Certain cells in the mammalian auditory brainstem (three neurons from the periphery) show exceptionally small standard deviations in the microsecond range of the timing of the first action potential response under repeated trials of the same sound. Moreover, the latency and its standard deviation are quite independent of frequency and decibel level over large ranges. This behavior is exceptional since the inputs to these cells come from octopus and other cells in the contralateral cochlear nucleus, a nucleus which is ennervated by auditory nerve fibers that have much larger standard deviations and high dependence on frequency and decibel level. Thus, this system is an example of how the brain can use relatively sloppy and variable devices (neurons) to perform surprisingly accurate calculations. Professor Michael Reed, with colleague physiologist Joseph Blum, investigates this system with three research projects: (i) They create and investigate by machine computation a large scale mathematical model of the whole system using known physiological properties of auditory nerve fibers and recently discovered properties of octopus cells; (ii) They use mathematical analysis investigate of the improvement of the standard deviation of latency in converging networks where the target cells require coincident inputs within time windows in order to fire; (iii) They use partial differential equations to understand the special properties of dendritic information processing in octopus cells. Using mathematics and machine computation, Professor Michael Reed continues to study how groups of cells (neurons) in the auditory brainstem perform calculations that individual cells can not do by themselves. Neurons are inherently sloppy and variable devices that perform differently even under (apparently) the same conditions with the same inputs. Nevertheless, large groups of these neurons are able to perform together, reliably, highly accurate calculations. This project tries to understand this apparent paradox. The project sheds light on how and why human brains are organized the way they are and it suggests new mechanisms for the development of man-made thinking devices.
