Cell types, circuits, computation

How does the connectivity of a neuronal circuit, together with the individual properties of the cell types that take part in it, result in a given computation? We examine this question in the context of retinal circuits. We suggest that the retina can be viewed as a parallel assemblage of many small computational devices, highly stereotypical and task-specific circuits afferent to a given ganglion cell type, and we discuss some rules that govern computation in these devices. Multi-device processing in retina poses conceptual problems when it is contrasted with cortical processing. We lay out open questions both on processing in retinal circuits and on implications for cortical processing of retinal inputs.     

Continuous computation in engineered gene circuits

In this paper we consider the problem of representation and measurement in genetic circuits, and investigate how they can affect the reliability of engineered systems. We propose a design scheme, based on the notion of continuous computation, which addresses these issues. We illustrate the methodology by showing how a concept from computer architecture (namely, branch prediction) may be implemented in vivo, using a distributed approach. Simulation results confirm the in-principle feasibility of our method, and offer valuable insights into its future laboratory validation.     

Networks and circuits in cell regulation

Large-scale “omics” data are often represented as networks of interacting components, but such representation is inherently static and, as such, cannot provide a realistic picture of the temporal dynamics of complex cellular functions. These difficulties suggest moving to a modeling strategy that explicitly takes into account both the wiring of the components and the task they perform. From an engineering perspective, this problem resembles that of “circuit analysis”. In this paper, we focus on a limited but relevant biological circuit, the G1 to S transition in yeast cell cycle, and investigate both the network representation and the corresponding circuit described by a mathematical model, by means of a wide range of numerical simulation analysis. Reliable predictions of system-level properties are achieved and the parameters that mostly affect these properties are found out.     

Information processing,computation, and cognition

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects.     

Metacognition: computation, biology and function

Many complex systems maintain a self-referential check and balance. In animals, such reflective monitoring and control processes have been grouped under the rubric of metacognition. In this introductory article to a Theme Issue on metacognition, we review recent and rapidly progressing developments from neuroscience, cognitive psychology, computer science and philosophy of mind. While each of these areas is represented in detail by individual contributions to the volume, we take this opportunity to draw links between disciplines, and highlight areas where further integration is needed. Specifically, we cover the definition, measurement, neurobiology and possible functions of metacognition, and assess the relationship between metacognition and consciousness. We propose a framework in which level of representation, order of behaviour and access consciousness are orthogonal dimensions of the conceptual landscape.     

Neural correlations, population coding and computation

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.     

Kinship, Networks, and Exchange

Kinship, Networks, and Exchange. Thomas Schweizer and Douglas R. White. eds. New York; Cambridge University Press, 1997. 338 pp.     

Respiratory circuits: development, function and models

Breathing is a rhythmic motor behavior generated and controlled by hindbrain neuronal networks. Respiratory motor output arises from two distinct, but functionally interacting, rhythmogenic networks: the pre-B?tzinger complex (preB?tC) and the retrotrapezo?d nucleus/parafacial respiratory group (RTN/pFRG). This review outlines recent advances in delineating the genetic specification of the neuronal constituents of these two rhythmogenic networks, their respective roles in respiratory function and how they interact to constitute a functional respiratory circuit ensemble. The often lethal consequences of disruption to these networks found in naturally occurring developmental disorders, transgenic animals, and highly specific lesion studies are described. In addition, we discuss how recent computational models enhance our understanding of how respiratory networks generate and regulate respiratory behavior.     

Pets,Networks and Well-Being

ABSTRACT

This study explores two explanations for well-being among older adults: social network analysis and human–animal relations. The data are based on a stratified probability sample of community living adults, aged 60 years and over, located in several rural communities in north-eastern Oregon. The dependent variables include three distinct measures of psychological well-being: life satisfaction, locus of control, and self-esteem. The independent variables include pet ownership and various measures of the personal network of respondents, including network structural variables (range, density, and frequency of contact) and network content variables (both instrumental and expressive). Control variables include socio-demographic characteristics and a measure of physical health. Multiple regression analysis is used to estimate the effects of the independent and control variables. Pet ownership was related to two of the well-being measures – self-esteem and locus of control – but only for men. Some methodological and theoretical implications are discussed.     

Family,Networks and Academics

The history of anthropology is a growing field of study within the discipline itself. Our series ‘Key Informants on the History of Anthropology’ is offered as a contribution to the discussion of how anthropology, as it is understood and practised today, evolved and took shape. In this invited article, Larissa Adler Lomnitz reflects on how a cosmopolitan background aroused an interest in her in questions of social identity. Her studies of social networks in urban situations in Latin America are renowned in anthropology. Here she relates how they unfolded—and how they have been intimately connected to her family life.     

