Consistency of encoding in monkey visual cortex

Visual Categorization with Random Projection

Humans learn categories of complex objects quickly and from a few examples. Random projection has been suggested as a means to learn and categorize efficiently. We investigate how random projection affects categorization by humans and by very simple neural networks on the same stimuli and categorization tasks, and how this relates to the robustness of categories. We find that (1) drastic reduction in stimulus complexity via random projection does not degrade performance in categorization tasks by either humans or simple neural networks, (2) human accuracy and neural network accuracy are remarkably correlated, even at the level of individual stimuli, and (3) the performance of both is strongly indicated by a natural notion of category robustness.

Behavioral demand modulates object category representation in the inferior temporal cortex

Visual object categorization is a critical task in our daily life. Many studies have explored category representation in the inferior temporal (IT) cortex at the level of single neurons and population. However, it is not clear how behavioral demands modulate this category representation. Here, we recorded from the IT single neurons in monkeys performing two different tasks with identical visual stimuli: passive fixation and body/object categorization. We found that category selectivity of the IT neurons was improved in the categorization compared with the passive task where reward was not contingent on image category. The category improvement was the result of larger rate enhancement for the preferred category and smaller response variability for both preferred and nonpreferred categories. These specific modulations in the responses of IT category neurons enhanced signal-to-noise ratio of the neural responses to discriminate better between the preferred and nonpreferred categories. Our results provide new insight into the adaptable category representation in the IT cortex, which depends on behavioral demands.

Weaker error signals do not reduce the effectiveness of post-error adjustments: comparing error processing in young and middle-aged adults

In this study we investigated age-related differences in error processing, comparing performance measures and physiological indicators of error processing of middle-aged (41-59years) and young (18-31years) adults using a version of the Eriksen flanker task. Although middle-aged participants were overall slower, both groups showed a comparable decrease in reaction time on error trials as well as slower and more accurate post-error performance. Despite the preserved error speeding and post-error slowing effects, we found an amplitude reduction in the Ne/ERN, contradicting the existence of a direct relationship between the amplitude of this component and post-error adjustments. This was further supported by the lack of significant correlations between the single-trial Ne/ERN amplitude and error-related reaction times. The single-trial Ne/ERN distribution showed a reduced variance for middle-aged compared to young participants, suggesting that weaker overall error signals rather than lapses in error detection are responsible for the observed Ne/ERN amplitude reductions. However, we argue that the signal still reached the necessary threshold to trigger normal post-error adjustments. Finally, the early Pe showed a reduction in amplitude and an increase in latency for middle-aged compared to young adults. Together, the findings suggest clear signs of a physiological decline in error processing at an earlier age than previously known, but these changes do not yet affect implementation of adaptive behavioral changes in middle-aged participants.

Information processing speed, neural efficiency, and working memory performance in multiple sclerosis: differential relationships with structural magnetic resonance imaging

Multiple sclerosis (MS), a central nervous system (CNS) neurodegenerative disorder, involves lesions of both white and gray matter and reported cognitive impairments that include processing speed (PS), executive function, and working memory (WM). This study closely examined the specifics of these cognitive deficits and their relationship to structural brain damage. A visual n-back task with 3 WM load conditions was used to assess WM performance (task accuracy), PS (reaction time, RT), and a novel measure of processing efficiency (standard deviation of RT, RTSD) in MS patients and controls. These behavioral measures were related to quantitative magnetic resonance imaging (MRI) measures of white and gray matter integrity. Even when MS patients performed as well as controls, as seen for low WM load (0-back), they responded more slowly and were less efficient in their speed of responding. Accuracy findings indicated that the correct match trials were superior to correct nonmatch trials at differentiating MS patients from controls. Further, decreased accuracy during the highest WM load condition was associated with global damage that included both gray and white matter atrophy, while slowed PS and particularly processing inefficiency were associated primarily with white matter atrophy in MS. Importantly, relationships between PS, processing efficiency, performance accuracy, and structural MRI measures were seen only during the highest WM load condition, the condition that required the most executive control. These findings suggest that the MRI/behavioral relationships that were present exclusively during the 2-back condition may reflect connectivity involving frontal cortical systems, the site for executive control.

