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Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis. 1976. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Engineering. / Bibliography: leaves 377-385. / Ph.D.

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Abstract not available

Mammalian striate and circumstriate cortical neurons have long been understood as coding spatially localized retinal luminance variations, providing a basis for computing motion, stereopsis, and contours from the retinal image. However, such perceptual attributes do not always correspond to the retinal luminance variations in natural vision. Recordings from area 17 and 18 neurons revealed a specialized nonlinear processing stream that responded to stimulus attributes having no corresponding luminance variations. This nonlinear stream acts in parallel to the conventional luminance processing of single cortical neurons. The two streams were consistent in their preference for orientation and direction of motion, but distinct in processing spatial variations of the stimulus attributes. The ensemble of these neurons provides a combination of stimulus attributes with and without corresponding luminance variations.

No description available.

The physiological characteristics and organization of cat primary somatosensory cortex (SI) were studied in electrophysiological and anatomical experiments. In single cell recording experiments, quantitatively controlled mechanical stimuli were used to examine the responses of SI cortical neurons to the velocity component of skin or hair displacement. The firing frequency of most rapidly adapting neurons increased as stimulus velocity was increased. Rapidly adapting neurons were classified based on their response patterns to constant-velocity ramp stimuli. Neurons in these classes differed significantly in sensitivity to stimulus velocity and amplitude, adaptation rate, and spontaneous firing rate. The results suggest that frequency coding of stimulus displacement velocity could be performed by individual SI rapidly adapting neurons, and that the classes of rapidly adapting neurons may play different roles in sensation of tactile stimuli. Tract-tracing experiments were used to investigate the ipsilateral corticocortical connections of areas 3b and 2 in SI. Different patterns of connections were found for these areas: area 2 projects to areas 3b, 1, 3a, 5a, 4 and second somatosensory cortex (SII), and area 3b projects to areas 2, 1, 3a and SII. To further compare the organization of these areas, the thalamic input to the forepaw representation within each area was studied. The forepaw region in area 3b receives thalamic input exclusively from ventroposteriopr lateral nucleus (VPL), while area 2 receives input from VPL, medial division of the posterior complex (PoM), and lateral posterior nucleus (LP). These results suggest that area 2 lies at a higher position in the hierarchy of somatosensory information flow.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 255-256. / by Thomas Joseph Lynch III. / Ph.D.

When modeling a neuron, modelers often focus on the values of parameters that produce a desired output. However, if these parameters are not unique, there could be a number of parameter sets that produce the same output. Thus, even though the values of the various maximum conductances, half activation voltages and so on differ, as a set they can produce the same spike height, firing rates, and so forth. To examine whether or not parameter sets are unique, a 3-compartment motoneuron model was created that has 15 target outputs and 59 parameters. Using parameter searches, over one hundred parameter sets were created for this model that produced the same output (within tolerances). Parameter values vary between parameter sets and indicate that the parameter values are not unique. In addition, some parameters are more tightly constrained than others. Principal component analysis is used to examine the dimensionality of the input and output spaces. However, neurons are more than static output generators. For example, a variety of neuromodulatory influences are known to shift parameter values to alter neuronal output. Thus the question arises as to whether this non-uniqueness extends from model outputs to the models sensitivities to its parameters. In this work, the non-unique parameter sets are further analyzed using sensitivity analyses and output correlations to show that these values vary significantly between these parameter sets. Therefore, each of these models will react to parameter variation differently. This work concludes that parameter sets are non-unique but have varying sensitivity analyses and output correlations. The ramifications of this are discussed for both modelers and neuroscientists.