Biological neural network

In neuroscience, a biological neural network (sometimes called a neural pathway) is a series of interconnected neurons whose activation defines a recognizable linear pathway. The interface through which neurons interact with their neighbors usually consists of several axon terminals connected via synapses to dendrites on other neurons. If the sum of the input signals into one neuron surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon.

In contrast, a neural circuit is a functional entity of interconnected neurons that is able to regulate its own activity using a feedback loop (similar to a control loop in cybernetics).

Biological neural networks have inspired the design of artificial neural networks.

Early study

Early treatments of neural networks can be found in Herbert Spencer's Principles of Psychology, 3rd edition (1872), Theodor Meynert's Psychiatry (1884), William James' Principles of Psychology (1890), and Sigmund Freud's Project for a Scientific Psychology (composed 1895). The first rule of neuronal learning was described by Hebb in 1949, Hebbian learning. Thus, Hebbian pairing of pre-synaptic and post-synaptic activity can substantially alter the dynamic characteristics of the synaptic connection and therefore facilitate or inhibit signal transmission. The neuroscientists Warren Sturgis McCulloch and Walter Pitts published the first works on the processing of neural networks. They showed theoretically that networks of artificial neurons could implement logical, arithmetic, and symbolic functions. Simplified models of biological neurons were set up, now usually called perceptrons or artificial neurons. These simple models accounted for neural summation (i.e., potentials at the post-synaptic membrane will summate in the cell body). Later models also provided for excitatory and inhibitory synaptic transmission.

Artificial neural network

In machine learning and cognitive science, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected "neurons" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.

For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

Neural network (disambiguation)

Neural network(s) may refer to:

Crash Course

Crash Course (also known as Driving Academy) is a 1988 made for television teen film directed by Oz Scott.

Plot

Crash Course centers on a group of high schoolers in a driver’s education class; many for the second or third time. The recently divorced teacher, super-passive Larry Pearl, is on thin ice with the football fanatic principal, Principal Paulson, who is being pressured by the district superintendent to raise driver’s education completion rates or lose his coveted football program. With this in mind, Principal Paulson and his assistant, with a secret desire for his job, Abner Frasier, hire an outside driver’s education instructor with a very tough reputation, Edna Savage, aka E.W. Savage, who quickly takes control of the class.

The plot focuses mostly on the students and their interactions with their teachers and each other. In the beginning, Rico is the loner with just a few friends, Chadley is the bookish nerd with few friends who longs to be cool and also longs to be a part of Vanessa’s life who is the young, friendly and attractive girl who had to fake her mother’s signature on her driver’s education permission slip. Kichi is the hip-hop Asian kid who often raps what he has to say and constantly flirts with Maria, the rich foreign girl who thinks that the right-of-way on the roadways always goes to (insert awesomely fake foreign Latino accent) “my father’s limo”. Finally you have stereotypical football meathead J.J., who needs to pass his English exam to keep his eligibility and constantly asks out and gets rejected by Alice, the tomboy whose father owns “Santini & Son” Concrete Company. Alice is portrayed as being the “son” her father wanted.

Soft computing

In computer science, soft computing is the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.

Introduction

Soft computing (SC) solutions are unpredictable, uncertain and between 0 and 1. Soft Computing became a formal area of study in Computer Science in the early 1990s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. However, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. As such it forms the basis of a considerable amount of machine learning techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and bio-inspired computation.