicit activity and responses in the brain.

Model of computation

In computer science, and more specifically in computability theory and computational complexity theory, a model of computation is a model which describes how an output of a mathematical function is computed given an input. A model describes how units of computations, memories, and communications are organized. The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.

Computation

A computation is any type of arithmetic or non-arithmetic calculation that is well-defined. Common examples of computations are mathematical equations and computer algorithms. Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. The study of computation is the field of computability, itself a sub-field of computer science. The notion that mathematical statements should be ‘well-defined’ had been argued by mathematicians since at least the 1600s, but agreement on a suitable definition proved elusive.

Component Object Model

Component Object Model (COM) is a binary-interface standard for software components introduced by Microsoft in 1993. It is used to enable inter-process communication object creation in a large range of programming languages. COM is the basis for several other Microsoft technologies and frameworks, including OLE, OLE Automation, Browser Helper Object, ActiveX, COM+, DCOM, the Windows shell, DirectX, UMDF and Windows Runtime.

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