193 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
Efficient Data Representation by Selecting Prototypes with Importance Weights
Prototypical examples that best summarizes and compactly represents an
underlying complex data distribution communicate meaningful insights to humans
in domains where simple explanations are hard to extract. In this paper we
present algorithms with strong theoretical guarantees to mine these data sets
and select prototypes a.k.a. representatives that optimally describes them. Our
work notably generalizes the recent work by Kim et al. (2016) where in addition
to selecting prototypes, we also associate non-negative weights which are
indicative of their importance. This extension provides a single coherent
framework under which both prototypes and criticisms (i.e. outliers) can be
found. Furthermore, our framework works for any symmetric positive definite
kernel thus addressing one of the key open questions laid out in Kim et al.
(2016). By establishing that our objective function enjoys a key property of
that of weak submodularity, we present a fast ProtoDash algorithm and also
derive approximation guarantees for the same. We demonstrate the efficacy of
our method on diverse domains such as retail, digit recognition (MNIST) and on
publicly available 40 health questionnaires obtained from the Center for
Disease Control (CDC) website maintained by the US Dept. of Health. We validate
the results quantitatively as well as qualitatively based on expert feedback
and recently published scientific studies on public health, thus showcasing the
power of our technique in providing actionability (for retail), utility (for
MNIST) and insight (on CDC datasets) which arguably are the hallmarks of an
effective data mining method.Comment: Accepted for publication in International Conference on Data Mining
(ICDM) 201
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
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
Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior
Prisoner's Dilemma mainly treat the choice to cooperate or defect as an
atomic action. We propose to study online learning algorithm behavior in the
Iterated Prisoner's Dilemma (IPD) game, where we explored the full spectrum of
reinforcement learning agents: multi-armed bandits, contextual bandits and
reinforcement learning. We have evaluate them based on a tournament of iterated
prisoner's dilemma where multiple agents can compete in a sequential fashion.
This allows us to analyze the dynamics of policies learned by multiple
self-interested independent reward-driven agents, and also allows us study the
capacity of these algorithms to fit the human behaviors. Results suggest that
considering the current situation to make decision is the worst in this kind of
social dilemma game. Multiples discoveries on online learning behaviors and
clinical validations are stated.Comment: To the best of our knowledge, this is the first attempt to explore
the full spectrum of reinforcement learning agents (multi-armed bandits,
contextual bandits and reinforcement learning) in the sequential social
dilemma. This mental variants section supersedes and extends our work
arXiv:1706.02897 (MAB), arXiv:2005.04544 (CB) and arXiv:1906.11286 (RL) into
the multi-agent settin
Predicting human decision making in psychological tasks with recurrent neural networks
Unlike traditional time series, the action sequences of human decision making
usually involve many cognitive processes such as beliefs, desires, intentions
and theory of mind, i.e. what others are thinking. This makes predicting human
decision making challenging to be treated agnostically to the underlying
psychological mechanisms. We propose to use a recurrent neural network
architecture based on long short-term memory networks (LSTM) to predict the
time series of the actions taken by the human subjects at each step of their
decision making, the first application of such methods in this research domain.
In this study, we collate the human data from 8 published literature of the
Iterated Prisoner's Dilemma comprising 168,386 individual decisions and
postprocess them into 8,257 behavioral trajectories of 9 actions each for both
players. Similarly, we collate 617 trajectories of 95 actions from 10 different
published studies of Iowa Gambling Task experiments with healthy human
subjects. We train our prediction networks on the behavioral data from these
published psychological experiments of human decision making, and demonstrate a
clear advantage over the state-of-the-art methods in predicting human decision
making trajectories in both single-agent scenarios such as the Iowa Gambling
Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the
prediction, we observe that the weights of the top performers tends to have a
wider distribution, and a bigger bias in the LSTM networks, which suggests
possible interpretations for the distribution of strategies adopted by each
group
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
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