Visual stimulation decorrelates neuronal activity

The accuracy of neuronal encoding depends on the response statistics of individual neurons and the correlation of the activity between different neurons. Here, the dynamics of the neuronal response statistics in the anterior superior temporal sulcus of the macaque monkey is described. A transient reduction in the normalized trial-by-trial variability and decorrelation of the responses with both the activity of other neurons and previous activity of the same neuron are found at response onset. The variability of neuronal activity and its correlation structure return to the levels observed in the resting state 50-100 ms after response onset, except for marked increases in the signal correlation between neurons. The transient changes in the response statistics are seen even if there is little or no stimulus-elicited activity, indicating the effect is due to network properties rather than to activity changes per se. Modeling also indicates that the observed variations in response variability and correlation structure of the neuronal activity over time cannot be attributed to changes in firing rate. However, a reset of the underlying spike-generating process, possibly due to the driving input changing from recurrent to feedforward inputs, captures most of the observed changes. The nonstationarity indicated by the changes in correlation structure around response onset increases coding efficiency: compared with the mutual information calculated without regard to the transitory changes, the decorrelation increases the information conveyed by the initial response of modeled neuronal pairs by ≤ 4% and suggests that an integration time of as little as 50 ms is sufficient to extract 95% the available information during the initial response period.

The local and non-local components of the local field potential in awake primate visual cortex

The Local Field Potential (LFP) is the analog signal recorded from a microelectrode inserted into cortex, typically in the frequency band of approximately 1 to 200 Hz. Here visual stimuli were flashed on in the receptive fields of primary visual cortical neurons in awake behaving macaques, and both isolated single units (neurons) and the LFP signal were recorded from the same unipolar microelectrode. The fall-off of single unit activity as a visual stimulus was moved from near the center to near the edge of the receptive field paralleled the fall-off of the stimulus-locked (evoked) LFP response. This suggests that the evoked LFP strongly reflects local neuronal activity. However, the evoked LFP could be significant even when the visual stimulus was completely outside the receptive field and the single unit response had fallen to zero, although this phenomenon was variable. Some of the non-local components of the LFP may be related to the slow distributed, or non-retinotopic, LFP signal previously observed in anesthetized animals. The induced (not time-locked to stimulus onset) component of the LFP showed significant increases only for stimuli within the receptive field of the single units. While the LFP primarily reflects local neuronal activity, it can also reflect neuronal activity at more distant sites, although these non-local components are typically more variable, slower, and weaker than the local components.

Adaptive integration in the visual cortex by depressing recurrent cortical circuits

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

The response of cortical neurons to in vivo-like input current: theory and experiment : I. Noisy inputs with stationary statistics

The study of several aspects of the collective dynamics of interacting neurons can be highly simplified if one assumes that the statistics of the synaptic input is the same for a large population of similarly behaving neurons (mean field approach). In particular, under such an assumption, it is possible to determine and study all the equilibrium points of the network dynamics when the neuronal response to noisy, in vivo-like, synaptic currents is known. The response function can be computed analytically for simple integrate-and-fire neuron models and it can be measured directly in experiments in vitro. Here we review theoretical and experimental results about the neural response to noisy inputs with stationary statistics. These response functions are important to characterize the collective neural dynamics that are proposed to be the neural substrate of working memory, decision making and other cognitive functions. Applications to the case of time-varying inputs are reviewed in a companion paper (Giugliano et al. in Biol Cybern, 2008). We conclude that modified integrate-and-fire neuron models are good enough to reproduce faithfully many of the relevant dynamical aspects of the neuronal response measured in experiments on real neurons in vitro.

