Abstract
We seek to gain better understanding of sensory information processing in physical systems both natural and engineered. For an information processing system the statistics of the input, the details of the algorithm, and the task requirements determine the minimum information transmission rate, R (bits/sec). The properties of the physical substrate, such as bandwidth, noise and constraints on the signal value, determine the channel capacity, C (bits/sec). These physical properties must be such that C > R for reliable performance. Furthermore, any implementation involves tradeoffs among costs such as power, speed, accuracy, and area. We seek better understanding of the compromise between performance and cost.To date there exist several measurements of the information transmission rates for spiking and non-spiking neurons. These measurements must be supported by the physical properties of the communication channel, and we explore this relationship for one of these systems, the blowfly retina. We construct a communication channel model that incorporates all physical transformations from photons at the photoreceptor to the membrane voltage of the large monopolar cell in the laminar layer.In this talk I will begin with a brief historical review of previous work that employs information theoretic ideas to analyze neural information processing. I will then describe the components of the early vision system in the blowfly retina. From biophysical data available in the literature, we determine bandwidth limitations and noise contributions at the different stages and calculate the Shannon capacity of the system. We compare our model with empirical information capacities derived from measurements on the system. I will conclude my talk by briefly discussing future work aimed at determining the energy efficiency of the system as given by bit-energy, the ratio of information rate to power dissipated.References:P. Abshire and A.G. Andreou, “Relating Information Capacity to a Biophysical Model of the Blowfly Retina”, Electrical and Computer Engineering, Technical Report 13-1998.A.G. Andreou and P.M. Furth, “An Information Theoretic Framework for Comparing the Bit-Energy of Signal Representations at the Circuit Level,” Chapter 17, Low-Voltage/Low-Power Integrated Circuits and Systems, edited by Edgar Sanchez-Sinencio and Andreas G. Andreou, IEEE Press, 1998.Work supported by a DARPA/ONR Multidisciplinary University Research Initiative with Boston University on Automated Sensing and Vision Systems N00014-95-1-0409.