How behavioral constraints may determine optimal sensory representations

The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron's average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population? Previous theoretical studies and related experiments suggest that many response characteristics of sensory neurons are optimal for encoding stimulus-related information. This notion, however, does not explain the two general types of tuning profiles that are commonly observed: unimodal and monotonic. Here, I quantify the efficacy of a set of tuning curves according to the possible downstream motor responses that can be constructed from them. Curves that are optimal in this sense may have monotonic or non-monotonic profiles, where the proportion of monotonic curves and the optimal tuning curve width depend on the general properties of the target downstream functions. This dependence explains intriguing features of visual cells that are sensitive to binocular disparity and of neurons tuned to echo delay in bats. The numerical results suggest that optimal sensory tuning curves are shaped not only by stimulus statistics and signal-to-noise properties, but also according to their impact on downstream neural circuits and, ultimately, on behavior.Comment: 24 pages, 9 figures (main text + supporting information

Structural adjustment programs and human rights : an examination of current research

International Financial Institutions (IFIs) play an increasingly important role in the international economy. Experiencing the hardships of economic crises, nations are able to tum to these institutions for emergency lending and aid to stabilize their economies. However, these loans often come with conditions that often require but are not limited to austerity measures, trade liberalization, or currency devaluation. As more countries tum to IFIs for crisis lending, unintended consequences may occur, especially in the area of human rights. Research disagrees on if these programs and their subsequent conditions increase human rights violations, and if so, the extent these violations are increased. Two main arguments are present in current research. The first argues that structural adjustment programs do result in an increase in human rights violations, while the second argues that structural adjustment programs actually lowers human rights violations until the cost of repayment exceeds new loans. This paper seeks to examine both empirical arguments, and then present a critique of the current research on the topic.Honors CollegeThesis (B.?

Making Decisions with Unknown Sensory Reliability

To make fast and accurate behavioral choices, we need to integrate noisy sensory input, take prior knowledge into account, and adjust our decision criteria. It was shown previously that in two-alternative-forced-choice tasks, optimal decision making can be formalized in the framework of a sequential probability ratio test and is then equivalent to a diffusion model. However, this analogy hides a “chicken and egg” problem: to know how quickly we should integrate the sensory input and set the optimal decision threshold, the reliability of the sensory observations must be known in advance. Most of the time, we cannot know this reliability without first observing the decision outcome. We consider here a Bayesian decision model that simultaneously infers the probability of two different choices and at the same time estimates the reliability of the sensory information on which this choice is based. We show that this can be achieved within a single trial, based on the noisy responses of sensory spiking neurons. The resulting model is a non-linear diffusion to bound where the weight of the sensory inputs and the decision threshold are both dynamically changing over time. In difficult decision trials, early sensory inputs have a stronger impact on the decision, and the threshold collapses such that choices are made faster but with low accuracy. The reverse is true in easy trials: the sensory weight and the threshold increase over time, leading to slower decisions but at much higher accuracy. In contrast to standard diffusion models, adaptive sensory weights construct an accurate representation for the probability of each choice. This information can then be combined appropriately with other unreliable cues, such as priors. We show that this model can account for recent findings in a motion discrimination task, and can be implemented in a neural architecture using fast Hebbian learning

A Macro-Stochastic Approach to Improved Cost Estimation for Defense Acquisition Programs

Space is becoming increasingly congested as more objects are launched into orbit. The potential for a collision on orbit increases each time a new object enters space. This thesis presents a methodology to determine an optimal direction to maneuver a satellite that may be involved in a potential collision. The author presents a paradigm to determine the optimal direction of maneuver to achieve the lowest probability of collision, and examines how different magnitudes of a maneuver will affect the probability of collision. The methodology shows that if a satellite maneuvers in the optimal direction at any time during the orbit, except incremental periods and half periods, the probability of collision is reduced to a negligible amount. This provides a means to determine a maneuver direction and magnitude that will remove satellites from the potential collision area, while minimizing the resources necessary and maintaining mission quality

Soil phosphate detection and archaeology : in-stride phosphate detection and the elimination of arsenate interference to the malachite green method.