Stimulus selection via differential response latencies in visual cortical area V4

Any given region of the cerebral cortex gets multiple inputs, and how these inputs are combined or selected is a key component of cortical function. Experiments in brain slices or other reduced preparations have shown that excitatory inputs to cortex produce a delayed feed-forward inhibition, which suggests that the relative timing of inputs at the scale of tens of milliseconds is crucial to cortical operation. Other mechanisms, such as synaptic depression and feedback inhibition, have also been shown to produce strong effects on this timescale. Thus, the relative timing of inputs should be fundamental in determining how a given region of cortex selects or combines its inputs. A rhesus monkey (Macaca mulatta) was trained to fixate on a spot of light for juice reward. Isolated single units in visual cortical area V4 were recorded using standard microelectrode techniques. Two visual stimuli were positioned such that each alone elicited a strong response. The stimuli were presented both separately and in combination, and their contrast and relative onset timing were varied. In general, the response of each neuron to two stimuli was locked to the response to that single stimulus that produced the shortest latency. A partial exception was that the responses to low-contrast stimuli were often less effective at suppressing later-arriving responses to high-contrast stimuli. The presentation of two stimuli in the receptive field of a visual cortical neuron is proposed as a model system for how changes in the relative timing of inputs affect cortical function in the intact system.

Neural selectivity in anterior inferotemporal cortex for morphed photographic images during behavioral classification or fixation

Anterior inferotemporal cortex (aIT) contributes to the ability to discriminate and classify complex images. To determine whether and what proportion of single neurons in aIT cortex can yield enough information to classify complex images, we recorded from aIT neurons during the presentation of morphed photographic images in sessions in which monkeys classified images in a two alternative forced-choice--delayed-match-to-sample (2AFC-DMS) task or in sessions in which they performed a fixation task. The sample stimuli were chosen from a sequence in which one image was gradually morphed into another in a pair, while the original pair of images served as choices. Responses of many individual neurons in aIT cortex during the behavioral classification of the images, decoded using an ideal observer analysis, were sufficiently selective to account for the observed behavioral classification of the images. The responses of a separate population of neurons in aIT cortex recorded in subsequent sessions while the monkeys viewed the same images, were less selective than neural responses measured during sessions in which the 2AFC-DMS task was performed. Our findings show that many neurons in aIT could provide sensory information sufficient for the classification of images when a 2AFC-DMS task was performed.

Medial temporal lobe activity at recognition increases with the duration of mnemonic delay during an object working memory task

Object working memory (WM) engages a disseminated neural network, although the extent to which the length of time that data is held in WM influences regional activity within this network is unclear. We used functional magnetic resonance imaging to study a delayed matching to sample task in 14 healthy subjects, manipulating the duration of mnemonic delay. Across all lengths of delay, successful recognition was associated with the bilateral engagement of the inferior and middle frontal gyri and insula, the medial and inferior temporal, dorsal anterior cingulate and the posterior parietal cortices. As the length of time that data was held in WM increased, activation at recognition increased in the medial temporal, medial occipito-temporal, anterior cingulate and posterior parietal cortices. These results confirm the components of an object WM network required for successful recognition, and suggest that parts of this network, including the medial temporal cortex, are sensitive to the duration of mnemonic delay.

Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons

Neural dynamic processes correlated over several time scales are found in vivo, in stimulus-evoked as well as spontaneous activity, and are thought to affect the way sensory stimulation is processed. Despite their potential computational consequences, a systematic description of the presence of multiple time scales in single cortical neurons is lacking. In this study, we injected fast spiking and pyramidal (PYR) neurons in vitro with long-lasting episodes of step-like and noisy, in-vivo-like current. Several processes shaped the time course of the instantaneous spike frequency, which could be reduced to a small number (1-4) of phenomenological mechanisms, either reducing (adapting) or increasing (facilitating) the neuron's firing rate over time. The different adaptation/facilitation processes cover a wide range of time scales, ranging from initial adaptation (<10 ms, PYR neurons only), to fast adaptation (<300 ms), early facilitation (0.5-1 s, PYR only), and slow (or late) adaptation (order of seconds). These processes are characterized by broad distributions of their magnitudes and time constants across cells, showing that multiple time scales are at play in cortical neurons, even in response to stationary stimuli and in the presence of input fluctuations. These processes might be part of a cascade of processes responsible for the power-law behavior of adaptation observed in several preparations, and may have far-reaching computational consequences that have been recently described.

