270 research outputs found
Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information
Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie.Wethen determined
how the power of LFPs and spikes at different frequencies represents the visual features in the movie.Wefound that the most informative LFP frequency ranges were 1– 8 and 60 –100 Hz. LFPs in the range of 12– 40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies <12 Hz. We further quantified “signal
correlations” (correlations in the trial-averaged power response to different stimuli) and “noise correlations” (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60 –100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs<24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs<40 Hz showed very little signal and noise correlations with LFPs>40Hzand with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message
operator in expectation propagation (EP), which takes as input the set of
incoming messages to a factor node, and produces an outgoing message as output.
This learned operator replaces the multivariate integral required in classical
EP, which may not have an analytic expression. We use kernel-based regression,
which is trained on a set of probability distributions representing the
incoming messages, and the associated outgoing messages. The kernel approach
has two main advantages: first, it is fast, as it is implemented using a novel
two-layer random feature representation of the input message distributions;
second, it has principled uncertainty estimates, and can be cheaply updated
online, meaning it can request and incorporate new training data when it
encounters inputs on which it is uncertain. In experiments, our approach is
able to solve learning problems where a single message operator is required for
multiple, substantially different data sets (logistic regression for a variety
of classification problems), where it is essential to accurately assess
uncertainty and to efficiently and robustly update the message operator.Comment: accepted to UAI 2015. Correct typos. Add more content to the
appendix. Main results unchange
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals recorded at multiple electrodes. We recently introduced the MERLiN (Mixture Effect Recovery in Linear Networks) algorithm that is able to recover, from an observed linear mixture, a causal variable that is a linear effect of another given variable. Here we relax the assumption of this cause-effect relationship being linear and present an extended algorithm that can pick up non-linear cause-effect relationships. Thus, the main contribution is an algorithm (and ready to use code) that has broader applicability and allows for a richer model class. Furthermore, a comparative analysis indicates that the assumption of linear cause-effect relationships is not restrictive in analysing electroencephalographic data
Decision-Theoretic Planning with non-Markovian Rewards
A decision process in which rewards depend on history rather than merely on
the current state is called a decision process with non-Markovian rewards
(NMRDP). In decision-theoretic planning, where many desirable behaviours are
more naturally expressed as properties of execution sequences rather than as
properties of states, NMRDPs form a more natural model than the commonly
adopted fully Markovian decision process (MDP) model. While the more tractable
solution methods developed for MDPs do not directly apply in the presence of
non-Markovian rewards, a number of solution methods for NMRDPs have been
proposed in the literature. These all exploit a compact specification of the
non-Markovian reward function in temporal logic, to automatically translate the
NMRDP into an equivalent MDP which is solved using efficient MDP solution
methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process
Planner), a software platform for the development and experimentation of
methods for decision-theoretic planning with non-Markovian rewards. The current
version of NMRDPP implements, under a single interface, a family of methods
based on existing as well as new approaches which we describe in detail. These
include dynamic programming, heuristic search, and structured methods. Using
NMRDPP, we compare the methods and identify certain problem features that
affect their performance. NMRDPPs treatment of non-Markovian rewards is
inspired by the treatment of domain-specific search control knowledge in the
TLPlan planner, which it incorporates as a special case. In the First
International Probabilistic Planning Competition, NMRDPP was able to compete
and perform well in both the domain-independent and hand-coded tracks, using
search control knowledge in the latter
Learning Deep Features in Instrumental Variable Regression
Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by using an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression from the instrument to the treatment; and stage 2 performs linear regression from the treatment to the outcome, conditioned on the instrument. We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear. In this case, deep neural nets are trained to define informative nonlinear features on the instruments and treatments. We propose an alternating training regime for these features to ensure good end-to-end performance when composing stages 1 and 2, thus obtaining highly flexible feature maps in a computationally efficient manner. DFIV outperforms recent state-of-the-art methods on challenging IV benchmarks, including settings involving high dimensional image data. DFIV also exhibits competitive performance in off-policy policy evaluation for reinforcement learning, which can be understood as an IV regression task
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks. We exploit the primal-dual view of the MLE with a kinetics augmented model to obtain an estimate associated with an adversarial dual sampler. To represent this sampler, we introduce a novel neural architecture, dynamics embedding, that generalizes Hamiltonian Monte-Carlo (HMC). The proposed approach inherits the flexibility of HMC while enabling tractable entropy estimation for the augmented model. By learning both a dual sampler and the primal model simultaneously, and sharing parameters between them, we obviate the requirement to design a separate sampling procedure once the model has been trained, leading to more effective learning. We show that many existing estimators, such as contrastive divergence, pseudo/composite-likelihood, score matching, minimum Stein discrepancy estimator, non-local contrastive objectives, noise-contrastive estimation, and minimum probability flow, are special cases of the proposed approach, each expressed by a different (fixed) dual sampler. An empirical investigation shows that adapting the sampler during MLE can significantly improve on state-of-the-art estimators
Detecting Generalized Synchronization Between Chaotic Signals: A Kernel-based Approach
A unified framework for analyzing generalized synchronization in coupled
chaotic systems from data is proposed. The key of the proposed approach is the
use of the kernel methods recently developed in the field of machine learning.
Several successful applications are presented, which show the capability of the
kernel-based approach for detecting generalized synchronization. It is also
shown that the dynamical change of the coupling coefficient between two chaotic
systems can be captured by the proposed approach.Comment: 20 pages, 15 figures. massively revised as a full paper; issues on
the choice of parameters by cross validation, tests by surrogated data, etc.
are added as well as additional examples and figure
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