Lecture

Building Neural Networks: Assembling Brain Regions

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Description

This lecture focuses on assembling neural networks by defining space and populating it with neurons, using different approaches like arbitrary volumes, simplified volumes, and atlas-based volumes. The speaker discusses the challenges of defining space and positioning cells, emphasizing the importance of accurate morphologies and volume information. The lecture also covers the use of synthetic cells, morphological synthesis, and connectome strategies to recover missing data. Various rules and strategies for cell positioning, morphological synthesis, and connectome reconstruction are explained, highlighting the complexity of building accurate neural networks.

Official source

Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Related concepts (32)

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.

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.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Neural network

A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.

Romani people

The Romani, also spelled Romany or Rromani (ˈroʊməni or ˈrɒməni ), colloquially known as the Roma (: Rom), are an Indo-Aryan ethnic group who traditionally lived a nomadic, itinerant lifestyle. Linguistic and genetic evidence suggests that the Romani originated in the Indian subcontinent; in particular, the region of present-day Rajasthan. Their subsequent westward migration, possibly in waves, is now believed by historians to have occurred around 1000 CE.

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