The influence of cortical feature maps on the encoding of the orientation of a short line

The inhomogeneous distribution of the receptive fields of cortical neurons influences the cortical representation of the orientation of short lines seen in visual images. We construct a model of the response of populations of neurons in the human primary visual cortex by combining realistic response properties of individual neurons and cortical maps of orientation and location preferences. The encoding error, which characterizes the difference between the parameters of a visual stimulus and their cortical representation, is calculated using Fisher information as the square root of the variance of a statistically efficient estimator. The error of encoding orientation varies considerably with the location and orientation of the short line stimulus as modulated by the underlying orientation preference map. The average encoding error depends only weakly on the structure of the orientation preference map and is much smaller than the human error of estimating orientation measured psychophysically. From this comparison we conclude that the actual mechanism of orientation perception does not make efficient use of all the information available in the neuronal responses and that it is the decoding of visual information from neuronal responses that limits psychophysical performance.

Does a Bayesian model of V1 contrast coding offer a neurophysiological account of human contrast discrimination?

The dipper effect for contrast discrimination provides strong evidence that the underlying neural response is accelerating at low contrasts and saturating at high contrasts. The contrast-response functions of V1 neurons do have this sigmoidal shape, but individual neurons do not generally have a dynamic range wide enough to account for the dipper effect. This paper presents a Bayesian model of neurons in monkey V1, whose contrast-response function is described by a modified Naka-Rushton with multiplicative noise. It is shown that a model of groups of twelve or more neurons gives a reasonable explanation of the psychophysical data of two observers, but there is a large systematic error which is apparently due to the shape of the distribution of the monkey's sensitivity parameter, c50. A further model provides a better fit to the data by sacrificing strict adherence to V1 neuronal parameters and, instead using an arbitrary bimodal c50 distribution, perhaps reflecting differences between M- and P-cells.

Integrating neuronal coding into cognitive models: predicting reaction time distributions

Neurophysiological studies have examined many aspects of neuronal activity in terms of neuronal codes and postulated roles for these codes in brain processing. There has been relatively little work, however, examining the relationship between different neuronal codes and the behavioural phenomena associated with cognitive processes. Here, predictions about reaction time distributions derived from an accumulator model incorporating known neurophysiological data in temporal lobe visual areas of the macaque are examined. Results from human experimental studies examining the effects of changing stimulus orientation, size and contrast are consistent with the model, including qualitatively different changes in reaction time distributions with different stimulus manipulations. The different changes in reaction time distributions depend on whether the image manipulation changes neuronal response latency or magnitude and can be related to parallel or serial cognitive processes respectively. The results indicate that neuronal coding can be productively incorporated into computational models to provide mechanistic accounts of behavioural results related to cognitive phenomena.

Accuracy of identification of grating contrast by human observers: Bayesian models of V1 contrast processing show correspondence between discrimination and identification performance

This paper presents the results of a contrast identification study, where accuracy in identification is quantified as mutual information between stimulus contrast and observer's response. The stimulus was a set of 2-8 gratings, spanning the range of visible contrasts. Gratings from the set were presented individually for 500 ms, and the observer had to respond by giving the number label corresponding to the contrast of the grating presented. Mutual information increased with set size up to a maximum of around 2.35 bits, i.e., only 5 clearly identifiable contrasts. Set sizes greater than 5 showed a plateau or decline in performance. These data were well fit by Bayesian models of V1 contrast coding, with the parameters obtained by fitting the contrast discrimination results of Chirimuuta and Tolhurst [Chirimuuta, M., & Tolhurst, D. J. (2005). Does a Bayesian model of V1 contrast coding offer a neurophysiological account of human contrast discrimination? Vision Research].

High Response Reliability of Neurons in Primary Visual Cortex (V1) of Alert, Trained Monkeys

The reliability of neuronal responses determines the resources needed to represent the external world and constrains the nature of the neural code. Studies of anesthetized animals have indicated that neuronal responses become progressively more variable as information travels from the retina to the cortex. These results have been interpreted to indicate that perception must be based on pooling across relatively large numbers of cells. However, we find that in alert monkeys, responses in primary visual cortex (V1) are as reliable as the inputs from the retina and the thalamus. Moreover, when the effects of fixational eye movements were minimized, response variability (variance/mean - Fano factor, FF) in all V1 layers was low. When presenting optimal stimuli, the median FF was 0.3. High variability, FF approximately 1, was found only near threshold. Our results suggest that in natural vision, suprathreshold perception can be based on small numbers of optimally stimulated cells.

