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D E Rumelhart

Publications and source records attributed to D E Rumelhart.

6 recordsLinked to original sources
PubMed1998

Somatotopy of the human arm using fMRI.

We describe a technique for mapping out human somatosensory cortex using functional magnetic resonance imaging (fMRI). To produce cortical activation, a pneumatic apparatus presented subjects with a periodic series of air puffs in which a sliding window of five locations moved along the ventral surface of the left arm in a proximal-to-distal or distal-to-proximal direction. This approach, in which the phase-delay of the stimulus can be used to produce somatotopic maps of somatosensory cortex, is based on a method used to generate retinotopic maps of visual cortex. Functional images were acquired using an echoplanar 1.5T scanner and a T2*-weighted spiral acquisition pulse sequence. The periodic series of air puffs created phase-related activation in two cortical regions of the contralateral parietal lobe, the posterior bank of the central sulcus and a more posterior and lateral region.

Adult↗
PubMed1985

Distributed memory and the representation of general and specific information.

We describe a distributed model of information processing and memory and apply it to the representation of general and specific information. The model consists of a large number of simple processing elements which send excitatory and inhibitory signals to each other via modifiable connections. Information processing is thought of as the process whereby patterns of activation are formed over the units in the model through their excitatory and inhibitory interactions. The memory trace of a processing event is the change or increment to the strengths of the interconnections that results from the processing event. The traces of separate events are superimposed on each other in the values of the connection strengths that result from the entire set of traces stored in the memory. The model is applied to a number of findings related to the question of whether we store abstract representations or an enumeration of specific experiences in memory. The model simulates the results of a number of important experiments which have been taken as evidence for the enumeration of specific experiences. At the same time, it shows how the functional equivalent of abstract representations--prototypes, logogens, and even rules--can emerge from the superposition of traces of specific experiences, when the conditions are right for this to happen. In essence, the model captures the structure present in a set of input patterns; thus, it behaves as though it had learned prototypes or rules, to the extent that the structure of the environment it has learned about can be captured by describing it in terms of these abstractions.

Association Learning↗