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B J Richmond

Publications and source records attributed to B J Richmond.

At least 19 recordsLinked to original sources
PubMed2000

Intrinsic dynamics in neuronal networks. I. Theory.

Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.

Action Potentials↗
PubMed2000

Intrinsic dynamics in neuronal networks. II. experiment.

Neurons in many regions of the mammalian CNS remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How these firing patterns are maintained in the presence of powerful recurrent excitation, and how networks switch between them, is not well understood. In the previous paper, we addressed these issues theoretically; in this paper we address them experimentally. We found in both studies that a key parameter in controlling firing patterns is the fraction of endogenously active cells. The theoretical analysis indicated that steady firing rates are possible only when the fraction of endogenously active cells is above some threshold, that there is a transition to bursting when it falls below that threshold, and that networks becomes silent when the fraction drops to zero. Experimentally, we found that all steadily firing cultures contain endogenously active cells, and that reducing the fraction of such cells in steadily firing cultures causes a transition to bursting. The latter finding implies indirectly that the elimination of endogenously active cells would cause a permanent drop to zero firing rate. The experiments described here thus corroborate the theoretical analysis.

Action Potentials↗
PubMed2000

Response differences in monkey TE and perirhinal cortex: stimulus association related to reward schedules.

Anatomic and behavioral evidence shows that TE and perirhinal cortices are two directly connected but distinct inferior temporal areas. Despite this distinctness, physiological properties of neurons in these two areas generally have been similar with neurons in both areas showing selectivity for complex visual patterns and showing response modulations related to behavioral context in the sequential delayed match-to-sample (DMS) trials, attention, and stimulus familiarity. Here we identify physiological differences in the neuronal activity of these two areas. We recorded single neurons from area TE and perirhinal cortex while the monkeys performed a simple behavioral task using randomly interleaved visually cued reward schedules of one, two, or three DMS trials. The monkeys used the cue's relation to the reward schedule (indicated by the brightness) to adjust their behavioral performance. They performed most quickly and most accurately in trials in which reward was immediately forthcoming and progressively less well as more intermediate trials remained. Thus the monkeys appeared more motivated as they progressed through the trial schedule. Neurons in both TE and perirhinal cortex responded to both the visual cues related to the reward schedules and the stimulus patterns used in the DMS trials. As expected, neurons in both areas showed response selectivity to the DMS patterns, and significant, but small, modulations related to the behavioral context in the DMS trial. However, TE and perirhinal neurons showed strikingly different response properties. The latency distribution of perirhinal responses was centered 66 ms later than the distribution of TE responses, a larger difference than the 10-15 ms usually found in sequentially connected visual cortical areas. In TE, cue-related responses were related to the cue's brightness. In perirhinal cortex, cue-related responses were related to the trial schedules independently of the cue's brightness. For example, some perirhinal neurons responded in the first trial of any reward schedule including the one trial schedule, whereas other neurons failed to respond in the first trial but respond in the last trial of any schedule. The majority of perirhinal neurons had more complicated relations to the schedule. The cue-related activity of TE neurons is interpreted most parsimoniously as a response to the stimulus brightness, whereas the cue-related activity of perirhinal neurons is interpreted most parsimoniously as carrying associative information about the animal's progress through the reward schedule. Perirhinal cortex may be part of a system gauging the relation between work schedules and rewards.

Animals↗
PubMed1999

I see a face-a happy face.

Information analysis shows that face-selective neurons in inferior temporal cortex encode different stimulus attributes early and late in their response to the same image.

Facial Expression↗
PubMed1999

Stochastic nature of precisely timed spike patterns in visual system neuronal responses.

It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the potential for carrying information that would not be available from other measures such as spike count or latency. We examined exactly timed (1-ms precision) triplets and quadruplets of spikes in the stimulus-elicited responses of lateral geniculate nucleus (LGN) and primary visual cortex (V1) neurons of the awake fixating rhesus monkey. Large numbers of these precisely timed spike patterns were found. Information theoretical analysis showed that the precisely timed spike patterns carried only information already available from spike count, suggesting that the number of precisely timed spike patterns was related to firing rate. We therefore examined statistical models relating precisely timed spike patterns to response strength. Previous statistical models use observed properties of neuronal responses such as the peristimulus time histogram, interspike interval, and/or spike count distributions to constrain the parameters of the model. We examined a new stochastic model, which unlike previous models included all three of these constraints and unlike previous models predicted the numbers and types of observed precisely timed spike patterns. This shows that the precise temporal structures of stimulus-elicited responses in LGN and V1 can occur by chance. We show that any deviation of the spike count distribution, no matter how small, from a Poisson distribution necessarily changes the number of precisely timed spike patterns expected in neural responses. Overall the results indicate that the fine temporal structure of responses can only be interpreted once all the coarse temporal statistics of neural responses have been taken into account.

