Perceptual decision-making is an important and experimentally tractable paradigm for uncovering general principles of neural information processing and cognitive function. While the process of mapping sensory stimuli onto motor actions may appear to be simple, its neural underpinnings are poorly understood. The goal of this thesis is to better understand the neural mechanisms underlying perceptual decision-making by exploring three major questions: How is decision-relevant information encoded across the cortex? What cortical areas are necessary for perceptual decision-making? And finally, what neural mechanisms underlie the mapping of sensory percepts to appropriate motor outputs? We investigated the roles of visual (V1), posterior parietal (PPC), and frontal motor (fMC) cortices of mice during a memory-guided visual decision task. Large-scale calcium imaging revealed that neurons in each area were heterogeneous and spanned all task epochs (stimulus, delay, response). However, information encoding was distinct across regions, with V1 encoding stimulus, fMC encoding choice, and PPC multiplexing the two variables. Optogenetic inhibition during behavior showed that all regions were necessary during the stimulus epoch, but only fMC was required during the delay and response epochs. Stimulus information was therefore rapidly transformed into behavioral choice, requiring V1, PPC, and fMC during the transformation period, but only fMC for maintaining the choice in memory prior to execution. We further investigated whether the role of PPC was specific to visual processing or to sensorimotor transformation. Using calcium imaging during both engaged behavior and passive viewing, we found that unlike V1 neurons, most PPC neurons responded exclusively during task performance, although a minority exhibited contrast-dependent visual responses. By re-training mice on a reversed task contingency, we discovered that neurons in PPC but not V1 reflected the new sensorimotor contingency. Population analyses additionally revealed that task-specific information was represented in a dynamic code in PPC but not in V1. The strong task dependence, heterogeneity, and dynamic coding of PPC activity point to a central role in sensorimotor transformation. By measuring and manipulating activity across multiple cortical regions, we have gained insight into how the cortex processes information during sensorimotor decisions, paving the way for future mechanistic studies using the mouse system.

Seemingly simple behaviours turn out, on reflection, to be discouragingly complex. For many years, cognitive operations such as sensation, perception, comparing percepts to stored models (short-term and long-term memory), decision-making and planning of actions were treated by most neuroscientists as separate areas of research. This was not because the neuroscience community believed these operations to act independently—it is intuitive that any common cognitive process seamlessly interweaves these operations—but because too little was known about the individual processes constituting the full behaviour, and experimental paradigms and data collection methods were not sufficiently well developed to put the processes in sequence in any controlled manner. These limitations are now being overcome in the leading cognitive neuroscience laboratories, and this book is a timely summary of the current state of the art. The theme of the book is how the brain uses sensory information to develop and decide upon the appropriate action, and how the brain determines the appropriate action to optimize the collection of new sensory information. It addresses several key questions. How are percepts built up in the cortex and how are judgments of the percept made? In what way does information flow within and between cortical regions, and what is accomplished by successive (and reverberating) stages of processing? How are decisions made about the percept subsequently acted upon, through their conversion to a response according to the learned criterion for action? How does the predicted or expected sensation interact with the actual incoming flow of sensory signals? The chapters and discussions in the book reveal how answering these questions requires an understanding of sensory–motor loops: our perception of the world drives new actions, and the actions undertaken at any moment lead to a new ‘view’ of the world. This book is a fascinating read for all clinical and experimental psychologists and neuroscientists, as well as anyone interested in how we perceive the world and act within it.

Sensory perception is fundamentally limited by the coding accuracy of sensory neural ensembles. Although a substantial body of work suggests that populations of sensory neurons exhibit correlated fluctuations that may bound the precision of neural coding, the extent to which these fluctuations extend across multiple cortical areas and interact with sensory coding during active animal behavior remain poorly understood. To examine the impact of correlated fluctuations on information coding and communication across sensory cortical areas, we imaged the Ca2+ activity of > 21,000 individual neurons across 11 neocortical areas in mice performing a Go/No-Go visual decision-making task. Multiple neocortical areas accurately encoded the visual stimulus, as well as the animal's decision to respond. Our analysis also revealed positively correlated noise fluctuations across neural populations in multiple neocortical areas. The mean strength of these noise correlations varied as a function of time across the visual stimulus presentation, delay, and response periods of our decision-making assay. Notably, sensory cortical neurons generally exhibited noise fluctuations that were more positively correlated at the start of visual stimulation, but then less so as a decision-making trial proceeded. Our results reveal that 60% of the total power of cortical variability stems from correlated fluctuations of neural populations spanning multiple distinct cortical areas. The strongest cortical fluctuation was a decision-coding activity mode that encompassed all brain areas under observation. We also found several fluctuation modes that encoded visual stimulus information but were shared across fewer brain areas. Overall, our analyses suggest that information regarding sensory stimuli and perceptual decisions are processed and shared between cortical areas through mutually non-interfering orthogonal channels. The timing of informative activity in these channels suggests that sensory information is first processed through intercommunications between multiple sensory areas, followed by a propagation of the final decision to all cortical areas involved.

