9,001 research outputs found
A Local Circuit Model of Learned Striatal and Dopamine Cell Responses under Probabilistic Schedules of Reward
Before choosing, it helps to know both the expected value signaled by a predictive cue and the associated uncertainty that the reward will be forthcoming. Recently, Fiorillo et al. (2003) found the dopamine (DA) neurons of the SNc exhibit sustained responses related to the uncertainty that a cure will be followed by reward, in addition to phasic responses related to reward prediction errors (RPEs). This suggests that cue-dependent anticipations of the timing, magnitude, and uncertainty of rewards are learned and reflected in components of the DA signals broadcast by SNc neurons. What is the minimal local circuit model that can explain such multifaceted reward-related learning? A new computational model shows how learned uncertainty responses emerge robustly on single trial along with phasic RPE responses, such that both types of DA responses exhibit the empirically observed dependence on conditional probability, expected value of reward, and time since onset of the reward-predicting cue. The model includes three major pathways for computing: immediate expected values of cures, timed predictions of reward magnitudes (and RPEs), and the uncertainty associated with these predictions. The first two model pathways refine those previously modeled by Brown et al. (1999). A third, newly modeled, pathway is formed by medium spiny projection neurons (MSPNs) of the matrix compartment of the striatum, whose axons co-release GABA and a neuropeptide, substance P, both at synapses with GABAergic neurons in the SNr and with the dendrites (in SNr) of DA neurons whose somas are in ventral SNc. Co-release enables efficient computation of sustained DA uncertainty responses that are a non-monotonic function of the conditonal probability that a reward will follow the cue. The new model's incorporation of a striatal microcircuit allowed it to reveals that variability in striatal cholinergic transmission can explain observed difference, between monkeys, in the amplitutude of the non-monotonic uncertainty function. Involvement of matriceal MSPNs and striatal cholinergic transmission implpies a relation between uncertainty in the cue-reward contigency and action-selection functions of the basal ganglia. The model synthesizes anatomical, electrophysiological and behavioral data regarding the midbrain DA system in a novel way, by relating the ability to compute uncertainty, in parallel with other aspects of reward contingencies, to the unique distribution of SP inputs in ventral SN.National Science Foundation (SBE-354378); Higher Educational Council of Turkey; Canakkale Onsekiz Mart University of Turke
A distinct peak-flux distribution of the third class of gamma-ray bursts: A possible signature of X-ray flashes?
Gamma-ray bursts are the most luminous events in the Universe. Going beyond
the short-long classification scheme we work in the context of three burst
populations with the third group of intermediate duration and softest spectrum.
We are looking for physical properties which discriminate the intermediate
duration bursts from the other two classes. We use maximum likelihood fits to
establish group memberships in the duration-hardness plane. To confirm these
results we also use k-means and hierarchical clustering. We use Monte-Carlo
simulations to test the significance of the existence of the intermediate group
and we find it with 99.8% probability. The intermediate duration population has
a significantly lower peak-flux (with 99.94% significance). Also, long bursts
with measured redshift have higher peak-fluxes (with 98.6% significance) than
long bursts without measured redshifts. As the third group is the softest, we
argue that we have {related} them with X-ray flashes among the gamma-ray
bursts. We give a new, probabilistic definition for this class of events.Comment: accepted for publication in Ap
Multiplexing regulated traffic streams: design and performance
The main network solutions for supporting QoS rely on traf- fic policing (conditioning, shaping). In particular, for IP networks the IETF has developed Intserv (individual flows regulated) and Diffserv (only ag- gregates regulated). The regulator proposed could be based on the (dual) leaky-bucket mechanism. This explains the interest in network element per- formance (loss, delay) for leaky-bucket regulated traffic. This paper describes a novel approach to the above problem. Explicitly using the correlation structure of the sources’ traffic, we derive approxi- mations for both small and large buffers. Importantly, for small (large) buffers the short-term (long-term) correlations are dominant. The large buffer result decomposes the traffic stream in a stream of constant rate and a periodic impulse stream, allowing direct application of the Brownian bridge approximation. Combining the small and large buffer results by a concave majorization, we propose a simple, fast and accurate technique to statistically multiplex homogeneous regulated sources. To address heterogeneous inputs, we present similarly efficient tech- niques to evaluate the performance of multiple classes of traffic, each with distinct characteristics and QoS requirements. These techniques, applica- ble under more general conditions, are based on optimal resource (band- width and buffer) partitioning. They can also be directly applied to set GPS (Generalized Processor Sharing) weights and buffer thresholds in a shared resource system
Interpolation of nonstationary high frequency spatial-temporal temperature data
The Atmospheric Radiation Measurement program is a U.S. Department of Energy
project that collects meteorological observations at several locations around
the world in order to study how weather processes affect global climate change.