Action Potentials↗
PubMed1999

Using response models to estimate channel capacity for neuronal classification of stationary visual stimuli using temporal coding.

Both spike count and temporal modulation are known to carry information about which of a set of stimuli elicited a response; but how much information temporal modulation adds remains a subject of debate. This question usually is addressed by examining the results of a particular experiment that depend on the specific stimuli used. Developing a response model allows us to ask how much more information is carried by the best use of response strength and temporal modulation together (that is, the channel capacity using a code incorporating both) than by the best use of spike count alone (the channel capacity using the spike count code). This replaces dependence on a particular data set with dependence on the accuracy of the model. The model is constructed by finding statistical rules obeyed by all the observed responses and assuming that responses to stimuli not presented in our experiments obey the same rules. We assume that all responses within the observed dynamic range, even if not elicited by a stimulus in our experiment, could be elicited by some stimulus. The model used here is based on principal component analysis and includes both response strength and a coarse (+/-10 ms) representation of temporal modulation. Temporal modulation at finer time scales carries little information about the identity of stationary visual stimuli (although it may carry information about stimulus motion or change), and we present evidence that, given its variability, it should not be expected to do so. The model makes use of a linear relation between the logarithms of mean and variance of responses, similar to the widely seen relation between mean and variance of spike count. Responses are modeled using truncated Gaussian distributions. The amount of stimulus-related information carried by spike count in our data are 0.35 and 0.31 bits in primary visual and inferior temporal cortices, respectively, rising to 0.52 and 0.37 bits for the two-principal-component code. The response model estimates that the channel capacity is 1.1 and 1.4 bits, respectively, using the spike count only, rising to 2.0 and 2.2 bits using two principal components. Thus using this representation of temporal modulation is nearly equivalent to adding a second independent cell using the spike count code. This is much more than estimated using transmitted information but far less than would be expected if all degrees of freedom provided by the individual spike times carried independent information.

Algorithms↗
PubMed1999

Response features determining spike times.

Interpreting messages encoded in single neuronal responses requires knowing which features of the responses carry information. That the number of spikes is an important part of the code has long been obvious. In recent years, it has been shown that modulation of the firing rate with about 25 ms precision carries information that is not available from the total number of spikes across the whole response. It has been proposed that patterns of exactly timed (1 ms precision) spikes, such as repeating triplets or quadruplets, might carry information that is not available from knowing about spike count and rate modulation. A model using the spike count distribution, the low-pass filtered PSTH (bandwidth below 30 Hz), and, to a small degree, the interspike interval distribution predicts the numbers and types of exactly-timed triplets and quadruplets that are indistinguishable from those found in the data. From this it can be concluded that the coarse (< 30 Hz) sequential correlation structure over time gives rise to the exactly timed patterns present in the recorded spike trains. Because the coarse temporal structure predicts the fine temporal structure, the information carried by the fine temporal structure must be completely redundant with that carried by the coarse structure. Thus, the existence of precisely timed spike patterns carrying stimulus-related information does not imply control of spike timing at precise time scales.

Animals↗
PubMed1999

Selenium-based digital radiography versus conventional film-screen radiography of the hands and feet: a subjective comparison.

