Catálogo de publicaciones - libros

In memory, the continuous flow of experience is punctuated at meaningful boundaries between one episode and the next. When salient events are separated by increasing amounts of space or time, memory systems can accommodate in two ways. One option is to increase the amount of neural resources devoted to longer event segments. The other is to maintain the same neural resources with sacrificed spatiotemporal resolution. Here we review how the spatial coding system is affected by the segmentation of space by goals and boundaries. We argue that the resolution of the place code is dictated by the amount of space encoded within periods of theta. Thus, the theta cycle is viewed as a ‘neural word’ that segregates segments of space and its cognitive equivalents (memory, planning). In support of this conclusion, we report that, as rats traverse a linear track, the beginning of a journey is represented at the falling phase of theta whereas the journey’s end is represented on the ascending phase. The current location is represented in the temporal context of the past and future event boundaries. These results are discussed in relation to the changes in physiology observed across the longitudinal axis of the hippocampus, with a special consideration for how sequence information could be integrated by downstream ‘reader’ neurons.

Some 320 million years ago (MYA), the evolution of a protective membrane surrounding the embryo, the amnion, enabled vertebrates to develop outside water and thus invade new terrestrial niches. These amniotes were the ancestors of today’s mammals and sauropsids (reptiles and birds). Present-day reptiles are a diverse group of more than 10,000 species that comprise the sphenodon, lizards, snakes, turtles and crocodilians. Although turtles were once thought to be the most “primitive” among the reptiles, current genomic data point toward two major groupings: the Squamata (lizards and snakes) and a group comprising both the turtles and the Archosauria (dinosaurs and modern birds and crocodiles). Dinosaurs inhabited the Earth from the Triassic (230 MYA), at a time when the entire landmass formed a single Pangaea. Dinosaurs flourished from the beginning of the Jurassic to the mass extinction at the end of the Cretaceous (65 MYA), and birds are their only survivors. What people generally call reptiles is thus a group defined in part by exclusion: it gathers amniote species that are neither mammals nor birds, making the reptiles technically a paraphyletic grouping. Despite this, the so-defined reptiles share many evolutionary, anatomical, developmental, physiological (e.g., ectothermia), and functional features. It is thus reasonable to talk about a “reptilian brain.”

Motor planning allows us to conceive, plan, and initiate skilled motor behaviors. Motor planning involves activity distributed widely across the cortex. How this activity dynamically comes together to guide movement remains an unsolved problem. We study motor planning in mice performing a tactile decision behavior. Head-fixed mice discriminate object locations with their whiskers and report their choice by directional licking (“lick left”/“lick right”). A short-term memory component separates tactile “sensation” and “action” into distinct epochs. Using loss-of-function experiments, cell-type specific electrophysiology, and cellular imaging, we delineate when and how activity in specific brain areas and cell types drives motor planning in mice. Our results suggest that information flows serially from sensory to motor areas during motor planning. The motor cortex circuit maintains the motor plan during short-term memory and translates the motor plan into motor commands that drive the upcoming directional licking.

Information coding in sensory neurons is both digital, in terms of neuronal output spike timing and rate, and analog, produced by the irregular subthreshold changes in somatic and dendritic membrane potential resulting from synchronized volleys of synaptic inputs. Intracellular recordings give a unique access to a composite multiscale signal where the local microscopic integration process realized by a single neuron can be studied in the global mesoscopic context of the “unseen” units afferent to the recorded cell. This chapter shows how reverse engineering approaches can be used in the primary visual cortex of higher mammals to reveal the hidden complexity of visual processing and establish causal links between the functional dynamics of synaptic echoes in primary visual cortex and perceptual biases in low-level, non-attentive perception.

The cortical circuit for spatial representation has multiple functionally distinct components, each dedicated to a highly specific aspect of spatial processing. The circuit includes place cells in the hippocampus as well as grid cells, head direction cells and border cells in the medial entorhinal cortex. In this review we discuss the functional organization of the hippocampal-entorhinal space circuit. We shall review data suggesting that the circuit of grid cells has a modular organization and we will discuss principles by which individual modules of grid cells interact with geometric features of the external environment. We shall argue that the modular organization of the grid-cell system may be instrumental in memory orthogonalization in place cells in the hippocampus. Taken together, these examples illustrate a brain system that performs computations at the highest level, yet remains one of the cortical circuits with the best readout for experimental analysis and intervention.

