Probing top-down information in neocortical layer 1

Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally generated top-down information. Decades of work have elucidated how sensory neocortex processes physical stimulus features. By contrast, examining how top-down information is encoded and integrated with bottom-up signals has been challenging using traditional neuroscience methods. Recent technological advances in functional imaging of brain-wide afferents in behaving mice have enabled the direct measurement of top-down information. Here, we review the emerging literature on encoding of these internally generated signals by different projection systems enriched in neocortical layer 1 during defined brain functions, including memory, attention, and predictive coding. Moreover, we identify gaps in current knowledge and highlight future directions for this rapidly advancing field.     

Context‐dependent coding and gain control in the auditory system of crickets

Sensory systems process stimuli that greatly vary in intensity and complexity. To maintain efficient information transmission, neural systems need to adjust their properties to these different sensory contexts, yielding adaptive or stimulus‐dependent codes. Here, we demonstrated adaptive spectrotemporal tuning in a small neural network, i.e. the peripheral auditory system of the cricket. We found that tuning of cricket auditory neurons was sharper for complex multi‐band than for simple single‐band stimuli. Information theoretical considerations revealed that this sharpening improved information transmission by separating the neural representations of individual stimulus components. A network model inspired by the structure of the cricket auditory system suggested two putative mechanisms underlying this adaptive tuning: a saturating peripheral nonlinearity could change the spectral tuning, whereas broad feed‐forward inhibition was able to reproduce the observed adaptive sharpening of temporal tuning. Our study revealed a surprisingly dynamic code usually found in more complex nervous systems and suggested that stimulus‐dependent codes could be implemented using common neural computations.     

Viewing and doing: similar cortical mechanisms for perceptual and motor learning

Historically, different groups of researchers have investigated the mechanisms of perceptual learning and motor learning. For sensory cortex, neurophysiological and psychophysical findings have linked changes in perception with altered neuronal tuning properties. However, less information has been forthcoming from motor cortex. This review compares recent findings on perceptual and motor learning, and suggests that similar mechanisms govern both. These mechanisms involve changes in both the center of neuronal tuning functions and their width or slope. The former reflects the values of the sensory or motor parameters that a neuron encodes, and the latter adjusts the encoding sensitivity. These similarities suggest that specific unifying principles for neural coding and computation exist across sensory and motor domains.     

Saturating Nonlinearities of Contrast Response in Human Visual Cortex

Response nonlinearities are ubiquitous throughout the brain, especially within sensory cortices where changes in stimulus intensity typically produce compressed responses. Although this relationship is well established in electrophysiological measurements, it remains controversial whether the same nonlinearities hold for population-based measurements obtained with human fMRI. We propose that these purported disparities are not contingent on measurement type and are instead largely dependent on the visual system state at the time of interrogation. We show that deploying a contrast adaptation paradigm permits reliable measurements of saturating sigmoidal contrast response functions (10 participants, 7 female). When not controlling the adaptation state, our results coincide with previous fMRI studies, yielding nonsaturating, largely linear contrast responses. These findings highlight the important role of adaptation in manifesting measurable nonlinear responses within human visual cortex, reconciling discrepancies reported in vision neuroscience, re-establishing the qualitative relationship between stimulus intensity and response across different neural measures and the concerted study of cortical gain control.SIGNIFICANCE STATEMENT Nonlinear stimulus–response relationships govern many essential brain functions, ranging from the sensory to cognitive level. Certain core response properties previously shown to be nonlinear with nonhuman electrophysiology recordings have yet to be reliably measured with human neuroimaging, prompting uncertainty and reconsideration. The results of this study stand to reconcile these incongruencies in the vision neurosciences, demonstrating the profound impact adaptation can have on brain activation throughout the early visual cortex. Moving forward, these findings facilitate the study of modulatory influences on sensory processing (i.e., arousal and attention) and help establish a closer link between neural recordings in animals and hemodynamic measurements from human fMRI, resuming a concerted effort to understand operations in the mammalian cortex.     

Improved mapping of information distribution across the cortical surface with the support vector machine

The early visual cortices represent information of several stimulus attributes, such as orientation and color. To understand the coding mechanisms of these attributes in the brain, and the functional organization of the early visual cortices, it is necessary to determine whether different attributes are represented by different compartments within each cortex. Previous studies addressing this question have focused on the information encoded by the response amplitude of individual neurons or cortical columns, and have reached conflicting conclusions. Given the correlated variability in response amplitude across neighboring columns, it is likely that the spatial pattern of responses across these columns encodes the attribute information more reliably than does the response amplitude. Here we present a new method of mapping the spatial distribution of information that is encoded by both the response amplitude and the spatial pattern. This new method is based on a statistical learning approach, the Support Vector Machine (SVM). Application of this new method to our optical imaging data suggests that information about stimulus orientation and color are distributed differently in the striate cortex, and this observation is consistent with the hypothesis of segregated representations of orientation and color in this area. We also demonstrate that SVM can be used to extract “single-condition” activation maps from noisy images of intrinsic optical signals.     

Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex

The precise timing of spikes of cortical neurons relative to stimulus onset carries substantial sensory information. To access this information the sensory systems would need to maintain an internal temporal reference that reflects the precise stimulus timing. Whether and how sensory systems implement such reference frames to decode time-dependent responses, however, remains debated. Studying the encoding of naturalistic sounds in primate (Macaca mulatta) auditory cortex we here investigate potential intrinsic references for decoding temporally precise information. Within the population of recorded neurons, we found one subset responding with stereotyped fast latencies that varied little across trials or stimuli, while the remaining neurons had stimulus-modulated responses with longer and variable latencies. Computational analysis demonstrated that the neurons with stereotyped short latencies constitute an effective temporal reference for relative coding. Using the response onset of a simultaneously recorded stereotyped neuron allowed decoding most of the stimulus information carried by onset latencies and the full spike train of stimulus-modulated neurons. Computational modeling showed that few tens of such stereotyped reference neurons suffice to recover nearly all information that would be available when decoding the same responses relative to the actual stimulus onset. These findings reveal an explicit neural signature of an intrinsic reference for decoding temporal response patterns in the auditory cortex of alert animals. Furthermore, they highlight a role for apparently unselective neurons as an early saliency signal that provides a temporal reference for extracting stimulus information from other neurons.     

Auditory information processing during human sleep as revealed by event-related brain potentials.

The main goal of this review is to elucidate up to what extent pre-attentive auditory information processing is affected during human sleep. Evidence from event-related brain potential (ERP) studies indicates that auditory information processing is selectively affected, even at early phases, across the different stages of sleep-wakefulness continuum. According to these studies, 3 main conclusions are drawn: (1) the sleeping brain is able to automatically detect stimulus occurrence and trigger an orienting response towards that stimulus if its degree of novelty is large; (2) auditory stimuli are represented in the auditory system and maintained for a period of time in sensory memory, making the automatic-change detection during sleep possible; and (3) there are specific brain mechanisms (sleep-specific ERP components associated with the presence of vertex waves and K-complexes) by which information processing can be improved during non-rapid eye movement sleep. However, the remarkably affected amplitude and latency of the waking-ERPs during the different stages of sleep suggests deficits in the building and maintenance of a neural representation of the stimulus as well as in the process by which neural events lead to an orienting response toward such a stimulus. The deactivation of areas in the dorsolateral pre-frontal cortex during sleep contributing to the generation of these ERP components is hypothesized to be one of the main causes for the attenuated amplitude of these ERPs during human sleep.     

Information-theoretic methods for studying population codes

Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.     

Single neuron firing properties impact correlation-based population coding

Correlated spiking has been widely observed, but its impact on neural coding remains controversial. Correlation arising from comodulation of rates across neurons has been shown to vary with the firing rates of individual neurons. This translates into rate and correlation being equivalently tuned to the stimulus; under those conditions, correlated spiking does not provide information beyond that already available from individual neuron firing rates. Such correlations are irrelevant and can reduce coding efficiency by introducing redundancy. Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: "ideal" integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. Pyramidal neurons are sensitive to both stimulus mean and variance, and thus exhibit both types of output correlation proportioned according to which operating mode is dominant. Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons.     

Brainstem control of orienting movements: intrinsic coordinate systems and underlying circuitry.

A fundamental issue in the understanding of how the nervous system processes information is the way in which sensory information is used to initiate and guide movements. Recent progress has been made by taking an information processing approach in which information--for example, the spatial location of an object towards which an animal will orient--is tracked through the nervous system from sensory to motor levels. In this approach, neurally encoded information is characterized in terms of its representation within a neural or intrinsic coordinate system or set of neural coding parameters. For example, the retina codes spatial location in terms of the location of activity on the retinal surface, whereas motoneurons code spatial location in terms of the pulling directions of the muscles they activate. In between these two peripheral stages, the information passes through intermediate coordinate systems. These intermediate coordinate systems can be characterized by recording or altering the activity of small groups of neurons while an animal is performing a well-defined sensorimotor task. Spatial location information is used to guide orienting movements, those movements made by the eyes, ears, head, or body which function to center an object of interest in the animal's visual field. The optic tectum and forebrain, their connections to the medial mesencephalic and rhombencephalic brainstem tegmental cell groups, and subsequent connections to brainstem motor nuclei and spinal cord are employed to control fundamental aspects of this behavior. Studies reviewed herein indicate that following the retinotopic coding of spatial location in the retina and tectum, spatial location information appears to enter a different coordinate system at tegmental levels in which spatial aspects of orienting movement are coded in terms of their discrete horizontal and vertical components. This Cartesian coordinate system is an example of an abstract neural coordinate system, in that it is a simple, low-dimensional representation of spatial location which differs greatly from both sensory and motor representations. Also, this Cartesian representation may be common to many orienting movements, yet it appears to differ from the coordinate systems controlling other movement types such as stabilization or phasic movements. This suggests an hypothesis in which coordinate systems, especially at intermediate levels of processing, may be organized according to behavioral task as opposed to being determined by the particular sensory or motor system involved in the behavior. Understanding the evolutionary heritage and computational function of abstract neural coordinate systems, and the relation between different coordinate systems and behavioral tasks may be useful in understanding general aspects of sensory information processing and motor control.     