OBJECTIVE: The purpose of this study was to subjectively compare the visibility of normal anatomy of the hands and feet using selenium-based digital radiography versus conventional film-screen (100-speed) radiography. SUBJECTS AND METHODS: Digital and film-screen images of the hands and feet of 24 patients were obtained without an antiscatter grid using identical X-ray exposure. Each pair of images was evaluated independently by five experienced radiologists for visibility of normal anatomy using a six-point rating scale. Soft tissues, cortical bone, and trabeculae were evaluated. For each observer, "equivalence" was defined as a mean difference in image quality of less than 1 unit on the 0-5 scale used in the study. Paired t tests were also performed to determine whether the average visibility rating of one technique was statistically superior to that of the other at a .05 level of significance for each observer and at each anatomic landmark. RESULTS: In all categories, selenium-based digital images were rated equivalent to film-screen images by the five observers. Using the sum of the nine landmarks, four of the five observers rated the quality of selenium-based digital images superior to that of film-screen images. CONCLUSION: Subjective visibility of normal anatomy of the hands and feet using selenium-based digital radiography was similar to that achieved using conventional film-screen radiography.

Aged↗
PubMed1998

Neuronal signals in the monkey ventral striatum related to progress through a predictable series of trials.

Single neurons in the ventral striatum of primates carry signals that are related to reward and motivation. When monkeys performed a task requiring one to three bar release trials to be completed successfully before a reward was given, they seemed more motivated as the rewarded trials approached; they responded more quickly and accurately. When the monkeys were cued as to the progress of the schedule, 89 out of 150 ventral striatal neurons responded in at least one part of the task: (1) at the onset of the visual cue, (2) near the time of bar release, and/or (3) near the time of reward delivery. When the cue signaled progress through the schedule, the neuronal activity was related to the progress through the schedule. For example, one large group of these neurons responded in the first trial of every schedule, another large group responded in trials other than the first of a schedule, and a third large group responded in the first trial of schedules longer than one. Thus, these neurons coded the state of the cue, i.e., the neurons carried the information about how the monkey was progressing through the task. The differential activity disappeared on the first trial after randomizing the relation of the cue to the schedule. Considering the anatomical loop structure that includes ventral striatum and prefrontal cortex, we suggest that the ventral striatum might be part of a circuit that supports keeping track of progress through learned behavioral sequences that, when successfully completed, lead to reward.

Action Potentials↗
PubMed1998

Coding strategies in monkey V1 and inferior temporal cortices.

We would like to know whether the statistics of neuronal responses vary across cortical areas. We examined stimulus-elicited spike count response distributions in V1 and inferior temporal (IT) cortices of awake monkeys. In both areas, the distribution of spike counts for each stimulus was well described by a Gaussian distribution, with the log of the variance in the spike count linearly related to the log of the mean spike count. Two significant differences in response characteristics were found: both the range of spike counts and the slope of the log(variance) versus log(mean) regression were larger in V1 than in IT. However, neurons in the two areas transmitted approximately the same amount of information about the stimuli and had about the same channel capacity (the maximum possible transmitted information given noise in the responses). These results suggest that neurons in V1 use more variable signals over a larger dynamic range than IT neurons, which use less variable signals over a smaller dynamic range. The two coding strategies are approximately as effective in transmitting information.

Animals↗
PubMed1998

Imaging modalities for study of the distal ulnar region.

Judicious use of diagnostic imaging maximizes the diagnostic capabilities of the surgeon treating the distal radio-ulnar joint (DRUJ). A good clinical history and clinical examination are necessary to direct the selection of appropriate imaging studies. Plain radiographs are almost always the first imaging examination. More advanced imaging techniques are costly and may provide only limited information. This article discusses imaging modalities useful for assessment of the DRUJ and the area around it.

Arthralgia↗
PubMed1997

Neuronal codes: reading them and learning how their structure influences network organization.

Our investigations of the primate visual system show that neuronal responses carry information in a multi-dimensional code that is superimposed onto the response envelope in a slow time varying fashion. The precision of timing is 30 ms or more. In primary visual cortex response latency and response strength are largely independent, with latency more closely coding contrast or visibility and strength more closely coding stimulus orientation, or perhaps shape. Adjacent neurons in both V1 and inferior temporal cortex share only about 10% of their stimulus-related information, which we demonstrate to be consistent with the idea that cortical layers were organized to minimize information loss.

Animals↗
PubMed1997

Insensitivity of V1 complex cell responses to small shifts in the retinal image of complex patterns.