Our behaviors range from mindful, deliberative streams of action to sequences of action that are so nearly automatic that we can perform them almost without thinking. Transitions between these modes of behavior occur as we learn behavioral routines. We have studied these transitions and the neural activity that occurs in corticostriatal loops as they take place. We find that neural activity in these loops is strongly modified during habit learning and that specific corticostriatal circuits can powerfully control value-based decision-making and habits.

Parsing a cognitive task into a sequence of successive operations is a central problem in cognitive neuroscience. A major advance is now possible thanks to the application of pattern classifiers to time-resolved recordings of brain activity [electro-encephalography (EEG), magneto-encephalography (MEG), or intracranial recordings]. The method determines precisely when a specific mental content becomes explicitly represented in brain activity. Most importantly, the ability of these pattern classifiers to generalize across time and experimental conditions sheds light on the temporal organization of information-processing stages. To illustrate these ideas, we show how the decoding of MEG and EEG recordings can be used to track the fate of conscious and unconscious brain processes during simple masking and auditory novelty tasks. The experimental results yield converging results, suggesting that conscious perception is associated with the late formation of a distributed and stable neural assembly that encodes the content of subjective perception.

Sleep is universal, tightly regulated, and many cognitive functions are impaired if we do not sleep. But why? Why do our brains need to disconnect from the environment for hours every day? We discuss here the synaptic homeostasis hypothesis (SHY), which proposes that sleep is the price the brain pays for plasticity, to consolidate what we already learned, and be ready to learn new things the next day. In brief, new experiments show that the net strength of synapses increases with wake and decreases with sleep. As we discuss, these findings can explain why sleep is necessary for the well-being of neural cells and brain circuits, and how the regulation of synaptic strength may be a universal, essential function of sleep.

For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying understanding bridging between the micro-, meso-, and macro-scales of the brain and psychology. This chapter outlines an integrated view of the neurophysiological systems, psychophysical signals, and theoretical considerations related to consciousness. First, we review the signals that correlate to consciousness during psychophysics experiments. We then review the underlying neural mechanisms giving rise to these signals. Finally, we discuss the computational and theoretical functions of such neural mechanisms, and begin to outline means in which these are related to ongoing theoretical research.

Our everyday professional and personal lives are irrevocably affected by technologies that search and understand the meaning of data, that store and preserve important information, and that automate complex computations through algorithmic abstraction. People increasingly rely on products from computer companies such as Google, Apple, Microsoft and IBM, not to mention their spinoffs, apps, WiFi, iCloud, HTML, smartphones and the like. Countless daily tasks and habits, from shopping to reading, entertainment, learning and the visual arts, have been profoundly altered by this technological revolution. Science has also benefited from this rapid progress in the field of information and computer science and associated technologies (ICT). For example, the tentative confirmation of the existence of the Higgs boson (CMS Collaboration et al. Phys Lett B 716:30–61, 2012), made through a combination of heavy industrial development, internet-based scientific communication and collaboration, with data federation, integration, mining and analysis (Rajasekar et al. iRODS primer: integrated rule-oriented data system. Synthesis lectures on information concepts, retrieval, and services. Morgan & Claypool, San Rafael, 2010; Chiang et al. BMC Bioinformatics 12:361, 2011; Marks. New Sci 196:28–29, 2007), has taken our understanding of the structure of inorganic matter to a new level (Hay et al. The fourth paradigm: data-intensive scientific discovery. Microsoft, Redmond, WA, 2009). But within this vision of universal progress, there is one anomaly: the relatively poor exploitation and application of new ICT techniques in the context of the clinical neurosciences. A pertinent example is the genetic study of brain diseases and associated bioinformatics methods. Despite a decade of work on clinically well-defined cohorts, disappointment remains among some that genome-wide association studies (GWAS) have not solved many questions of disease causation, especially in psychiatry (Goldstein. N Engl J Med 360:1696–1698, 2009). One question is whether we have the appropriate disease categories. Another factor is that gene expression is affected by environmental and endogenous factors, as is protein function in different circumstances (think of the effects of age, developmental stage and nutrition). It is clear that any genetic associations with disease expression are likely to be highly complex. Why then are the world’s most powerful supercomputers not being deployed with novel algorithms grounded in complexity mathematics to identify biologically homogeneous disease types, or to understand the many interactions that lead to the integrated functions that arise from DNA metabolism, such as cognition? Is it from a lack of appropriate data and methods or are the reasons related to our current clinical scientific culture?