As one of its initiatives, it operates a set of fixed but irregularly-spaced
monitoring facilities in the Southern Great Plains region of the U.S. We
describe methods for interpolating temperature records from these fixed
facilities to locations at which no observations were made, which can be useful
when values are required on a spatial grid. We interpolate by conditionally
simulating from a fitted nonstationary Gaussian process model that accounts for
the time-varying statistical characteristics of the temperatures, as well as
the dependence on solar radiation. The model is fit by maximizing an
approximate likelihood, and the conditional simulations result in
well-calibrated confidence intervals for the predicted temperatures. We also
describe methods for handling spatial-temporal jumps in the data to interpolate
a slow-moving cold front.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS633 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Machine-z: Rapid Machine Learned Redshift Indicator for Swift Gamma-ray Bursts
Studies of high-redshift gamma-ray bursts (GRBs) provide important
information about the early Universe such as the rates of stellar collapsars
and mergers, the metallicity content, constraints on the re-ionization period,
and probes of the Hubble expansion. Rapid selection of high-z candidates from
GRB samples reported in real time by dedicated space missions such as Swift is
the key to identifying the most distant bursts before the optical afterglow
becomes too dim to warrant a good spectrum. Here we introduce "machine-z", a
redshift prediction algorithm and a "high-z" classifier for Swift GRBs based on
machine learning. Our method relies exclusively on canonical data commonly
available within the first few hours after the GRB trigger. Using a sample of
284 bursts with measured redshifts, we trained a randomized ensemble of
decision trees (random forest) to perform both regression and classification.
Cross-validated performance studies show that the correlation coefficient
between machine-z predictions and the true redshift is nearly 0.6. At the same
time our high-z classifier can achieve 80% recall of true high-redshift bursts,
while incurring a false positive rate of 20%. With 40% false positive rate the
classifier can achieve ~100% recall. The most reliable selection of
high-redshift GRBs is obtained by combining predictions from both the high-z
classifier and the machine-z regressor.Comment: Accepted to the Monthly Notices of the Royal Astronomical Society
Journal (10 pages, 10 figures, and 3 Tables
How Much is the Whole Really More than the Sum of its Parts? 1 + 1 = 2.5: Superlinear Productivity in Collective Group Actions
In a variety of open source software projects, we document a superlinear
growth of production () as a function of the number of active
developers , with with large dispersions. For a typical
project in this class, doubling of the group size multiplies typically the
output by a factor , explaining the title. This superlinear law is
found to hold for group sizes ranging from 5 to a few hundred developers. We
propose two classes of mechanisms, {\it interaction-based} and {\it large
deviation}, along with a cascade model of productive activity, which unifies
them. In this common framework, superlinear productivity requires that the
involved social groups function at or close to criticality, in the sense of a
subtle balance between order and disorder. We report the first empirical test
of the renormalization of the exponent of the distribution of the sizes of
first generation events into the renormalized exponent of the distribution of
clusters resulting from the cascade of triggering over all generation in a
critical branching process in the non-meanfield regime. Finally, we document a
size effect in the strength and variability of the superlinear effect, with
smaller groups exhibiting widely distributed superlinear exponents, some of
them characterizing highly productive teams. In contrast, large groups tend to
have a smaller superlinearity and less variability.Comment: 29 pages, 8 figure
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