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Biomedical subjects

I W Hunter

Publications and source records attributed to I W Hunter.

63 records · Page 4Linked to original sources
PubMed1981

Respiratory components of human postural sway.

The effects of respiration on postural sway in normal human subjects was studied using averaging and spectral techniques. During voluntarily paced respiration a substantial proportion of sway was found to result from respiratory activity. The magnitude of the respiratory contribution to sway was approximately constant over the normal range of respiratory rates and linearly related to respiration amplitude. Respiration evoked similar changes in reaction torque in seated subjects. These results indicate that body movements associated with paced respiration are an important disturbance to the postural control system. Furthermore, these disturbances are not reduced by compensatory movements of the hip as has been suggested by others.

Humans↗
PubMed1979

Concurrent schedules: undermatching and control by previous experimental conditions.

Five pigeons were trained on concurrent variable-interval schedules. A series of conditions in which the ratio of reinforcement rates on two keys was progressively increased and then decreased was arranged twice. The birds were then exposed to an irregular sequence of conditions. Each condition in which reinforcement was available on both keys lasted six sessions. Performance in the first, third, and sixth sessions after a condition change was analyzed. Following a condition change, preference was biased toward the preference in the last condition, but this effect largely disappeared before the sixth session of training. The birds' preferences also appeared less sensitive to reinforcement rates in early sessions after a transition. Preference in a session was a function of both the reinforcements in that session and the reinforcements obtained in as many as four or five previous sessions. The effects of reinforcements in previous sessions could be summarized by the performance in the immediately preceding session, giving a relatively simple relation between present performance and a combination of present reinforcement and prior session performance. While such hysteresis could cause undermatching when only a small number of sessions are arranged in a condition, undermatching in a stable-state performance probably arises elsewhere.

Animals↗
PubMed1978

Response rate and changeover performance on concurrent variable-interval schedules.

Six pigeons were exposed to variable-interval schedules arranged on one, two, three, and four response keys. The reinforcement rate was also varied across conditions. Numbers of responses, the time spent responding, the number of reinforcements, and the number of changeovers between keys were recorded. Response rates on each key were an increasing function of reinforcement rate on that key and a decreasing function of the reinforcement rate on other keys. Response and time-allocation ratios under-matched ratios of obtained reinforcements. Three sets of equations were developed to express changeover rate as a function of response rate, time allocation, and reinforcement rate respectively. These functions were then applied to a broad range of experiments in the literature in order to test their generality. Further expressions were developed to account for changeover rates reported in experiments where changeover delays were varied.

Journal Article↗
PubMed1976

Performance on variable-interval schedules arranged singly and concurrently.

Extensive parametric data were obtained from pigeons responding on variable-interval schedules arranged on three, two, and one response keys. Number of responses on the keys, the time spent responding on the keys, and the number of reinforcements obtained on the keys were measured. Response rates on each key were an increasing function of the reinforcement rate on that key, and an inverse function of the reinforcement rate on the other keys. In terms of preference, both response and time-allocation ratios undermatched ratios of obtained reinforcements, and the degree of undermatching was consistent both within, and between, two- and three-schedule data. When absolute response-rate data were analyzed according to Herrnstein's (1970) quantitative account, obtained values of assumed constants were not consistent either within or between conditions. However, a power-function modification of Herrnstein's account fitted the data well and provided similar exponent values to those obtained for the undermatching of preference ratios.

Journal Article↗
PubMedDate not supplied

The identification of nonlinear biological systems: Volterra kernel approaches.

Representation, identification, and modeling are investigated for nonlinear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximated by a Volterra series (or functional expansion). Next, we examine system identification through estimating the kernels in a Volterra functional expansion approximation for the system. A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biological applications of kernel estimation techniques.

Algorithms↗
PubMedDate not supplied

The identification of nonlinear biological systems: Volterra kernel approaches.

Representation, identification, and modeling are investigated for nonlinear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximated by a Volterra series (or functional expansion). Next, we examine system identification through estimating the kernels in a Volterra functional expansion approximation for the system. A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biological applications of kernel estimation techniques.

Linear Models↗
PubMedDate not supplied

Two methods for identifying Wiener cascades having noninvertible static nonlinearities.

Two methods are proposed for identifying the component elements of a Wiener cascade that is comprised of a dynamic linear element (L) followed by a static nonlinearity (N). Both methods avoid potential problems of instability in a procedure presented by Paulin [M. G. Paulin, Biol. Cybern. 69: 67-76, 1993], which itself is a modification of a method described earlier by Hunter and Korenberg [I. W. Hunter and M. J. Korenberg, Biol. Cybern. 55: 135-144, 1996]. The latter method is a rapidly convergent iterative procedure that produces accurate estimates of the L and N elements from short data records, provided that the static nonlinearity N is invertible. Subsequently, Paulin introduced a modification that removed this limitation and enabled identification of Wiener cascades with nonmonotonic static nonlinearities. However, Paulin presented his modification employing an autoregressive moving average (ARMA) model representation for the dynamic linear element. To remove the possibility that the estimated ARMA model could be unstable, we recast the procedure by utilizing instead a rapid method for finding an impulse response representation for the dynamic linear element. However, in this form the procedure did not have good convergence properties, so we introduced two key ideas, both of which provide effective alternatives for identifying Wiener cascades whether or not the static nonlinearities therein are invertible. The new procedures are illustrated on challenging examples involving high-degree polynomial static nonlinearities, of odd or even symmetry, a high-pass linear element, and output noise corruption of 50%.

Algorithms↗