Criteria interactions across visual attributes

Judgmental interference in dual tasks has been demonstrated in conditions where the detection or discrimination of different contrast increments applied to two stimuli presented simultaneously or in sequence. The present work demonstrates such interference for changes along two distinct visual features, namely contrast and orientation, when simultaneously applied to the same or to two distinct objects (Gabor-patches). The interference reveals itself in the use of quasi-equal decision criteria for both dimensions, in conflict with an optimal behavior requiring that criteria be proportional to the sensitivities for the distinct changes. The quasi-equality of the criteria assessed for contrast and orientation changes implies the equality of the internal noises characterizing the respective detection process, hence suggesting that they are limited at the decision level. Among the conceptual consequences of this limitation are the existence of a meta-attribute decisional dimension (tantamount to that of a "central executive system") and the questionable merits of probability summation over spatial channels. In addition, the data show a significant sensitivity drop in the dual- with respect to the single-task conditions, all the more so when the modulated features belong to two objects rather than to the same object. While the sensitivity drop in dual tasks is the standard trademark of distributed attention, it is argued that decisional interference is yet another aspect of it.

Temporal dynamics of shape analysis in macaque visual area V2

The firing rate of visual cortical neurons typically changes substantially during a sustained visual stimulus. To assess whether, and to what extent, the information about shape conveyed by neurons in visual area V2 changes over the course of the response, we recorded the responses of V2 neurons in awake, fixating monkeys while presenting a diverse set of static shape stimuli within the classical receptive field. We analyzed the time course of various measures of responsiveness and stimulus-related response modulation at the level of individual cells and of the population. For a majority of V2 cells, the response modulation was maximal during the initial transient response (40-80 ms after stimulus onset). During the same period, the population response was relatively correlated, in that V2 cells tended to respond similarly to specific subsets of stimuli. Over the ensuing 80-100 ms, the signal-to-noise ratio of individual cells generally declined, but to a lesser degree than the evoked-response rate during the corresponding time bins, and the response profiles became decorrelated for many individual cells. Concomitantly, the population response became substantially decorrelated. Our results indicate that the information about stimulus shape evolves dynamically and relatively rapidly in V2 during static visual stimulation in ways that may contribute to form discrimination.

An adjustment to the time-rescaling method for application to short-trial spike train data

It is important to validate models of neural data using appropriate goodness-of-fit measures. Models summarizing some response features--for example, spike count distributions or peristimulus time histograms--can be assessed using standard statistical tools. Measuring the fit of a full point-process model of spike trains is more difficult. Recently, Barbieri, Quirk, Frank, Wilson, and Brown (2001) and Brown, Barbieri, Ventura, Kass, and Frank (2002) presented a method for rescaling time so that if an underlying description correctly describes the conditional intensity function of a point process, the rescaling will convert the process into a homogeneous Poisson process. The corresponding interevent intervals are exponential with mean 1 and can be transformed to be uniform; tests of the uniformity of the transformed intervals are thus tests of how well the model fits the data. When the lengths of interevent intervals are comparable to the length of the observation window, as can happen in common neurophysiology paradigms using short trials, the fact that long intervals cannot be observed (are censored) can cause the tests based on time rescaling to reject a correct model inappropriately. This article presents a simple adjustment to the time-rescaling method to account for interval censoring, avoiding inappropriate rejection of acceptable models for short-trial data. We illustrate the adjustment's effect using both simulated data and short-trial data from monkey primary visual cortex.

Neural noise and movement-related codes in the macaque supplementary motor area

We analyzed the variability of spike counts and the coding capacity of simultaneously recorded pairs of neurons in the macaque supplementary motor area (SMA). We analyzed the mean-variance functions for single neurons, as well as signal and noise correlations between pairs of neurons. All three statistics showed a strong dependence on the bin width chosen for analysis. Changes in the correlation structure of single neuron spike trains over different bin sizes affected the mean-variance function, and signal and noise correlations between pairs of neurons were much smaller at small bin widths, increasing monotonically with the width of the bin. Analyses in the frequency domain showed that the noise between pairs of neurons, on average, was most strongly correlated at low frequencies, which explained the increase in noise correlation with increasing bin width. The coding performance was analyzed to determine whether the temporal precision of spike arrival times and the interactions within and between neurons could improve the prediction of the upcoming movement. We found that in approximately 62% of neuron pairs, the arrival times of spikes at a resolution between 66 and 40 msec carried more information than spike counts in a 200 msec bin. In addition, in 19% of neuron pairs, inclusion of within (11%)- or between-neuron (8%) correlations in spike trains improved decoding accuracy. These results suggest that in some SMA neurons elements of the spatiotemporal pattern of activity may be relevant for neural coding.

