An optimal decision population code that accounts for correlated variability unambiguously predicts a subject's choice

Representation of a perceptual bias in the prefrontal cortex

Perception is influenced by sensory stimulation, prior knowledge, and contextual cues, which collectively contribute to the emergence of perceptual biases. However, the precise neural mechanisms underlying these biases remain poorly understood. This study aims to address this gap by analyzing neural recordings from the prefrontal cortex (PFC) of monkeys performing a vibrotactile frequency discrimination task. Our findings provide empirical evidence supporting the hypothesis that perceptual biases can be reflected in the neural activity of the PFC. We found that the state-space trajectories of PFC neuronal activity encoded a warped representation of the first frequency presented during the task. Remarkably, this distorted representation of the frequency aligned with the predictions of its Bayesian estimator. The identification of these neural correlates expands our understanding of the neural basis of perceptual biases and highlights the involvement of the PFC in shaping perceptual experiences. Similar analyses could be employed in other delayed comparison tasks and in various brain regions to explore where and how neural activity reflects perceptual biases during different stages of the trial.

Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex

How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain's adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.

Stimulus-dependent relationships between behavioral choice and sensory neural responses

Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.

Approaches to inferring multi-regional interactions from simultaneous population recordings: Inferring multi-regional interactions from simultaneous population recordings

Most past studies of neural representations and dynamics have focused on recordings from single brain areas. However, growing evidence of brain-wide, parallel representations of cognitive variables suggests that analyzing neural representations and dynamics in individual brain areas can benefit from understanding the context of multi-regional interactions that support them. Moreover, perturbation experiments revealed that the manner in which these parallel representations interact with each other can differ dramatically across different pairs of brain areas. Recent advances in recording technology offer a potentially powerful substrate to study how multi-regional interactions coordinate neural representations in individual brain areas and dictate behavior on a single-trial basis through simultaneous recordings of multiple brain areas. We review pragmatic approaches to studying multi-regional interactions and illustrate them in the concrete context of a rodent delayed response task paradigm.

The Correlation of Neuronal Signals with Behavior at Different Levels of Visual Cortex and Their Relative Reliability for Behavioral Decisions

Behavior can be guided by neuronal activity in visual, auditory, or somatosensory cerebral cortex, depending on task requirements. In contrast to this flexible access of cortical signals, several observations suggest that behaviors depend more on neurons in later areas of visual cortex than those in earlier areas, although neurons in earlier areas would provide more reliable signals for many tasks. We recorded from neurons in different levels of visual cortex of 2 male rhesus monkeys while the animals did a visual discrimination task and examined trial-to-trial correlations between neuronal and behavioral responses. These correlations became stronger in primary visual cortex as neuronal signals in that area became more reliable relative to the other areas. The results suggest that the mechanisms that read signals from cortex might access any cortical area depending on the relative value of those signals for the task at hand.SIGNIFICANCE STATEMENT Information is encoded by the action potentials of neurons in various cortical areas in a hierarchical manner such that increasingly complex stimulus features are encoded in successive stages. The brain must extract information from the response of appropriate neurons to drive optimal behavior. A widely held view of this decoding process is that the brain relies on the output of later cortical areas to make decisions, although neurons in earlier areas can provide more reliable signals. We examined correlations between perceptual decisions and the responses of neurons in different levels of monkey visual cortex. The results suggest that the brain may access signals in any cortical area depending on the relative value of those signals for the task at hand.

Sucrose intensity coding and decision-making in rat gustatory cortices

Sucrose's sweet intensity is one attribute contributing to the overconsumption of high-energy palatable foods. However, it is not known how sucrose intensity is encoded and used to make perceptual decisions by neurons in taste-sensitive cortices. We trained rats in a sucrose intensity discrimination task and found that sucrose evoked a widespread response in neurons recorded in posterior-Insula (pIC), anterior-Insula (aIC), and Orbitofrontal cortex (OFC). Remarkably, only a few Intensity-selective neurons conveyed the most information about sucrose's intensity, indicating that for sweetness the gustatory system uses a compact and distributed code. Sucrose intensity was encoded in both firing-rates and spike-timing. The pIC, aIC, and OFC neurons tracked movement direction, with OFC neurons yielding the most robust response. aIC and OFC neurons encoded the subject's choices, whereas all three regions tracked reward omission. Overall, these multimodal areas provide a neural representation of perceived sucrose intensity, and of task-related information underlying perceptual decision-making.