Progress in Brain Research is the most acclaimed and accomplished series in neuroscience, firmly established as an extensive documentation of the advances in contemporary brain research. The volumes, some of which are derived from important international symposia, contain authoritative reviews and original articles by invited specialists. The rigorous editing of the volumes assures that they will appeal to all laboratory and clinical brain research workers in the various disciplines: neuroanatomy, neurophysiology, neuropharmacology, neuroendocrinology, neuropathology, basic neurology, biological psychiatry, and the behavioral sciences. This volume, The Cerebellum and Memory Formation: Structure, Computation and Function, covers topics including feedback control of cerebellar learning; cortico-cerebellar organization and skill acquisition; cerebellar plasticity and learning in the oculomotor system, and more. - Leading authors review the state-of-the-art in their field of investigation, and provide their views and perspectives for future research - The volume reflects current thinking about the ways in which the cerebellum can engage in learning, and the contributors come from a variety of research fields - The chapters express perspectives from different levels of analysis that range from molecular and cellular mechanisms through to long-range systems that allow the cerebellum to communicate with other brain areas

Sensory experience can powerfully alter the spatial and temporal organization of population codes in the brain throughout an animal's lifetime. In this dissertation I focus on how changes in sensory experience, by learning or environmental enrichment, alter sensory map topography in rodent primary sensory cortex. In Chapter 1 I review recent advances in our understanding of how perceptual learning and related sensory manipulations shape the structure of primary sensory cortex maps across multiple sensory modalities. Classic studies of map plasticity in primary sensory cortex have shown that experience shapes sensory tuning in individual neurons and at the average population level. But effective neural population codes depend on more than just sensory tuning in individual cells averaged over time. With population calcium imaging, activity can be measured simultaneously in large ensembles of neurons during behavior. This and related techniques make it possible to study changes in the single-cell level organization of sensory coding, as well as high-dimensional codes that depend on the activity of large ensembles of cells, and which may change with behavioral context. Here I review recent population calcium imaging and recording studies that have characterized population codes in sensory cortex, and tracked how they change with sensory manipulations and training on perceptual learning tasks. These studies confirm average sensory tuning changes observed in earlier studies, but also reveal other features of plasticity, including sensory gain modulation, restructuring of firing correlations, and differential routing of information to output pathways. Unexpectedly strong day-to-day variation exists in single-neuron sensory tuning, which stabilizes during learning. These are novel dimensions of plasticity in sensory cortex, which refine population codes during learning, but whose mechanisms are unknown. Most of what is known about how sensory experience shapes maps is based on studies that have used robust sensory manipulations, such as deprivation, chronic over-stimulation, or explicit pairing of a sensory stimulus with a reward or punishment, as in the studies discussed in Chapter 1. However, much less is known about how maps are refined by natural behavior-driven sensory experience. To understand how natural tactile experience influences map development, We studied the effects of tactile enrichment on the organization of the whisker map in mouse primary somatosensory cortex (S1), which is highly plastic throughout life and which contains an anatomically well-defined map of the whiskers. Mice were raised with enrichment (EN) or normal housing (CT) beginning at weaning (P21). We focused our study on Layer 2/3 (L2/3), which is particularly plastic and which has a “salt-and-pepper” organization of whisker tuning, and Layer 4 (L4), which contains the anatomical map of the whiskers. The genetically-encoded calcium indicator GCaMP6s was expressed virally in L2/3 or L4 excitatory cells using cell-type and layer-specific Cre mice. At P62 ± 13 days, we measured neuronal responses to 9 whiskers using 2-photon imaging through a chronic cranial window. After imaging was complete, cells were localized relative to anatomical barrel column boundaries corresponding to the whiskers in the stimulus set. Within a single anatomical column in both L2/3 and L4 excitatory cells, cells were heterogeneously tuned to different whiskers. Enrichment increased somatotopic precision (the fraction of cells within a certain radius that were anatomically tuned) in both layers near the centers of anatomical columns. Enrichment also sharpened whisker tuning curves in both layers, and essentially increased signal-to-noise of whisker coding in L2/3 by decreasing spontaneous activity while maintaining response magnitudes for columnar whiskers. In L2/3, point representations (the density and spatial spread of neurons responding to a whisker) were sharpened by increased whisker responses among cells located close to column centers. To study the impact of enrichment on the spatial and temporal structure of population coding, we compared pairwise noise and signal correlations in whisker-evoked activity within each column. In L2/3, signal correlations – which reflect overall tuning similarity – were similar in EN and CT for pairs within columns, though signal correlations were higher in EN for pairs located further apart. However, for pairs of neurons located across column boundaries but at similar distances, signal correlations were dramatically decreased in EN, while comparable to in-column levels in CT. In both groups, noise correlations – which reflect shared trial-by-trial variability that is independent of the stimulus – decreased sharply with distance between neurons. For pairs of cells across column boundaries in CT mice, signal and noise correlations exhibited the same relationships with inter-cell distance as within column pairs; however, In EN mice, cross-column signal and noise correlations were substantially reduced compared to in-column pairs. Essentially, enrichment induced reorganization of functional correlations along anatomical column boundaries. This suggests that enrichment may selectively alter connectivity and/or shared synaptic input according to column boundaries. These findings demonstrate a strong impact of juvenile sensory experience on functional columnar topography and organization in S1, and indicate that enrichment sharpens whisker representations at the population level in L2/3.

