97 research outputs found
Dynamical and Statistical Criticality in a Model of Neural Tissue
For the nervous system to work at all, a delicate balance of excitation and
inhibition must be achieved. However, when such a balance is sought by global
strategies, only few modes remain balanced close to instability, and all other
modes are strongly stable. Here we present a simple model of neural tissue in
which this balance is sought locally by neurons following `anti-Hebbian'
behavior: {\sl all} degrees of freedom achieve a close balance of excitation
and inhibition and become "critical" in the dynamical sense. At long
timescales, the modes of our model oscillate around the instability line, so an
extremely complex "breakout" dynamics ensues in which different modes of the
system oscillate between prominence and extinction. We show the system develops
various anomalous statistical behaviours and hence becomes self-organized
critical in the statistical sense
Context Attentive Bandits: Contextual Bandit with Restricted Context
We consider a novel formulation of the multi-armed bandit model, which we
call the contextual bandit with restricted context, where only a limited number
of features can be accessed by the learner at every iteration. This novel
formulation is motivated by different online problems arising in clinical
trials, recommender systems and attention modeling. Herein, we adapt the
standard multi-armed bandit algorithm known as Thompson Sampling to take
advantage of our restricted context setting, and propose two novel algorithms,
called the Thompson Sampling with Restricted Context(TSRC) and the Windows
Thompson Sampling with Restricted Context(WTSRC), for handling stationary and
nonstationary environments, respectively. Our empirical results demonstrate
advantages of the proposed approaches on several real-life datasetsComment: IJCAI 201
Noise-induced memory in extended excitable systems
We describe a form of memory exhibited by extended excitable systems driven
by stochastic fluctuations. Under such conditions, the system self-organizes
into a state characterized by power-law correlations thus retaining long-term
memory of previous states. The exponents are robust and model-independent. We
discuss novel implications of these results for the functioning of cortical
neurons as well as for networks of neurons.Comment: 4 pages, latex + 5 eps figure
Topological Effects of Synaptic Time Dependent Plasticity
We show that the local Spike Timing-Dependent Plasticity (STDP) rule has the
effect of regulating the trans-synaptic weights of loops of any length within a
simulated network of neurons. We show that depending on STDP's polarity,
functional loops are formed or eliminated in networks driven to normal spiking
conditions by random, partially correlated inputs, where functional loops
comprise weights that exceed a non-zero threshold. We further prove that STDP
is a form of loop-regulating plasticity for the case of a linear network
comprising random weights drawn from certain distributions. Thus a notable
local synaptic learning rule makes a specific prediction about synapses in the
brain in which standard STDP is present: that under normal spiking conditions,
they should participate in predominantly feed-forward connections at all
scales. Our model implies that any deviations from this prediction would
require a substantial modification to the hypothesized role for standard STDP.
Given its widespread occurrence in the brain, we predict that STDP could also
regulate long range synaptic loops among individual neurons across all brain
scales, up to, and including, the scale of global brain network topology.Comment: 26 pages, 5 figure
Computational Models of Adult Neurogenesis
Experimental results in recent years have shown that adult neurogenesis is a
significant phenomenon in the mammalian brain. Little is known, however, about
the functional role played by the generation and destruction of neurons in the
context of and adult brain. Here we propose two models where new projection
neurons are incorporated. We show that in both models, using incorporation and
removal of neurons as a computational tool, it is possible to achieve a higher
computational efficiency that in purely static, synapse-learning driven
networks. We also discuss the implication for understanding the role of adult
neurogenesis in specific brain areas.Comment: To appear Physica A, 7 page
Perceptual basis of evolving Western musical styles
The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation.Facultad de Bellas Arte
Perceptual basis of evolving Western musical styles
The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation.Facultad de Bellas Arte
Response Time Distributions in Rapid Chess: A Large-Scale Decision Making Experiment
Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation
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