An important role for neurons in the early visual system is to convey information about the structure of visual stimuli. However, neuronal responses show substantial variation across presentations of the same stimulus. In awake monkeys, it has been assumed that a great deal of this variation is related to the scatter in eye position (inducing scatter in the retinal position of the stimulus). Here we investigate the implied consequence of this assumption, i.e., that the scatter variation in eye position degrades the decodability of the neural response. We recorded from 50 complex cells in primary visual cortex of fixating monkeys while different complex stimuli were presented. Three types of retinal shifts were considered: natural scatter in the fixation, systematic fixation point shift, and systematic stimulus position shift. The stimulus pattern accounts for >50% of the response variance, always six times that accounted for by the scatter in eye position during fixation. The retinal location of a stimulus had to be shifted by 10-12 min of arc, an amount almost two times larger than the smallest picture element, before the responses changed systematically. Nonetheless, changes of the stimulus at the single pixel level often gave rise to discriminable responses. Thus complex cells convey information about the spatial structure of a stimulus, independent of rigid stimulus displacements on the order of the receptive field size or smaller.

Animals↗
PubMed1996

Neural signals in the monkey ventral striatum related to motivation for juice and cocaine rewards.

1. The results of neuropsychological, neuropharmacological, and neurophysiological experiments have implicated the ventral striatum in reward-related processes. We designed a task to allow us to separate the effects of sensory, motor, and internal signals so that we could study the correlation between the activity of neurons in the ventral striatum and different motivational states. In this task, a visual stimulus was used to cue the monkeys as to their progress toward earning a reward. The monkeys performed more quickly and with fewer mistakes in the rewarded trials. After analyzing the behavioral results from three monkeys, we recorded from 143 neurons from two of the monkeys while they performed the task with either juice or cocaine reward. 2. In this task the monkey was required to release its grip on a bar when a small visual response cue changed colors from red (the wait signal) to green (the go signal). The duration of the wait signal was varied randomly. The cue became blue whenever the monkey successfully responded to the go signal within 1 s of its appearance. A reward was delivered after the monkey successfully completed one, two, or three trials. The schedules were randomly interleaved. A second visual stimulus that progressively brightened or dimmed signaled to the monkeys their progress toward earning a reward. This discriminative cue allowed the monkeys to judge the proportion of work remaining in the current ratio schedule of reinforcement. Data were collected from three monkeys while they performed this task. 3. The average reaction times became faster and error rates declined as the monkeys progressed toward completing the current schedule of reinforcement and thereby earning a reward, whereas the modal reaction time did not change. As the duration of the wait period before the go signal increased, the monkeys reacted more quickly but their error rates scarcely changed. From these results we infer that the effects of motivation and motor readiness in this task are generated by separate mechanisms rather than by a single mechanism subserving generalized arousal. 4. The activity of 138 ventral striatal neurons was sampled in two monkeys while they performed the task to earn juice reward. We saw tonic changes in activity throughout the trials, and we saw phasic activity following the reward. The activity of these neurons was markedly different during juice-rewarded trials than during correctly performed trials when no reward was forthcoming (or expected). The responses also were weakly, but significantly, related to the proximity of the reward in the schedules requiring more than one trial. 5. The monkeys worked to obtain intravenous cocaine while we recorded 62 neurons. For 57 of the neurons, we recorded activity while the monkeys worked in blocks of trials during which they self-administered cocaine after blocks during which they worked for juice. Although fewer neurons responded to cocaine than to juice reward (19 vs. 33%), this difference was not significant. The neuronal response properties to cocaine and juice rewards were independent; that is, the responses when one was the reward one failed to predict the response when the other was the reward. In addition, the neuronal activity lost most of its selectivity for rewarded trials, i.e, the activity did not distinguish nearly as well between cocaine and sham rewards as between juice and sham rewards. 6. Our results show that mechanisms by which cocaine acts do not appear to be the same as the ones activated when the monkeys were presented with an oral juice reward. This finding raises the intriguing possibility that the effects of cocaine could be reduced selectively without blocking the effects of many natural rewards.

Animals↗
PubMed1996

Latency: another potential code for feature binding in striate cortex.

1. We recorded the responses of 37 striate cortical complex cells in fixating monkeys while presenting a set of oriented stimuli that varied in contrast. 2. The two response parameters of strength and latency can be interpreted as a code: the strength defines the stimulus form (here the orientation), and the latency is more a function of the stimulus contrast. 3. Synchronization based on latency could make a strong contribution to the process of organizing the neural responses to different objects, i.e., binding.

Animals↗