Barry Richmond, M.D.
Dr. Richmond graduated from Harvard with a B.A. in 1965, and from Case Western Reserve Medical School with an M.D. in 1971. He did a residency in Pediatrics at University Hospitals of Cleveland from 1971-73, and a residency in Neurology at the Harvard Longwood program (Peter Bent Brigham-Beth Israel-Boston Children's Hospital) from 1973-1976. He is board certified in Pediatrics and Neurology with Special Competence in Child Neurology. He joined the Laboratory of Neurobiology in 1976 to study the neurophysiology of the visual system in awake, behaving monkeys. In 1980 he joined the Laboratory of Neuropsychology to set up a program to study how information about visual stimuli is encoded and processed by single neurons and ensembles of neurons. This work led to formation of the Section on Neural Coding and Computation in 1996.
The program started in 1980 has evolved into the current Section on Neural Coding and Computation. This group has studied the coding visual stimuli as a model for higher brain functions such as perception, memory and motivation. The earliest work looked at the influence of focal attention on neural activity. In the early 1980's we showed that the visual receptive fields of temporal lobe neurons changed their size under different attentional requirements. A long term and ongoing project to learn how information is coded by single neurons grew out of this work. Recently our understanding of neural codes has advanced enough so that we have a nearly perfect description of the complexities of neural firing, accounting for almost all of the variability within single neuronal activity. On a practical level this has led to a real-time decoding algorithm that decodes single neuronal firing accurately enough instant-by-instant to translate activity into the commands needed to drive a prosthetic device. Over the past ten years, the section has also been studying neural processes underlying normal motivation and reward expectancy. Specifically, we examined how information about the identity of a visual stimulus is transformed into information about stimulus meaning for reward expectancy, i.e., how the brain learns to use visual cues to predict the future outcome of a sequence of behavioral tasks. This work has grown tremendously over the past five years as we have been able to show that a large number of brain regions, including the ventral and dorsal striatum and rhinal, insular and cingulate cortices have signals that correspond to the number of trials necessary to obtain a reward or reach a goal. Dr. Richmond’s section has recently shown that the rhinal cortex is necessary for associating visual stimuli with predictions of reward expectancy. In an extremely promising technical advance, we have developed a molecular approach (using antisense technology) to use in monkeys. With this approach, we have shown that the D2 receptor in rhinal cortex is critical for forming these associations. This molecularly based approach provides a powerful means to connect molecular events with neural codes and behavior. This line of work will provide new insight into how normal motivation arises and how motivation might be disrupted in both intrinsic and acquired disorders, e.g., OCD and drug abuse. This approach has potential application for testing molecular therapies on a temporary and, when necessary, permanent basis.
Intersection of reward and memory in monkey rhinal cortex. Clark AM, Bouret S, Young AM, Richmond BJ. J Neurosci. 2012 May 16;32(20):6869-77. doi: 10.1523/JNEUROSCI.0887-12.2012. PMID: 22593056.
Ventromedial and orbital prefrontal neurons differentially encode internally and externally driven motivational values in monkeys. Bouret S, Richmond BJ. J Neurosci. 2010 Jun 23;30(25):8591-601. doi: 10.1523/JNEUROSCI.0049-10.2010. PMID: 20573905.
Monkeys quickly learn and generalize visual categories without lateral prefrontal cortex. Minamimoto T, Saunders RC, Richmond BJ. Neuron. 2010 May 27;66(4):501-7. doi: 10.1016/j.neuron.2010.04.010. PMID: 20510855.
Stochasticity, spikes and decoding: sufficiency and utility of order statistics. Richmond BJ. Biol Cybern. 2009 Jun;100(6):447-57. doi: 10.1007/s00422-009-0321-x. Epub 2009 Jun 11. PMID: 19517130.
Modeling the violation of reward maximization and invariance in reinforcement schedules. La Camera G, Richmond BJ. PLoS Comput Biol. 2008 Aug 8;4(8):e1000131. doi: 10.1371/journal.pcbi.1000131. PMID: 18688266.
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