Flutter discrimination: neural codes,perception, memory and decision making

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.     

Variance as a signature of neural computations during decision making

Traditionally, insights into neural computation have been furnished by averaged firing rates from many stimulus repetitions or trials. We pursue an analysis of neural response variance to unveil neural computations that cannot be discerned from measures of average firing rate. We analyzed single-neuron recordings from the lateral intraparietal area (LIP), during a perceptual decision-making task. Spike count variance was divided into two components using the law of total variance for doubly stochastic processes: (1) variance of counts that would be produced by a stochastic point process with a given rate, and loosely (2) the variance of the rates that would produce those counts (i.e., "conditional expectation"). The variance and correlation of the conditional expectation exposed several neural mechanisms: mixtures of firing rate states preceding the decision, accumulation of stochastic "evidence" during decision formation, and a stereotyped response at decision end. These analyses help to differentiate among several alternative decision-making models.     

A plausible neural circuit for decision making and its formation based on reinforcement learning

A human’s, or lower insects’, behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit’s constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit’s output layer, which generates a stable spike firing rate to encode flight commands, controls the insect’s angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit’s information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee’s behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit’s generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.     

Dual equipoise shared decision making: definitions for decision and behaviour support interventions

Background

In the experience of health professionals, it appears that interacting with peers in the workplace fosters learning and information sharing. Informal groups and networks present good opportunities for information exchange. Communities of practice (CoPs), which have been described by Wenger and others as a type of informal learning organization, have received increasing attention in the health care sector; however, the lack of uniform operating definitions of CoPs has resulted in considerable variation in the structure and function of these groups, making it difficult to evaluate their effectiveness.

Objective

To critique the evolution of the CoP concept as based on the germinal work by Wenger and colleagues published between 1991 and 2002.

Discussion

CoP was originally developed to provide a template for examining the learning that happens among practitioners in a social environment, but over the years there have been important divergences in the focus of the concept. Lave and Wenger's earliest publication (1991) centred on the interactions between novices and experts, and the process by which newcomers create a professional identity. In the 1998 book, the focus had shifted to personal growth and the trajectory of individuals' participation within a group (i.e., peripheral versus core participation). The focus then changed again in 2002 when CoP was applied as a managerial tool for improving an organization's competitiveness.

Summary

The different interpretations of CoP make it challenging to apply the concept or to take full advantage of the benefits that CoP groups may offer. The tension between satisfying individuals' needs for personal growth and empowerment versus an organization's bottom line is perhaps the most contentious of the issues that make CoPs difficult to cultivate. Since CoP is still an evolving concept, we recommend focusing on optimizing specific characteristics of the concept, such as support for members interacting with each other, sharing knowledge, and building a sense of belonging within networks/teams/groups. Interventions that facilitate relationship building among members and that promote knowledge exchange may be useful for optimizing the function of these groups.     

Rough sets and genetic algorithms in learning cellular neural networks cloning template for decision making system

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.     

Left-right asymmetry: making the right decision early

A left-right asymmetry in neuronal function is specified surprisingly early during embryogenesis in Caenorhabditis elegans. Do early cues influence left-right asymmetries in other animals? How are early cues remembered until late in development?     

Economic valuation of kelp forests in northern Chile: values of goods and services of the ecosystem

Kelp beds, besides being one of the most important benthic resources in northern Chile, provide a variety of environmental goods and services. In order to evaluate economically the wild kelp populations in northern Chile (26° to 32° S) more than simply their commercial value as a source of raw materials for alginate extraction, we used several economic indicators to holistically assess the value of a group of brown seaweeds of economic importance, Lessonia spp. and Macrocystis pyrifera: (1) market value of biomass as a source of raw material for extraction of alginic acid, (2) market value of associated species of economic importance, (3) value as a source of scientific information, (4) value as a climate buffer (CO₂ capture and release of O₂), (5) value of associated biodiversity (non-commercial species), (6) value as cultural heritage and (7) value as a reservoir of biodiversity. Existence values of kelp beds which estimate the willingness of citizens to pay and work without payment to preserve the ecosystem were calculated using the contingent valuation technique. The results indicate that kelp beds in northern Chile have a total value of US $540 million. Of this total, kelp fishery accounts for 75 % and associated-species fisheries account for 15 %. In this context, the economic value of Chilean kelp beds is mainly associated with the industry of alginate extraction. By contrast, existence value as a source of scientific information or environmental buffer for CO₂ capture or O₂ production represents only 9 % of the total value, representing a very low relative importance to society. The economic valuation of coastal resources and marine ecosystems is a complementary tool for decision making and implementation of public policies related to the conservation and sustainable exploitation of renewable resources and their ecosystems.     

Neuroethology of reward and decision making

Ethology, the evolutionary science of behaviour, assumes that natural selection shapes behaviour and its neural substrates in humans and other animals. In this view, the nervous system of any animal comprises a suite of morphological and behavioural adaptations for solving specific information processing problems posed by the physical or social environment. Since the allocation of behaviour often reflects economic optimization of evolutionary fitness subject to physical and cognitive constraints, neurobiological studies of reward, punishment, motivation and decision making will profit from an appreciation of the information processing problems confronted by animals in their natural physical and social environments.     

A framework for studying the neurobiology of value-based decision making

Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.     

Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control

Numerous psychophysical studies suggest that the sensorimotor system chooses actions that optimize the average cost associated with a movement. Recently, however, violations of this hypothesis have been reported in line with economic theories of decision-making that not only consider the mean payoff, but are also sensitive to risk, that is the variability of the payoff. Here, we examine the hypothesis that risk-sensitivity in sensorimotor control arises as a mean-variance trade-off in movement costs. We designed a motor task in which participants could choose between a sure motor action that resulted in a fixed amount of effort and a risky motor action that resulted in a variable amount of effort that could be either lower or higher than the fixed effort. By changing the mean effort of the risky action while experimentally fixing its variance, we determined indifference points at which participants chose equiprobably between the sure, fixed amount of effort option and the risky, variable effort option. Depending on whether participants accepted a variable effort with a mean that was higher, lower or equal to the fixed effort, they could be classified as risk-seeking, risk-averse or risk-neutral. Most subjects were risk-sensitive in our task consistent with a mean-variance trade-off in effort, thereby, underlining the importance of risk-sensitivity in computational models of sensorimotor control.