Probing Sensory Readout via Combined Choice-Correlation Measures and Microstimulation Perturbation

It is controversial whether covariation between neuronal activity and perceptual choice (i.e., choice correlation) reflects the functional readout of sensory signals. Here, we combined choice-correlation measures and electrical microstimulation on a site-to-site basis in the medial superior temporal area (MST), middle temporal area (MT), and ventral intraparietal area (VIP) when macaques discriminated between motion directions in both fine and coarse tasks. Microstimulation generated comparable effects between tasks but heterogeneous effects across and within brain regions. Within the MST and MT, microstimulation significantly biased an animal's choice toward the sensory preference instead of choice-related signals of the stimulated units. This was particularly evident for sites with conflict preference of sensory and choice-related signals. In the VIP, microstimulation failed to produce significant effects in either task despite strong choice correlations presented in this area. Our results suggest that sensory readout may not be inferred from choice-related signals during perceptual decision-making tasks.

Decoding a Decision Process in the Neuronal Population of Dorsal Premotor Cortex

When trained monkeys discriminate the temporal structure of two sequential vibrotactile stimuli, dorsal premotor cortex (DPC) showed high heterogeneity among its neuronal responses. Notably, DPC neurons coded stimulus patterns as broader categories and signaled them during working memory, comparison, and postponed decision periods. Here, we show that such population activity can be condensed into two major coding components: one that persistently represented in working memory both the first stimulus identity and the postponed informed choice and another that transiently coded the initial sensory information and the result of the comparison between the two stimuli. Additionally, we identified relevant signals that coded the timing of task events. These temporal and task-parameter readouts were shown to be strongly linked to the monkeys' behavior when contrasted to those obtained in a non-demanding cognitive control task and during error trials. These signals, hidden in the heterogeneity, were prominently represented by the DPC population response.

Dopamine reward prediction error signal codes the temporal evaluation of a perceptual decision report

Learning to associate unambiguous sensory cues with rewarded choices is known to be mediated by dopamine (DA) neurons. However, little is known about how these neurons behave when choices rely on uncertain reward-predicting stimuli. To study this issue we reanalyzed DA recordings from monkeys engaged in the detection of weak tactile stimuli delivered at random times and formulated a reinforcement learning model based on belief states. Specifically, we investigated how the firing activity of DA neurons should behave if they were coding the error in the prediction of the total future reward when animals made decisions relying on uncertain sensory and temporal information. Our results show that the same signal that codes for reward prediction errors also codes the animal's certainty about the presence of the stimulus and the temporal expectation of sensory cues.

Distinct timescales of population coding across cortex

The cortex represents information across widely varying timescales^1–5. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds^6,7, whereas in association cortex behavioral choices can require the maintenance of information over seconds^8,9. However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question is unanswered because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we discovered that population codes can be essential to achieve long coding timescales. Furthermore, we found that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioral choices in auditory cortex (AC) and posterior parietal cortex (PPC) as mice performed a sound localization task. Auditory stimulus information was stronger in AC than in PPC, and both regions contained choice information. Although AC and PPC coded information by tiling in time neurons that were transiently informative for ~200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among PPC neurons was strong and extended over long time lags, whereas coupling among AC neurons was weak and short-lived. Stronger coupling in PPC led to a population code with long timescales and a representation of choice that remained consistent for approximately one second. In contrast, AC had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behavior.

Visual Stimulus Detection Correlates with the Consistency of Temporal Sequences within Stereotyped Events of V1 Neuronal Population Activity

Sensory information about the world is translated into rate codes, such that modulations in mean spiking activity of neurons relate to differences in stimulus features. More recently, it has been proposed that also temporal properties of activity, such as assembly formation and sequential population activation, are important for understanding the relation between neuronal activity and behavioral output. These phenomena appear to be robust properties of neural circuits, but their relevance for perceptual judgments, such as the behavioral detection of stimuli, remains to be tested. Studying neuronal activity with two-photon calcium imaging in primary visual cortex of mice performing a go/no-go visual detection task, we found that assemblies (i.e., configurations of neuronal group activity) reliably recur, as defined using Ward-method clustering. However, population activation events with a recurring configuration of core neurons did not appear to serve a particular function in the coding of orientation or the detection of stimuli. Instead, we found that, regardless of whether the population event showed a recurring or nonrecurring configuration of neurons, the sequence of cluster activation was correlated with the detection of stimuli. Moreover, each neuron showed a preferred temporal position of activation within population events, which was robust despite varying neuronal participation. Furthermore, the timing of neuronal activity within such a sequence was more consistent when a stimulus was detected (hits) than when it remained unreported (misses). Our data indicate that neural processing of information related to visual detection behavior depends on the temporal positioning of individual and group-wise cell activity.

