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Notes: Abstract A simulated neuron was constructed in which effects on spike discharge of altering certain fundamental biophysical parameters could be studied. The simulation was performed by use of a digital computer. The simulation was tested by comparing performance of the simulated neuron with that of actual neurons. Rates and patterns of spike discharge were achieved for the simulated neuron that were comparable to those recorded from units in the motor cortex of awake cats. Altering biophysical parameters such as firing threshold, levels of synaptic input or rates of transverse and longitudinal current spread produced appropriate alterations of discharge rate. On the above basis it was possible to investigate interrelated effects on spike discharge of changing levels of synaptic input and rates of current spread within the simulated neuron. With low rates of longitudinal current spread, graded levels of synaptic input produced correspondingly graded levels of ouput discharge. With high rates of longitudinal current spread, the transfer properties of the neuron were markedly altered. The neuron became a bistable operator where synaptic inputs above a certain level were enhanced and all those below were suppressed. A linear relationship was found to exist between firing threshold and the level of synaptic input required to reach the transition from quiescence to near-tetanic rates of discharge. Alterations in behavior are increasingly thought to be subserved by changes in the efficacy of synaptic transmission or in the post-synaptic intergrative propeties of neurons. The results of our investigations describe the interplay between those two processes.

Notes: Abstract In a simulated neuron with a dendritic tree, the relative effects of active and passive dendritic membranes on transfer properties were studied. The simulations were performed by means of a digital computer. The computations calculated the changes in transmembrane voltages of many compartments over time as a function of other biophysical variables. These variables were synaptic input intensity, critical firing threshold, rate of leakage of current across the membrane, and rate of longitudinal current spread between compartments. For both passive and active dendrites, the transfer properties of the soma studied for different rates of longitudinal current spread. With low rates of current spread, graded changes in firing threshold produced correspondingly graded changes in output discharge. With high rates of current spread, the neuron became a bistable operator where spiking was enhanced if the threshold was below a certain level and suppressed if the threshold was above that level. Since alterations in firing threshold were shown to have the same effect on firing rate as alterations in synaptic input intensity, the neuron can be said to change from graded to contrast-enhancing in its response to stimuli of different intensities. The presence or absence of dendritic spiking was found to have a significant effect on the integrative properties of the simulated neuron. In particular, contrast enhancement was considerably more pronounced in neurons with passive than with active dendrites in that somatic spike rates reached a higher maximum when dendrites were passive. With active dendrites, a less intense input was needed to initiate somatic spiking than with passive dendrites because a distal dendritic spike could easily propagate by means of longitudinal current spread to the soma. Once somatic spiking was initiated, though, spike rates tended to be lower with active than with passive dendrites because the soma recovered more slowly from its post-spike refractory period if it was also influenced by refractory periods in the dendrites. The experiment of comparing neurons with active and passive dendrites was repeated at a different, higher value of synaptic input. The same differences in transfer properties between the active and passive cases emerged as before. Spiking patterns in neurons with active dendrites were also affected by the time distribution of synaptic inputs. In a previous study, inputs had been random over both space and time, varying about a predetermined mean, whereas in the present study, inputs were random over space but uniform over time. When inputs were made uniform over time, spiking became more difficult to initiate and the transition from graded to bistable response became less sharp.

Notes: Summary Linear filter analysis was used to detect the occurrence of neuroelectric signals in associated noisy background electrical activity by matching a signal template against incoming neuroelectric data. Bach signal to be detected in the neuroelectric data consisted of a gross potential change recorded at the coronal — precruciate cortex of a cat, evoked by an auditory conditional stimulus, and related to the production of a conditioned facial movement. Detection of the occurrence of the signal corresponded closely to detection of the ensuing movement. The operation of the matched filter on the signals in noise was studied for different threshold levels of detection. Threshold settings were selected to maximize successful detections and to minimize false alarms. The results of our experimental detections agreed closely with predicted, theoretical detection levels derived from Wiener's models of optimum detection of signals in additive noise. Levels of detection were found to depend upon the signal to noise ratios and frequency spectra of the analyzed data and could be predicted a priori from a knowledge of the latter on the basis of Wiener's theory. The ability to predict optimum detection levels, by the linear filter method, of cortical electrical signals related to the production of movements may provide a basis for evaluating the merit of such signals in the design of prosthetic devices for motor control.

Notes: Abstract Features of two potassium conductances implicated in the acquisition of conditioned reflexes, the slow calcium dependent conductance (gK+(Ca)) and the fast transient conductance (gK+(A)), were incorporated into a 6 × 6 element artificial neural network. Adaptive algorithms derived from observations of cortical neurons during associative learning changed gK+(A) in proportion to the product of this current and an EPSP-induced second messenger concentration, and changed gK+(Ca) as a function of a spike-induced second messenger concentration. This network concurrently acquired two distinct representations in response to presentation of stimuli: one resembled associative conditioning (defined in terms of its senstivity to forward pairing vs. simultaneous or backward pairing); the other reflected contiguous pairings of stimuli. The acquisition of one representation did not markedly interfere with acquisition of the other. This network may accordingly serve as an example of a self-organizing system which minimizes the postulated inherent cross talk between functionally dissiminar representations (Minsky and Papert 1988).