Summation characteristics of the neural network subserving self-stimulation reward

• Main Author: Mason, Patrick Alan.
• Format: Others
• Language: en
• Published: McGill University 1984
• Subjects: Brain stimulation.; Reinforcement (Psychology); Reward (Psychology)
• Online Access: http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=72006

This research examines the summation characteristics of the neural network subserving self-stimulation reward. The data show that the neural network has two integrators that sum the signals produced by brain stimulation. The time constant of the first integrator is approximately 450 msec, whereas that of the second integrator is approximately 6.5 sec. Furthermore, these integrators are sensitive to the spatiotemporal arrival of the signals. === When prolonged stimulation is delivered at a high pulse frequency, the initial pulses contribute the most to the rewarding effect. Later pulses are affected by the reduced ability of the neurons or synapses to transmit signals along the neural network due to fatigue. === A fatigue effect may be dissipated by splitting a pulse train into two parts by an interval of no stimulation. This should increase the rewarding effectiveness of the pulse train. However, the rewarding effectiveness is dependent upon the duration of the interval of no stimulation and the magnitude of the two pulse-train halves. A long interval of no stimulation combined with a low stimulation magnitude may cause a frustration response and a decay in memory of the associations between the response, first pulse-train half, and second pulse-train half. These would make the rewarding effectiveness of the two pulse-train halves lower than that of a continuous pulse train. === Previous models of summation are unable to predict the present results. The data are explained in terms of a newly developed model of summation involving two central integrators and fatigue.