Decoding Word Information from Spatiotemporal Activity of Sensory Neurons

Spatiotemporal activity of neurons is ubiquitous in sensory coding in the CNS. It is a fundamental problem for sensory perception to understand how sensory information is decoded from the spatiotemporal activity. However, little is known about the decoding mechanism. To address this issue, we are concerned with auditory system as a model system exhibiting spatiotemporal activity. We present here a model of auditory cortex, which performs a hierarchical processing of auditory information. The model consists of three layers of two-dimensional networks. The first layer represents auditory stimulus as a spatiotemporal activity of neurons. The second layer consists of feature-detecting neurons, which extract the features of phonemes and their overlaps from the spatiotemporal activity of the first layer. The third layer combines information of the sound features encoded by the second layer and decodes word information about the sound stimulus as a temporal sequence of attractors. Using the model, we show how the information of phonemes and words emerge in the hierarchical processing of the auditory cortex. We also show that the overlap between phonemes plays a crucial role in linking the attractors of phonemes. The present study may provide a clue for understanding the mechanism by which word information is decoded from spatiotemporal activity of neurons.     

Reading Between the Spikes of the Hypothalamic Neural Code

Reading the spike coding of hypothalamic neurones presents a considerable challenge because they exhibit highly irregular firing patterns. Electrophysiologists working in the motor and sensory systems, in which neurones fire more regularly, have devised satisfactory methods to describe the firing of cells, although the statistical assumptions that underlie the methods do not apply to hypothalamic neurones. Measurement of neural activity is nevertheless vital to characterise the activity of neuroendocrine cells. It has thus become necessary to develop methods suitable for the analysis of the highly irregular spike discharge patterns of both spontaneous and stimulus‐evoked firing of hypothalamic neurones. We review techniques used to meet this challenge and demonstrate their considerable capacity to address important physiological questions. We also introduce a novel approach for valid statistical estimation of the information conveyed by the response of a single neurone to a periodic stimulus. The approach demonstrated significant diurnal rhythms of synaptic connectivity between hypothalamic nuclei.     

A model of task-specific focal dystonia

Task-specific focal dystonia is a task-specific movement disorder which manifests itself as a loss of voluntary motor control in extensively trained movements. The condition is most frequent in musicians. Until today, the aetiology of focal hand dystonia is not completely understood, but there is growing evidence for an abnormal cortical processing of sensory information, as well as degraded representation of motor functions. It was demonstrated that in the somatosensory cortex the topographical location of sensory inputs from individual fingers is corrupted. Occasionally, a change in sensory information of the hand may at least temporarily improve the condition. This phenomenon is called sensory trick. In this paper, we propose a model of encoding of sensory stimuli which could explain the task specificity of cortical representations of the fingers or other effectors in the context of dystonia. In the framework of this model a sensory stimulus is encoded as a signal vector of higher dimension. A part of its components directly represents the sensory stimulus, while the remaining components describe the context. This model does not only account for the task specificity, but may also explain some characteristics of the retraining process in this disorder.     

A roadmap to integrate astrocytes into Systems Neuroscience

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca²⁺ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte–neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca²⁺ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.     

An improved method for the estimation of firing rate dynamics using an optimal digital filter

In most neural systems, neurons communicate by means of sequences of action potentials or 'spikes'. Information encoded by spike trains is often quantified in terms of the firing rate which emphasizes the frequency of occurrence of action potentials rather than their exact timing. Common methods for estimating firing rates include the rate histogram, the reciprocal interspike interval, and the spike density function. In this study, we demonstrate the limitations of these aforementioned techniques and propose a simple yet more robust alternative. By convolving the spike train with an optimally designed Kaiser window, we show that more robust estimates of firing rate are obtained for both low and high-frequency inputs. We illustrate our approach by considering spike trains generated by simulated as well as experimental data obtained from single-unit recordings of first-order sensory neurons in the vestibular system. Improvements were seen in the prevention of aliasing, phase and amplitude distortion, as well as in the noise reduction for sinusoidal and more complex input profiles. We review the generality of the approach, and show that it can be adapted to describe neurons with sensory or motor responses that are characterized by marked nonlinearities. We conclude that our method permits more robust estimates of neural dynamics than conventional techniques across all stimulus conditions.