A Neuroscientist's Radical Theory of How Networks Become Conscious - Wired Science It’s a question that’s perplexed philosophers for centuries and scientists for decades: Where does consciousness come from? We know it exists, at least in ourselves. But how it arises from chemistry and electricity in our brains is an unsolved mystery. Neuroscientist Christof Koch, chief scientific officer at the Allen Institute for Brain Science, thinks he might know the answer. “The electric charge of an electron doesn’t arise out of more elemental properties. What Koch proposes is a scientifically refined version of an ancient philosophical doctrine called panpsychism — and, coming from someone else, it might sound more like spirituality than science. Koch’s insights have been detailed in dozens of scientific articles and a series of books, including last year’s Consciousness: Confessions of a Romantic Reductionist. WIRED: How did you come to believe in panpsychism? Christof Koch: I grew up Roman Catholic, and also grew up with a dog. 'What is the simplest explanation?
Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. 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. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background History Farley and Wesley A. Models or both
Cellular neural network In computer science and machine learning, cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing non-visual problems to geometric maps, modelling biological vision and other sensory-motor organs. CNN architecture Due to their number and variety of architectures, it is difficult to give a precise definition for a CNN processor. Cells are defined in a normed space, commonly a two-dimensional Euclidean geometry, like a grid. Most CNN architectures have cells with the same relative interconnect, but there are applications that require, Multiple-Neighborhood-Size CNN (MNS-CNN), consisting of spatially variant topology. Literature review There are several overviews of CNN processors. Related processing architectures Model of computation
Restricted Boltzmann machine Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units). A restricted Boltzmann machine (RBM) is a generative stochastic neural network that can learn a probability distribution over its set of inputs. RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986, but only rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000s. RBMs have found applications in dimensionality reduction, classification, collaborative filtering, feature learning and topic modelling. They can be trained in either supervised or unsupervised ways, depending on the task. Restricted Boltzmann machines can also be used in deep learning networks. In particular, deep belief networks can be formed by "stacking" RBMs and optionally fine-tuning the resulting deep network with gradient descent and backpropagation. Structure and visible unit for the visible units and where
Les architectures neuromorphiques Les ordinateurs sont vraiment loin d'être les seuls systèmes capables de traiter de l'information. Les automates mécaniques furent les premiers à en être capables : les ancêtres des calculettes étaient bel et bien des automates basés sur des pièces mécaniques, et n'étaient pas programmables. Par la suite, ces automates furent remplacés par les circuits électroniques analogiques et numériques non-programmables. La suite logique fût l'introduction de la programmation : l'informatique était née. De nos jours, de nouveaux types de circuits de traitement de l’information ont vu le jour. Dans ce qui va suivre, nous allons voir : Réseaux de neurones matériels Ces architectures s'inspirent fortement du cerveau humain, et du fonctionnement du système nerveux. Mais dans d'autres cas, le réseau de neurone peut apprendre tout seul à partir d'exemples, et est capable de catégoriser de lui-même les entrées qu'on lui fournit. Simulation matérielle de systèmes nerveux Les accélérateurs matériels Memristors
Deep learning Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts. Definition Deep learning is a class of machine learning algorithms that:(pp199–200) use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Overview Interpretations
Multilayer Perceptron Neural Networks A Brief History of Neural Networks Neural networks are predictive models loosely based on the action of biological neurons. The selection of the name “neural network” was one of the great PR successes of the Twentieth Century. It certainly sounds more exciting than a technical description such as “A network of weighted, additive values with nonlinear transfer functions”. However, despite the name, neural networks are far from “thinking machines” or “artificial brains”. A typical artifical neural network might have a hundred neurons. The original “Perceptron” model was developed by Frank Rosenblatt in 1958. Interest in neural networks was revived in 1986 when David Rumelhart, Geoffrey Hinton and Ronald Williams published “Learning Internal Representations by Error Propagation”. Types of Neural Networks When used without qualification, the terms “Neural Network” (NN) and “Artificial Neural Network” (ANN) usually refer to a Multilayer Perceptron Network. Multilayer Perceptron Architecture
Self-organizing map A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map or network. Like most artificial neural networks, SOMs operate in two modes: training and mapping. A self-organizing map consists of components called nodes or neurons. Large SOMs display emergent properties. Learning algorithm Variables
An Introduction to Neural Networks Prof. Leslie Smith Centre for Cognitive and Computational Neuroscience Department of Computing and Mathematics University of Stirling. firstname.lastname@example.org last major update: 25 October 1996: minor update 22 April 1998 and 12 Sept 2001: links updated (they were out of date) 12 Sept 2001; fix to math font (thanks Sietse Brouwer) 2 April 2003 This document is a roughly HTML-ised version of a talk given at the NSYN meeting in Edinburgh, Scotland, on 28 February 1996, then updated a few times in response to comments received. What is a neural network? Some algorithms and architectures. Where have they been applied? What new applications are likely? Some useful sources of information. Some comments added Sept 2001 NEW: questions and answers arising from this tutorial Why would anyone want a `new' sort of computer? What are (everyday) computer systems good at... .....and not so good at? Good at Not so good at Fast arithmetic Interacting with noisy data or data from the environment Massive parallelism Courses
Machine Learning Project at the University of Waikato in New Zealand Neural Network Applications An Artificial Neural Network is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof. There are many different types of Neural Networks, each of which has different strengths particular to their applications. The abilities of different networks can be related to their structure, dynamics and learning methods. 2.0 Applications There are abundant materials, tutorials, references and disparate list of demos on the net.
Connectionism Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience, and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units. There are many forms of connectionism, but the most common forms use neural network models. Basic principles The central connectionist principle is that mental phenomena can be described by interconnected networks of simple and often uniform units. The form of the connections and the units can vary from model to model. For example, units in the network could represent neurons and the connections could represent synapses. Spreading activation In most connectionist models, networks change over time. Neural networks Most of the variety among neural network models comes from: Biological realism Learning The weights in a neural network are adjusted according to some learning rule or algorithm.
Google scientist Jeff Dean on how neural networks are improving everything Google does Simon Dawson Google's goal: A more powerful search that full understands answers to commands like, "Book me a ticket to Washington DC." Jon Xavier, Web Producer, Silicon Valley Business Journal If you've ever been mystified by how Google knows what you're looking for before you even finish typing your query into the search box, or had voice search on Android recognize exactly what you said even though you're in a noisy subway, chances are you have Jeff Dean and the Systems Infrastructure Group to thank for it. As a Google Research Fellow, Dean has been working on ways to use machine learning and deep neural networks to solve some of the toughest problems Google has, such as natural language processing, speech recognition, and computer vision. Q: What does your group do at Google? A: We in our group are trying to do several things. |View All
Dimensionality reduction In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction. Feature selection Feature extraction The main linear technique for dimensionality reduction, principal component analysis, performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. Principal component analysis can be employed in a nonlinear way by means of the kernel trick. An alternative approach to neighborhood preservation is through the minimization of a cost function that measures differences between distances in the input and output spaces. Dimension reduction See also Notes Jump up ^ Roweis, S. References Fodor,I. (2002) "A survey of dimension reduction techniques". External links