Coding of the contrasts in natural images by populations of neurons in primary visual cortex (V1)

It is possible to discriminate between grating contrasts over a 300-fold contrast range, whereas V1 neurons have very limited dynamic ranges. Using populations of model neurons with contrast-response parameters taken from electrophysiological studies (cat and macaque), we investigated ways of combining responses to code contrast over the full range. One model implemented a pooling rule that retained information about individual response patterns. The second summed responses indiscriminately. We measured accuracy of contrast identification over a wide range of contrasts and found the first model to be more accurate; the mutual information between actual and estimated contrast was also greatest for this model. The accuracy peak for the population of cat neurons coincided with the peak of the distribution of contrasts in natural images, suggesting an ecological match. Macaque neurons seem better able to code contrasts that are slightly higher on average than those found in the natural environment.

Vision and the statistics of the visual environment

It is widely believed that visual systems are optimized for the visual properties of the environment inhabited by the organism. A specific instance of this principle is known as the Efficient Coding Hypothesis, which holds that the purpose of early visual processing is to produce an efficient representation of the incoming visual signal. The theory provides a quantitative link between the statistical properties of the world and the structure of the visual system. As such, specific instances of this theory have been tested experimentally, and have been used to motivate and constrain models for early visual processing.

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.

Responses of primate visual cortical V4 neurons to simultaneously presented stimuli

We report here results from 45 primate V4 visual cortical neurons to the preattentive presentations of seven different patterns located in two separate areas of the same receptive field and to combinations of the patterns in the two locations. For many neurons, we could not determine any clear relationship for the responses to two simultaneous stimuli. However, for a substantial fraction of the neurons we found that the firing rate was well modeled as the maximum firing rate of each stimulus presented separately. It has previously been proposed that taking the maximum of the inputs ("MAX" operator) could be a useful operation for neurons in visual cortex, although there has until now been little direct physiological evidence for this hypothesis. Our results here provide direct support for the hypothesis that the MAX operator plays a significant (although certainly not exclusive) role in generating the receptive field properties of visual cortical neurons.

The temporal resolution of neural codes: does response latency have a unique role?

This article reviews the nature of the neural code in non-human primate cortex and assesses the potential for neurons to carry two or more signals simultaneously. Neurophysiological recordings from visual and motor systems indicate that the evidence for a role for precisely timed spikes relative to other spike times (ca. 1-10 ms resolution) is inconclusive. This indicates that the visual system does not carry a signal that identifies whether the responses were elicited when the stimulus was attended or not. Simulations show that the absence of such a signal reduces, but does not eliminate, the increased discrimination between stimuli that are attended compared with when the stimuli are unattended. The increased accuracy asymptotes with increased gain control, indicating limited benefit from increasing attention. The absence of a signal identifying the attentional state under which stimuli were viewed can produce the greatest discrimination between attended and unattended stimuli. Furthermore, the greatest reduction in discrimination errors occurs for a limited range of gain control, again indicating that attention effects are limited. By contrast to precisely timed patterns of spikes where the timing is relative to other spikes, response latency provides a fine temporal resolution signal (ca. 10 ms resolution) that carries information that is unavailable from coarse temporal response measures. Changes in response latency and changes in response magnitude can give rise to different predictions for the patterns of reaction times. The predictions are verified, and it is shown that the standard method for distinguishing executive and slave processes is only valid if the representations of interest, as evidenced by the neural code, are known. Overall, the data indicate that the signalling evident in neural signals is restricted to the spike count and the precise times of spikes relative to stimulus onset (response latency). These coding issues have implications for our understanding of cognitive models of attention and the roles of executive and slave systems.