Behaviorism

Behaviorism (or behaviourism), is the science of behavior that focuses on observable behavior only,[1] it is also an approach to psychology that combines elements of philosophy, methodology, and theory.[2] It emerged in the early twentieth century as a reaction to "mentalistic" psychology, which often had difficulty making predictions that could be tested using rigorous experimental methods. The primary tenet of behaviorism, as expressed in the writings of John B. Watson, B. F. Skinner, and others, is that psychology should concern itself with the observable behavior of people and animals, not with unobservable events that take place in their minds.[3] The behaviorist school of thought maintains that behaviors as such can be described scientifically without recourse either to internal physiological events or to hypothetical constructs such as thoughts and beliefs.[4] Versions[edit] Two subtypes are: Definition[edit] Experimental and conceptual innovations[edit] Relation to language[edit] Related: ⏫ AI

Allen Newell Allen Newell (March 19, 1927 – July 19, 1992) was a researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Department of Psychology. He contributed to the Information Processing Language (1956) and two of the earliest AI programs, the Logic Theory Machine (1956) and the General Problem Solver (1957) (with Herbert A. Simon). He was awarded the ACM's A.M. Turing Award along with Herbert A. Simon in 1975 for their basic contributions to artificial intelligence and the psychology of human cognition.[1][2] Early studies[edit] Newell completed his Bachelor's degree from Stanford in 1949. Afterwards, Newell "turned to the design and conduct of laboratory experiments on decision making in small groups" (Simon). Artificial intelligence[edit] His work came to the attention of economist (and future nobel laureate) Herbert A. Later achievements[edit] Awards and honors[edit]

His Ideas Activation function In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. This is similar to the behavior of the linear perceptron in neural networks. However, it is the nonlinear activation function that allows such networks to compute nontrivial problems using only a small number of nodes. Functions[edit] In biologically inspired neural networks, the activation function is usually an abstraction representing the rate of action potential firing in the cell. , where is the Heaviside step function. A line of positive slope may also be used to reflect the increase in firing rate that occurs as input current increases. is the slope. All problems mentioned above can be handled by using a normalizable sigmoid activation function. , where the hyperbolic tangent function can also be any sigmoid. where

Generative grammar Early versions of Chomsky's theory were called transformational grammar, and this term is still used as a general term that includes his subsequent theories. There are a number of competing versions of generative grammar currently practiced within linguistics. Chomsky's current theory is known as the Minimalist program. Chomsky has argued that many of the properties of a generative grammar arise from an "innate" universal grammar. Most versions of generative grammar characterize sentences as either grammatically correct (also known as well formed) or not. Frameworks[edit] There are a number of different approaches to generative grammar. Historical development of models of transformational grammar[edit] Chomsky, in an award acceptance speech delivered in India in 2001, claimed "The first generative grammar in the modern sense was Panini's grammar".This work, called the Ashtadhyayi, was composed in 6th century BC. Standard Theory (1957–1965)[edit] Extended Standard Theory (1965–1973)[edit]

free variables and bound variables A bound variable is a variable that was previously free, but has been bound to a specific value or set of values. For example, the variable x becomes a bound variable when we write: 'For all x, (x + 1)2 = x2 + 2x + 1.' or 'There exists x such that x2 = 2.' In either of these propositions, it does not matter logically whether we use x or some other letter. Examples[edit] Before stating a precise definition of free variable and bound variable, the following are some examples that perhaps make these two concepts clearer than the definition would: In the expression n is a free variable and k is a bound variable; consequently the value of this expression depends on the value of n, but there is nothing called k on which it could depend. y is a free variable and x is a bound variable; consequently the value of this expression depends on the value of y, but there is nothing called x on which it could depend. Variable-binding operators[edit] The following are variable-binding operators. for sums or where

Profiles: The Devil’s Accountant PROFILE of Noam Chomsky... Writer describes the scene during Chomsky’s Thursday evening M.I.T. class about politics... When Chomsky likened the September 11th attacks to Clinton’s bombing of a factory in Khartoum, many found the comparison not only absurd but repugnant: how could he speak in the same breath of an attack intended to maximize civilian deaths and one intended to minimize them?

Spiking neural network Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal. In the context of spiking neural networks, the current activation level (modeled as some differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, and then either firing or decaying over time. Beginnings[edit] Applications[edit] CoDi

Commentary Steve Furber Stephen Byram "Steve" Furber CBE, FRS, FREng (born 1953) is the ICL Professor of Computer Engineering at the School of Computer Science at the University of Manchester[61] and is probably best known for his work at Acorn Computers, where he was one of the designers of the BBC Micro and the ARM 32-bit RISC microprocessor.[3][62][63][57][64][65][66] Education[edit] Furber was educated at Manchester Grammar School and represented the UK in the International Mathematical Olympiad in Hungary in 1970 and won a bronze medal.[67] He went on to study the Cambridge Mathematical Tripos at St John's College, Cambridge, receiving a Bachelor of Arts degree in mathematics in 1974. Acorn Computers, BBC Micro and ARM[edit] From 1980 to 1990, Furber worked at Acorn Computers where he was a Hardware Designer and then Design Manager. Research[edit] In 2003, Furber was a member of the EPSRC research cluster in biologically-inspired[70] novel computation. Awards and honours[edit]