This book brings together leading investigators who represent various aspects of brain dynamics with the goal of presenting state-of-the-art current progress and address future developments. The individual chapters cover several fascinating facets of contemporary neuroscience from elementary computation of neurons, mesoscopic network oscillations, internally generated assembly sequences in the service of cognition, large-scale neuronal interactions within and across systems, the impact of sleep on cognition, memory, motor-sensory integration, spatial navigation, large-scale computation and consciousness. Each of these topics require appropriate levels of analyses with sufficiently high temporal and spatial resolution of neuronal activity in both local and global networks, supplemented by models and theories to explain how different levels of brain dynamics interact with each other and how the failure of such interactions results in neurologic and mental disease. While such complex questions cannot be answered exhaustively by a dozen or so chapters, this volume offers a nice synthesis of current thinking and work-in-progress on micro-, meso- and macro- dynamics of the brain.

A comprehensive treatment of the skills and techniques needed for visual psychophysics, from basic tools to sophisticated data analysis. Vision is one of the most active areas in biomedical research, and visual psychophysical techniques are a foundational methodology for this research enterprise. Visual psychophysics, which studies the relationship between the physical world and human behavior, is a classical field of study that has widespread applications in modern vision science. Bridging the gap between theory and practice, this textbook provides a comprehensive treatment of visual psychophysics, teaching not only basic techniques but also sophisticated data analysis methodologies and theoretical approaches. It begins with practical information about setting up a vision lab and goes on to discuss the creation, manipulation, and display of visual images; timing and integration of displays with measurements of brain activities and other relevant techniques; experimental designs; estimation of behavioral functions; and examples of psychophysics in applied and clinical settings. The book's treatment of experimental designs presents the most commonly used psychophysical paradigms, theory-driven psychophysical experiments, and the analysis of these procedures in a signal-detection theory framework. The book discusses the theoretical underpinnings of data analysis and scientific interpretation, presenting data analysis techniques that include model fitting, model comparison, and a general framework for optimized adaptive testing methods. It includes many sample programs in Matlab with functions from Psychtoolbox, a free toolbox for real-time experimental control. Once students and researchers have mastered the material in this book, they will have the skills to apply visual psychophysics to cutting-edge vision science.

Temporal coding in the brain documents a revolution now occurring in the neurosciences. How does parallel processing of information bind together the complex nature of the outer and our inner worlds? Do intrinsic oscillations and transient cooperative states of neurons represent the physiological basis of cognitive and motor functions of the brain? Some answers to these challenging issues are provided in this book by leading world experts of brain function. A common denominator of the works presented in this volume is the nature and mechanisms of neuronal cooperation in the temporal domain. The topics range from simple organisms to the human brain. The volume is intended for investigators and graduate students in neurophysiology, cognitive neuroscience, neural computation and neurology.

Current theories of visual change detection emphasize the importance of conscious attention to detect unexpected changes in the visual environment. However, an increasing body of studies shows that the human brain is capable of detecting even small visual changes, especially if such changes violate non-conscious probabilistic expectations based on repeating experiences. In other words, our brain automatically represents statistical regularities of our visual environmental. Since the discovery of the auditory mismatch negativity (MMN) event-related potential (ERP) component, the majority of research in the field has focused on auditory deviance detection. Such automatic change detection mechanisms operate in the visual modality too, as indicated by the visual mismatch negativity (vMMN) brain potential to rare changes. VMMN is typically elicited by stimuli with infrequent (deviant) features embedded in a stream of frequent (standard) stimuli, outside the focus of attention. In this research topic we aim to present vMMN as a prediction error signal. Predictive coding theories account for phenomena such as mismatch negativity and repetition suppression, and place them in a broader context of a general theory of cortical responses. A wide range of vMMN studies has been presented in this Research Topic. Twelve articles address roughly four general sub-themes including attention, language, face processing, and psychiatric disorders. Additionally, four articles focused on particular subjects such as the oblique effect, object formation, and development and time-frequency analysis of vMMN. Furthermore, a review paper presented vMMN in a hierarchical predictive coding framework. Each paper in this Research Topic is a valuable contribution to the field of automatic visual change detection and deepens our understanding of the short term plasticity underlying predictive processes of visual perceptual learning.

Neuroscience has paid only little attention to decision-making for many years. Although no field of science has cohered around this topic, a variety of researchers in different areas of neuroscience ranging from cellular physiology to neuropsychology and computational neuroscience have been engaged in working on this issue. Thus, the time seemed to be ripe to bring these researchers together and discuss the state of the art of the neurobiology of decision-making in a broad forum. This book is a collection of contributions presented at that forum in Paris in October 1994 organized by the Fondation IPSEN.