SIGNIFICANCE STATEMENT

Temporally coactive neurons have been hypothesized to form functional assemblies that might subserve different functions in the brain, but many of these proposed functions have not yet been experimentally tested. We used two-photon calcium imaging in V1 of mice performing a stimulus detection task to study the relation of assembly activity to the behavioral detection of visual stimuli. We found that the presence of recurring assemblies per se was not correlated with behavior, and these assemblies did not appear to serve a function in the coding of stimulus orientation. Instead, we found that activity in V1 is characterized by population events of varying membership, within which the consistency of the temporal sequence of neuronal activation is correlated with stimulus detection.

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

The problem of neural coding in perceptual decision making revolves around two fundamental questions: (i) How are the neural representations of sensory stimuli related to perception, and (ii) what attributes of these neural responses are relevant for downstream networks, and how do they influence decision making? We studied these two questions by recording neurons in primary somatosensory (S1) and dorsal premotor (DPC) cortex while trained monkeys reported whether the temporal pattern structure of two sequential vibrotactile stimuli (of equal mean frequency) was the same or different. We found that S1 neurons coded the temporal patterns in a literal way and only during the stimulation periods and did not reflect the monkeys' decisions. In contrast, DPC neurons coded the stimulus patterns as broader categories and signaled them during the working memory, comparison, and decision periods. These results show that the initial sensory representation is transformed into an intermediate, more abstract categorical code that combines past and present information to ultimately generate a perceptually informed choice.

Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns

Previous studies have demonstrated the importance of the primary sensory cortex for the detection, discrimination, and awareness of visual stimuli, but it is unknown how neuronal populations in this area process detected and undetected stimuli differently. Critical differences may reside in the mean strength of responses to visual stimuli, as reflected in bulk signals detectable in functional magnetic resonance imaging, electro-encephalogram, or magnetoencephalography studies, or may be more subtly composed of differentiated activity of individual sensory neurons. Quantifying single-cell Ca²⁺ responses to visual stimuli recorded with in vivo two-photon imaging, we found that visual detection correlates more strongly with population response heterogeneity rather than overall response strength. Moreover, neuronal populations showed consistencies in activation patterns across temporally spaced trials in association with hit responses, but not during nondetections. Contrary to models relying on temporally stable networks or bulk signaling, these results suggest that detection depends on transient differentiation in neuronal activity within cortical populations.

DOI:http://dx.doi.org/10.7554/eLife.10163.001

Seeing is not the same as perceiving, where an object is recognized and information about it is interpreted by the brain. Things might be in your field of view, but not actively perceived; for example, when daydreaming with your eyes open. Many researchers have investigated how the brain responds differently to a perceived object compared with something that is seen but not perceived. However, using relatively coarse techniques, only small differences in brain activity have been found.

Many of the techniques used to investigate brain activity only look at the average activity of a group of neurons – the cells in the brain that process information. This raises the possibility that the perception of an object relies on more subtle or complex interactions in brain activity. To investigate this, Montijn et al. trained mice to lick a reward spout that gave out sugar water when they perceived a particular image. A technique called two-photon calcium imaging was then used to simultaneously record the activity of tens to hundreds of neurons in part of the brain called the visual cortex as the mice performed the perception task.

This revealed that the average activation of a group of neurons was only weakly related to whether a mouse had perceived the image. However, differences in the strength of the responses of the individual neurons in the group reflected perception more strongly: when a mouse perceived the image and licked in response, a heterogeneous (non-uniform) set of neuronal responses occurred. The diversity of the neuronal responses could also be used to predict how quickly a mouse would respond to an image. These activity differences would not be picked up by techniques that detect the average activity of many neurons, explaining why these effects had not previously been seen.

These findings shed light on which patterns of activity in the visual region of the brain lead to objects being perceived or not. Whether similar mechanisms operate in different regions of the brain remains to be investigated.

DOI:http://dx.doi.org/10.7554/eLife.10163.002

Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty

Under uncertainty, the brain uses previous knowledge to transform sensory inputs into the percepts on which decisions are based. When the uncertainty lies in the timing of sensory evidence, however, the mechanism underlying the use of previously acquired temporal information remains unknown. We study this issue in monkeys performing a detection task with variable stimulation times. We use the neural correlates of false alarms to infer the subject's response criterion and find that it modulates over the course of a trial. Analysis of premotor cortex activity shows that this modulation is represented by the dynamics of population responses. A trained recurrent network model reproduces the experimental findings and demonstrates a neural mechanism to benefit from temporal expectations in perceptual detection. Previous knowledge about the probability of stimulation over time can be intrinsically encoded in the neural population dynamics, allowing a flexible control of the response criterion over time.