MarthaAndrew 42M/36F
3 posts
8/12/2005 10:38 am

Last Read:
3/5/2006 9:27 pm


The Connectionist Approach

This section is designed to give the reader who is unfamiliar with connectionist models some of the basic vocabulary which will be useful in reading the remainder of the review. It is not the goal of this review to provide a comprehensive introduction to the study of connectionist and neural network modeling. Many sources are available for this purpose. A number of excellent review articles are available which summarize specific types of connectionist networks the mathematics and mechanics of computational simulations of connectionist networks and the relative strengths of this type of modeling over traditional modeling technologies. Connectionist models represent information throughout a connected network of units. In some connectionist networks each unit has a particular meaning (e.g., a single unit represents the idea “dog”. In another class of these models, termed “neural networks”, the units are each individually meaningless and information is represented only in a distributed fashion, as a function of the simultaneous activation of multiple units. In a connectionist model, each unit receives “activation” from other units to which it is connected in response to the stimulation of these units. The unit then sends a transfer function of the activations coming into it to other units to which it is connected. The transfer function may be a function of the sum of the units inputs or may be more complex, involving temporal factors such as the average input over time. Thus, connections between units may be excitatory (if the sum is greater than zero, leading to relative activation of the unit to which receives the connection) or inhibitory (if the sum is less than zero, leading to relative inactivation of the unit which receives the connection). The degree to which such units resemble biological neurons varies across implementations. Most articles to be reviewed here use relatively simple analogs of neurons. The lines on the left of the node in connections from other units coming into the node, much as dendrites are connected to a biological neuron. The lines on the right of the node represent connections extending out from the node, through which its activation will propagate, much as an axon caries the activations of a biological neuron. Often, each unit is also given a weight or "bias" loosely corresponding to a neuron's threshold for activation. Units in a connectionist network have thus been likened to biological neurons which receive information on dendrites and send out an aggregate of that informtion over axons. Environmental or internal factors (e.g., visual stimuli) which cause units to be “stimulated” are determined and modeled explicitly by the connectionist modeler. Common computational analog of a neuron. Activation enters through channels on the neuron's left which represent dendrites. The strength of activation over a given pathway is governed by the activation coming from the neurons to which the paths are connected and the strength of the paths themselves. The neuron's body computes a function of the sum of these activations (e.g., a sigmoid function) and propagates this function through the connections to other units depicted on the neuron's right. These neuron-like nodes may be assembled in arbitrary patterns. The structure, or "architecture" of the network governs what information may pass into the network, the manner in which nodes may activate each other, and what information may be said to flow out of the network. Association with a single representation of a concept (e.g., entity or relationship) in a neural network may involve activation of a number of nodes in the connected cognitive network, such that the resultant pattern of activation represents a meaningful association though no individual node which is activated connotes this meaning. For example, activation of the highlighted nodes in Figure 2 may represent a particular concept such as a letter of the alphabet. Were some of these nodes not activated at a particular time, the entire concept may be said to be incompletely activated (e.g., if the network represents cognitive association, the network may be interpretted as not generating a clear reminding in response to a stimulus). Thus, the lack of central control in such networks, robustness of each node or information processing element to noise, and ability to model changes in state via changing activations make such networks particularly suited to modeling information processing tasks.
A connectionist network’s response to a stimulus generally involves successive activation of a number of nodes in the network. This pattern of activations can represent an association in memory between two stimuli, a reaction to an external stimulus, or some other construct based on the network’s architecture and the designer’s conception of the network. Thus the processes operating within connectionist networks can be assumed to correspond to neuronal, cognitive, or behavioral events based on the intuitions of the network’s designer. For this reason, it is said that connectionist networks can model multiple psychological “granularities” or “levels of analysis”. By strategically modifying the strengths of connections between units in response to a stimulus the network can be made to associate one set of activations with another (e.g., representing an association in memory between two stimuli). For example, the activation of one node may be associated with the activation of other nodes by increasing the strength of connections between these nodes. When this process is automated using a mathematical algorithm, the network is said to "learn" an association. As learned associations have been the foundation for many schools of psychology (e.g., behaviorism and structuralism) this process by which the network is made to associate one set of values (possibly representing environmental stimuli) with another (e.g., behaviors) is often implicated in models of psychopathology. Once a connectionist network model has been created, its behavior can be evaluated on a number of dimensions. The choice of what dimension is to be evaluated generally reflects the processes which the network is designed to simulate. For example, if the network is designed to simulate performance on some information processing task, associations made by the network could be compared to correct assocations; an analog of human error rate could thus be established. Similarly, it may take a number of associative steps for a network to settle on a learned association. The number of associative processing cycles or “epochs” the network needs to associate a stimulus with a particular response can be examined as an analog of reaction time. Alternately, if the network is designed to simulate association with a single concept in response to a stimulus, the amount of randomness in the network’s associations over time can be examined as an analog of flightiness of ideas.

japaneselee 49M
407 posts
8/12/2005 12:27 pm

mmm yummy pics. pls contact me!

rm_bigbee67 49M
1 post
9/20/2005 11:12 pm

Would it be possible to post the associated illustrations? I am a visual learner, in addition to being a native of the the "Show-Me State. With pictures, it makes it easier to remember the "why"; then the "what" can be logically recalled. And in closing let me add that the profile pictures you posted were a educational experience. I hope my girlfriend, a resident of the capital city, and I are able to learn much more...visual, auditory, and kinesthetically!

pipes528 51M/48F
6 posts
10/7/2005 10:36 pm

Well the site of your sexy body sure stirred up my neuron's .Hmmm, always wanted to take a trip to Missouri.

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