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Barry J Richmond

Publications and source records attributed to Barry J Richmond.

12 recordsLinked to original sources
PubMed2006

Dopamine neuronal responses in monkeys performing visually cued reward schedules.

Dopamine neurons are important for reward-related behaviours. They have been recorded during classical conditioning and operant tasks with stochastic reward delivery. However, daily behaviour, although frequently complex in the number of steps, is often very predictable. We studied the responses of 75 dopamine neurons during schedules of trials in which the events and related reward contingencies could be well-predicted, within and across trials. In this visually cued reward schedule task, a visual cue tells the monkeys exactly how many trials, 1, 2, 3, or 4, must be performed to obtain a reward. The number of errors became larger as the number of trials remaining before the reward increased. Dopamine neurons frequently responded to the cues at the beginning and end of the schedules. Approximately 75% of the first-cue responsive neurons did not distinguish among the schedules that were beginning even though the cues were different. Approximately half of the last-cue responsive neurons depended on which schedule was ending, even though the cue signalling the last trial was the same in all schedules. Thus, the responses were related to what the monkey knew about the relation between the cues and the schedules, not the identity of the cues. These neurons also frequently responded to the go signal and/or to the OK signal indicating the end of a correctly performed trial whether a reward was forthcoming or not, and to the reward itself. Thus, dopamine neurons seem to respond to behaviourally important, i.e. salient, events even when the events have been well-predicted.

Animals↗
PubMed2006

A comparison of descriptive models of a single spike train by information-geometric measure.

In examining spike trains, different models are used to describe their structure. The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes in a common coordinate system. Within such a framework, it becomes straightforward to visualize higher-order correlations of different models and thereby assess the differences between models. We apply the information-geometric measure to compare two similar but not identical models of neuronal spike trains: the inhomogeneous Markov and the mixture of Poisson models. It is shown that they differ in the second- and higher-order interaction terms. In the mixture of Poisson model, the second- and higher-order interactions are of comparable magnitude within each order, whereas in the inhomogeneous Markov model, they have alternating signs over different orders. This provides guidance about what measurements would effectively separate the two models. As newer models are proposed, they also can be compared to these models using information geometry.

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PubMed2005

Neuronal signals in the monkey basolateral amygdala during reward schedules.

The amygdala is critical for connecting emotional reactions with environmental events. We recorded neurons from the basolateral complex of two monkeys while they performed visually cued schedules of sequential color discrimination trials, with both valid and random cues. When the cues were valid, the visual cue, which was present throughout each trial, indicated how many trials remained to be successfully completed before a reward. Seventy-six percent of recorded neurons showed response selectivity, with the selectivity depending on some aspects of the current schedule. After a reward, when the monkeys knew that the upcoming cue would be valid, 88 of 246 (36%) neurons responded between schedules, seemingly anticipating the receiving information about the upcoming schedule length. When the cue appeared, 102 of 246 (41%) neurons became selective, at this point encoding information about whether the current trial was the only trial required or how many more trials are needed to obtain a reward. These cue-related responses had a median latency of 120 ms (just between the latencies in inferior temporal visual area TE and perirhinal cortex). When the monkey was releasing a touch bar to complete the trial correctly, 71 of 246 (29%) neurons responded, with responses in the rewarded trials being similar no matter which schedule was ending, thus being sensitive to the reward contingency. Finally, 39 of 246 (16%) neurons responded around the reward. We suggest that basolateral amygdala, by anticipating and then delineating the schedule and representing reward contingency, provide contextual information that is important for adjusting motivational level as a function of immediate behavior goals.

Amygdala↗
PubMed2005

Effect of visual noise on pattern recognition.

We recognize objects even when they are partially degraded by visual noise. Using monkeys performing a sequential delayed match-to-sample task, we studied the relation between the amount of visual noise (5, 10, 15, 20 or 25%) degrading the eight black and white stimuli used here, and the accuracy and speed with which matching stimuli were identified. The correct response rate decreased slightly as the amount of visual noise increased for both monkeys. Even at the 25% noise level, the correct response rate was more than 80%, indicating that the monkeys can recognize the pattern they are trying to match when the pattern is masked with visual noise. In contrast, the reaction time to the match stimulus increased substantially as the amount of visual noise increased. Thus, the monkeys appear to be trading time to maintain accuracy, suggesting that the monkeys are accumulating information and/or testing hypotheses about whether the test stimulus is likely to be a match for the sample being held in short-term memory.

Animals↗
PubMed2005

Neuronal firing in anterior cingulate neurons changes modes across trials in single states of multitrial reward schedules.