The inferior temporal cortex: Architecture, computation, and representation

Neurons in the inferior temporal cortex (IT), an area crucially involved in visual object recognition in monkeys, show the visual response properties and anatomical/chemical nature which are distinct from those in the cortical areas that feed visual inputs to the IT. Earlier physiological studies showed that IT neurons have large receptive fields covering the center and contralateral (often bilateral) visual fields, stimulus selectivity for images of complex objects or shapes, and translation invariance of the stimulus selectivity. Recent studies have revealed new aspects of their properties such as invariant selectivity for shapes despite drastic changes in various physical attributes of stimuli, latent excitatory inputs masked by stimulus-specific GABAergic inhibition, selectivity for binocular disparity and 3-dimensional surface structures, profound effects of learning on the stimulus selectivity, and columnar clustering of neurons with similarstimulus selectivity for shapes and other object features. Another line of research using histological techniques have revealed that pyramidal neurons in the IT are larger in the size of dendritic arbors, in the number of dendritic branches and spines, and in the size and distribution of horizontal axonal arbors than those in the earlier areas, allowing them to integrate a larger population of afferents and process more diverse inputs. The concentration of several neurochemicals including those related to synaptic transmission or plasticity changes systematically towards the IT along the occipitotemporal pathway. Many of the characteristics of IT neurons parallel or explain certain aspects of visual object perception, although the behavioral relevance has yet to be addressed experimentally.     

Seeing things in motion: models, circuits, and mechanisms

Motion vision provides essential cues for navigation and course control as well as for mate, prey, or predator detection. Consequently, neurons responding to visual motion in a direction-selective way are found in almost all species that see. However, directional information is not explicitly encoded at the level of a single photoreceptor. Rather, it has to be computed from the spatio-temporal excitation level of at least two photoreceptors. How this computation is done and how this computation is implemented in terms of neural circuitry and membrane biophysics have remained the focus of intense research over many decades. Here, we review recent progress made in this area with an emphasis on insects and the vertebrate retina.     

Genetic regulation networks: circuits,regulons and attractors

We deal in this paper with the concept of genetic regulation network. The genes expression observed through the bio-array imaging allows the geneticist to obtain the intergenic interaction matrix W of the network. The interaction graph G associated to W presents in general interesting features like connected components, gardens of Eden, positive and negative circuits (or loops), and minimal components having 1 positive and 1 negative loop called regulons. Depending on parameters values like the connectivity coefficient K(W) and the mean inhibition weight I(W), the genetic regulation network can present several dynamical behaviours (fixed configuration, limit cycle of configurations) called attractors, when the observation time increases. We give some examples of such genetic regulation networks and analyse their dynamical properties and their biological consequences.     

Complex, multifocal, individual-specific attention-related cortical functional circuits

Recent studies focusing on the analysis of individual patterns of non-sensory-motor CNS activity may significantly alter our view of CNS functional mapping. We have recently provided evidence for highly variable attention-related Slow Potential (SP) generating cortical areas across individuals (Basile et al., 2003, 2006). In this work, we present new evidence, searching for other physiological indexes of attention by a new use of a well established method, for individual-specific sets of cortical areas active during expecting attention. We applied latency corrected peak averaging to oscillatory bursts, from 124-channel EEG recordings, and modeled their generators by current density reconstruction. We first computed event-related total power, and averaging was based on individual patterns of narrow task-induced band-power. This method is sensitive to activity out of synchrony with stimuli, and may detect task-related changes missed by regular Event-Related Potential (ERP) averaging. We additionally analyzed overall inter-electrode phase-coherence. The main results were (1) the detection of two bands of attention-induced beta range oscillations (around 25 and 21 Hz), whose scalp topography and current density cortical distribution were complex multi-focal, and highly variable across subjects, including prefrontal and posterior cortical areas. Most important, however, was the observation that (2) the generators of task-induced oscillations are largely the same individual-specific sets of cortical areas active during the resting, baseline state. We concluded that attention-related electrical cortical activity is highly individual-specific (significantly different from sensory-related visual evoked potentials or delta and theta induced band-power), and to a great extent already established during mere wakefulness. We discuss the critical implications of those results, in combination with other studies presenting individual data, to functional mapping: the need to abandon group averaging of task-related cortical activity and to revise studies on group averaged data, since the assumption of universal function to each cortical area appears deeply challenged. Clinical implications regard the interpretation of focal lesion consequences, functional reorganization, and neurosurgical planning.     

Quantum computation, non-demolition measurements, and reflective control in living systems

Internal computation underlies robust non-equilibrium living process. The smallest details of living systems are molecular devices that realize non-demolition quantum measurements. These smaller devices form larger devices (macromolecular complexes), up to living body. The quantum device possesses its own potential internal quantum state (IQS), which is maintained for a prolonged time via reflective error-correction. Decoherence-free IQS can exhibit itself by a creative generation of iteration limits in the real world. It resembles the properties of a quasi-particle, which interacts with the surround, applying decoherence commands to it. In this framework, enzymes are molecular automata of the extremal quantum computer, the set of which maintains highly ordered robust coherent state, and genome represents a concatenation of error-correcting codes into a single reflective set. The biological evolution can be viewed as a functional evolution of measurement constraints in which limits of iteration are established, possessing criteria of perfection and having selective values.