Barry J. Richmond, M.D.

Chief,

Section on Neural Coding

and

Computation

Dr. Barry Richmond

Projects Currently Under Investigation

related Papers:

1. Oram, Wiener, Lestienne & Richmond; Stochastic Nature of Precisely Timed Spike Patterns in Visual System Neuronal Responses J. Neurophsiol. 81: 3021-3033. 1999.

2. Gershon, Wiener, Latham & Richmond; Coding Strategies in Monkey V1 and Inferior Temporal Cortices J. Neurophysiol. 79: 1135-1144. 1998.

3.   Wiener & Richmond; Using Response Models to Estimate Channel Capacity for Neuronal Classification of Stationary Visual Stimuli Using Temporal Coding J. Neurophysiol. 82: 2961-2875. 1999.

4. Richmond & Gawne; The Relationship Between Neuronal Codes and Cortical Organization Neuronal Ensembles Strategies for Recording and Decoding, Chapter 3. Wiley-Liss, New York. 1998.    

5. Latham, Richmond, Nelson, & Nirenberg; Intrinsic Dynamics in Neuronal Networks. I. Theory J. Neurophysiol. 83: 828-835. 2000.   

6. Latham, Richmond, Nelson, & Nirenberg; Intrinsic Dynamics in Neuronal Networks. II. Experiment J. Neurophysiol. 83: 808-827. 2000.   

7. Liu & Richmond; Response Differences in Monkey TE and Perirhinal Cortex: Stimulus Association Related to Reward Schedules J. Neurophysiol. 83: 1677-1692. 2000.

8. Richmond, Oram, & Wiener; Response Features Determining Spike Times Neural Plasticity Vol. 6, No. 4, 133-145. 2000.

9. Murray & Richmond; Role of perirhinal cortex in object perception, memory, and associatons Current Opinion in Neurobiology 11: 188-193. 2001.

10 . Liu, Murray, & Richmond; Learning motivational significance of visual cues for reward scedules requires rhinal cortex Nature Neuroscience Vol. 3, No. 12, 1307-1515. December 2000.

11. Wiener, Oram, Liu, & Richmond; Consistency of Encoding in Monkey Visual Cortex Journal of Neuroscience 21(20): 8210-8221. October 16, 2001.

12. Richmond;  Information Coding    Science   Vol. 294: 2493-2494. December 21, 2001.

13. Wiener, M.C. and Richmond, B.J.;  Model based decoding of spike trains    in press at Biosystems  

14. Munetaka Shidara and Barry J. Richmond;  Anterior Cingulate: Single Neuronal Signals Related to Degree of Reward Expectancy    Science   Vol. 296: 1709-1711. May 31, 2002.

15.Barry J. Richmond, Zheng Liu, and Munetaka Shidara; Predicting Future Rewards    Science   Vol. 301: 179-180. July 11, 2003.

16.Matthew C. Wiener and Barry J. Richmond; Decoding Spike Trains Instant by Instant Using Order Statistics and the Mixture-of-Poissons Model    The Journal of Neuroscience   Vol. 23, No. 6: 2394-2406. March 15, 2003.

17.Zheng Liu, Barry J. Richmond, Elisabeth A. Murray, Richard C. Saunders, Sara Steenrod, Barbara K. Stubblefield, Deidra M. Montague, and Edward I. Ginns; DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward;   Proceedings of the National Academy of Sciences (PNAS)  Vol. 101, No. 33: 12336-12341. August 17, 2004.

Neural Coding

of Visual Stimuli             

Neural Mechanisms of

Motivation and Reward

Neurons in different regions within the visual system carry out different aspects of visual pattern analysis needed for visual perception. This project involves discovering how and where the pieces that make up a visually perceived object are brought together and integrated. The latency before neurons start firing after pattern presentation is one important aspect of the time structure of a neuronal message. We find that the latency indicates how easily the pattern can be seen, and the intensity of firing--the response strength--is a code indicating what the pattern is. Finding that the latency is related to how easily the pattern can be perceived suggests that the features within a pattern are grouped. We construct a model to show how the neuronal signals entering the visual cortex from the lateral geniculate nucleus can be combined to make this feature grouping occur. When inferior temporal cortical neurons respond to a pattern obscured by visual noise, the time it takes for the monkey to respond to the pattern correctly is closely predicted by the time it takes for the neuronal messages to indicate which pattern is present. Our results at both ends of the visual cortical streams suggest there is a valid stimulus pattern template that gates these neuronal responses. All of these findings taken together suggest that the visual system is transmitting signals that describe messages that encode stimuli as complex, integrated objects from the earliest cortical stages, and this integration becomes more general as the signals pass to later stages of visual processing.

In our studies of motivation, we have found that monkeys work faster and with fewer errors when a cue indicates that a reward will be delivered immediately after the next correct response than when the cue indicates that additional trials will follow. Single neurons in the ventral striatum signal the rewarded trial when it follows one or more unrewarded trials, thus providing a neural signal that could reinforce complex behavior. The neuronal responses are directly related to the associative learning of the meaning of the cue in a complex behavioral task. Specifically, the neurons track the animal's chronological course of a behavioral sequence that ultimately leads to a reward. Neurons located 3-4 mm away in area TE of the temporal cortex fail to show this effect. Neurons in the perirhinal cortex are gated by the motivational state of the animal. Thus we hypothesize that dopaminergic input provides signals sensitive to long-term progress through a planned or expected series of tasks which culminate in reward. Disorders of motivation accompany many serious psychiatric and neurological disorders. Also, pharmacological agents that interfere with normal motivational processes have a devastating effect on individual behavior. This project studies the mechanisms that underlie motivational behavior with the goal of designing strategies for more effective treatments for those disorders that adversely affect normal motivation.