The recorded responses of single neurons often vary considerably in the numbers of spikes emitted across repeats of a single experimental condition. Because of this irregularity and for theoretical convenience the responses are often approximated using a Poisson process. However, it has been frequently pointed out that many details of the responses, including the distribution of spike counts across similar trials, are not consistent with a Poisson process, even an inhomogeneous one. Wiener and Richmond (2003, J Neurosci 23:2394-2406) showed that the spike count distributions could usually be fitted nicely by mixtures of a few (1-3) Poisson distributions, a step they regarded as a computational convenience. Now, we find that a substantial proportion (47%) of the neuronal responses from anterior cingulate cortex, which we conceptualize as part of a system related to the balance between work and reward, have responses with multimodal firing rate distributions. When these distributions are modeled as mixtures of Poisson distributions, the proportions of the different Poisson distributions are related to behavioral state, and might be related to cognitive factors. This suggests that the neurons undergo behaviorally-related mode changes.

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PubMed2004

DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward.

When schedules of several operant trials must be successfully completed to obtain a reward, monkeys quickly learn to adjust their behavioral performance by using visual cues that signal how many trials have been completed and how many remain in the current schedule. Bilateral rhinal (perirhinal and entorhinal) cortex ablations irreversibly prevent this learning. Here, we apply a recombinant DNA technique to investigate the role of dopamine D2 receptor in rhinal cortex for this type of learning. Rhinal cortex was injected with a DNA construct that significantly decreased D2 receptor ligand binding and temporarily produced the same profound learning deficit seen after ablation. However, unlike after ablation, the D2 receptor-targeted, DNA-treated monkeys recovered cue-related learning after 11-19 weeks. Injecting a DNA construct that decreased N-methyl-d-aspartate but not D2 receptor ligand binding did not interfere with learning associations between the cues and the schedules. A second D2 receptor-targeted DNA treatment administered after either recovery from a first D2 receptor-targeted DNA treatment (one monkey), after N-methyl-d-aspartate receptor-targeted DNA treatment (two monkeys), or after a vector control treatment (one monkey) also induced a learning deficit of similar duration. These results suggest that the D2 receptor in primate rhinal cortex is essential for learning to relate the visual cues to the schedules. The specificity of the receptor manipulation reported here suggests that this approach could be generalized in this or other brain pathways to relate molecular mechanisms to cognitive functions.

Animals↗
PubMed2004

Differential encoding of information about progress through multi-trial reward schedules by three groups of ventral striatal neurons.

In the course of daily activity we continually judge whether the goal sought is worth the work that must be done to obtain it. The ventral striatum is thought to play a central role in making such judgments. When reward schedules are used to investigate these judgments ventral striatum neurons show responses near the time of the cue, the bar-release, and/or the reward delivery. We evaluated the type of coding that occurs at these three time points by using codes or factorizations with: (1) two states for reward versus non-reward, (2) four states for the progress in the reward schedule, and (3) six states for all of the states of the schedule, quantified using information theory and ANOVA. For the bar-release- and reward-related responses the percent variance explained was as high for the two states code as with the six states code. The information for the four state code rose slightly but significantly for the bar-release-related neurons. For the cue-related neurons the code with six states carried more information than the simpler codes. Thus, responses at different times appear to play different roles. Responses occurring early in trials differentiate all states, i.e., the path to a reward, whereas those late in trials code knowledge of impending reward.

Action Potentials↗
PubMed2003

Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model.

In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do not. Order statistics are time-consuming to compute in the general case. We demonstrate that data from neurons in primary visual cortex are well fit by a mixture of Poisson processes; in this special case, our computations are substantially faster. In these data, spike timing contributed information beyond that available from the spike count throughout the trial. At the end of the trial, a decoder based on the mixture-of-Poissons model correctly decoded about three times as many trials as expected by chance, compared with approximately twice as many as expected by chance using the spike count only. If our model perfectly described the spike trains, and enough data were available to estimate model parameters, then our Bayesian decoder would be optimal. For four-fifths of the sets of stimulus-elicited responses, the observed spike trains were consistent with the mixture-of-Poissons model. Most of the error in estimating stimulus probabilities is attributable to not having enough data to specify the parameters of the model rather than to misspecification of the model itself.

Action Potentials↗
PubMed2002

Anterior cingulate: single neuronal signals related to degree of reward expectancy.

As monkeys perform schedules containing several trials with a visual cue indicating reward proximity, their error rates decrease as the number of remaining trials decreases, suggesting that their motivation and/or reward expectancy increases as the reward approaches. About one-third of single neurons recorded in the anterior cingulate cortex of monkeys during these reward schedules had responses that progressively changed strength with reward expectancy, an effect that disappeared when the cue was random. Alterations of this progression could be the basis for the changes from normal that are reported in anterior cingulate population activity for obsessive-compulsive disorder and drug abuse, conditions characterized by disturbances in reward expectancy.

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PubMedDate not supplied

Model based decoding of spike trains.

Reliably decoding neuronal responses requires knowing what aspects of neuronal responses are stimulus related, and which aspects act as noise. Recent work shows that spike trains can be viewed as stochastic samples from the rate variation function, as estimated by the time dependent spike density function (or normalized peristimulus time histogram). Such spike trains are exactly described by order statistics, and can be decoded millisecond-by-millisecond by iterative application of order statistics.

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