Archaeologists use soil analysis to detect chemicals, like phosphate, to indicate areas of anthropogenic activity. Phosphate detection is a multi-step process, which makes standard techniques time consuming. Kinetic studies decreased the analysis time for the malachite green (MG) method of phosphate detection. The 3-minute method allows extraction and analysis to be complete in 15 minutes. Continued studies resulted in two-color spectral monitoring, which provided values instantaneously. Arsenate (As(V)) interfere with the MG method and results in overestimation of phosphate. As(V) must be reduced to non-interfering arsenite. Two As(V) reducing agents--L-Cysteine and thiosulfate--were investigated. The thiosulfate method was suitable for field implementation with the 3-minute malachite green method. L-Cysteine is compatible with both MG time scales, but pre-reduction could not be improved beyond 20 minutes. The 3-minute malachite green method was utilized at an archaeological site in Virginia. The survey led to delineation of the site boundaries

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Informing Spacecraft Maneuver Decisions to Reduce Probability of Collision

Space is becoming increasingly congested as more objects are launched into orbit. The potential for a collision on orbit increases each time a new object enters space. This thesis presents a methodology to determine an optimal direction to maneuver a satellite that may be involved in a potential collision. The author presents a paradigm to determine the optimal direction of maneuver to achieve the lowest probability of collision, and examines how different magnitudes of a maneuver will affect the probability of collision. The methodology shows that if a satellite maneuvers in the optimal direction at any time during the orbit, except incremental periods and half periods, the probability of collision is reduced to a negligible amount. This provides a means to determine a maneuver direction and magnitude that will remove satellites from the potential collision area, while minimizing the resources necessary and maintaining mission quality

Wellbeing among U.S. Veterans: Results from the 2010 National Survey of Veterans

Our research focuses on self-rated general health and access to healthcare among veterans. We used data collected by the 2010 National Survey of Veterans, a nationally representative survey of veterans in the U.S. The purpose is to identify and assess aspects of military experiences which could be responsible for differences in veterans’ health and their access to healthcare. Specifically, we investigate how exposure to combat, as well as exposure to specific traumas, can have a lasting impact on the health of veterans. We utilized two nested regression models around our focal variables; a logistic regression model was used to assess the access to mental healthcare, while an ordinal regression model was used to assess self-rated general health. We were also able to infer that a structural change in policies for veterans’ healthcare might have provided significant benefits among the population. Findings show unique effects on health patterns for combat and trauma in the field. Paradoxically, we also observe that many of the socio-economic indicators operated quite differently than they do for the general population in the United States in terms of their links to health differences

Cortical Variability and Challenges for Modeling Approaches.

The functional role of the observed neuronal variability (the disparity in neural responses across multiple instances of the same experiment) is again receiving close attention in Computational and Systems Neuroscience (e.g., Durstewitz et al., 2010; Moreno-Bote et al., 2011; Oram, 2011; Beck et al., 2012; Churchland and Abbott, 2012; Brunton et al., 2013; Masquelier, 2013; Mattia et al., 2013; Balaguer-Ballester et al., 2014; Renart and Machens, 2014; Bujan et al., 2015; Lin et al., 2015; Pachitariu et al., 2015; Arandia-Romero et al., 2016; Doiron et al., 2016; McDonnell et al., 2016). Special consideration is currently given to understanding how spiking (Bujan et al., 2015; Deneve and Machens, 2016; Doiron et al., 2016; Hartmann et al., 2016; Landau et al., 2016) and phenomenological (Goris et al., 2014; Lin et al., 2015; Mochol et al., 2015; Arandia-Romero et al., 2016; Doiron et al., 2016) models account for the wide range of classical and new phenomena associated with trial-to-trial uncorrelated activity. Specifically, it has often been proposed that a network state characterized by largely asynchronous spike times whilst maintaining slow oscillations in the firing-rates, may represent the default spontaneous cortical mode (e.g., Sanchez-Vives and Mattia, 2014; Deneve and Machens, 2016; Sancristobal et al., 2016); and similar states could also underlie observed stimulus-driven variability in rate (Litwin Kumar and Doiron, 2012; Deneve and Machens, 2016; Hartmann et al., 2016). However, the way in which such a computationally advantageous network state for neural coding is achieved can differ substantially between modeling approaches; this challenge will be the focus of this manuscript