Heads of Laboratories
Laboratory of Biophysics
Everything we see is the result of light-induced patterns of electrical activity in the nerve networks of the retina and brain. What we see, however, is processed into an intelligible form by complex transformations within these networks. Mr. Knight works to develop a description of these networks in mathematical equations that will allow scientists to predict how the system will respond to specific visual patterns.
Mr. Knight and his colleagues study the visual part of the central nervous system, where they apply technology and theoretical means refined in their own laboratory in an effort to understand how the nervous system processes input information. Mr. Knight is specifically interested in the broad multicellular interactions among biophysical processes, which individually lie at the subcellular level. His laboratory’s efforts demand the conjoined development of experimental procedures that induce and record detailed neural responses and theoretical tools, including dynamical equations and computer simulation, that may then describe those neural responses quantitatively. Because the visual sense provides unique opportunities for highly structured input, their efforts focus on the visual part of the central nervous system.
Vision occurs when a moving pattern of colored light arrives from the external visual world and induces dynamical patterns of electrical activity in a sequence of neural processing networks that start with the retina, which is specialized brain tissue, and continue into the brain. At each step, profound signal transformations occur that ultimately reduce the input to a form useful for action. Mr. Knight and his colleagues study that process with computer-generated stimuli, including modified natural-scene movies, designed by theoretical considerations that facilitate the interpretation of response features in terms of the responding system’s predictive dynamical laws. Their data include single-cell recordings from identified nerve cells of anesthetized animals.
The lab’s emphasis has been on the retina, the primary visual cortex, and the lateral geniculate nucleus, which is a processing network between the retina and the cortex. Members of Mr. Knight’s laboratory have been some of the principal contributors to our emerging understanding of the dynamics of the cat retina, which shows numerous dynamical features that prove typical of many vertebrates.
Recent detailed analysis of responses of retinal ganglion cells to time-dependent naturalistic stimuli reveals that they may be classified within a special subset of neuron designs that might be descriptively called “faithful copy neurons.” A population of such neurons produces an aggregate firing rate that far better mimics its neural input than would different choices of neuron design. The way a network of such neurons behaves depends critically on the nature of interconnections and only insensitively on variations in the dynamics of the individual neurons. The generality of this feature and its implications for network design are currently under study.
Recently, Mr. Knight’s associates have been able to record simultaneously from numerous cells in the lateral geniculate nucleus. The responses of these cells naturally classify them into several response categories. By the creation of a new data analysis technique, it has proven possible to quantitatively measure the rate of information transfer through a group of such cells and also to measure the degree to which information transmitted by different cell groups is independent or redundant.
B.A. in physics and mathematics, 1952
Staff Member, 1955–1961
Los Alamos National Laboratory
Associate Professor with Tenure, 1973–1988
The Rockefeller University
Crumiller, M. et al. The measurement of information transmitted by a neural population: Promises and challenges. Entropy 15, 3507–3527 (2013).
Crumiller, M. et al. Estimating the amount of information conveyed by a population of neurons. Front Neurosci. 5, 90 (2011).
Sirovich, L. and Knight, B. Spiking neurons and the first passage problem. Neural Comput. 23, 1675–1703 (2011).
Knight, B.W. Hartline-Ratliff model. Scholarpedia J. 6, 2121 (2011).
Knight, B.W. Dynamics of encoding in neuron populations: Some general mathematical features. Neural Comput. 12, 473–518 (2000).