Adaptation by change of passive membrane resistance

Summary form only given, as follows. Membrane resistance is modeled in short-term memory equations of neural networks as a self-relaxation parameter which ensures exponential decay to zero when the input stimulus is removed. Several models were examined, and it was shown that steady-state network response and sensitivity is dependent on the (passive) membrane resistance. Change of membrane resistance thus provides a mechanism for post-sensory adaptation. The results were examined through analog hardware implementation and simulation of one of the neural network models, and agreement with theoretical analysis was observed. A simple mechanism for tuning the operating point and sensitivity was implemented and demonstrated. The implementation has direct technological application while the tuning mechanism itself explains some of the short-term adaptive behavior of biological systems