5,973 research outputs found
Neural connectivity in syntactic movement processing
Linguistic theory suggests non-canonical sentences subvert the dominant agent-verb-theme order in English via displacement of sentence constituents to argument (NP-movement) or non-argument positions (wh-movement). Both processes have been associated with the left inferior frontal gyrus and posterior superior temporal gyrus, but differences in neural activity and connectivity between movement types have not been investigated. In the current study, functional magnetic resonance imaging data were acquired from 21 adult participants during an auditory sentence-picture verification task using passive and active sentences contrasted to isolate NP-movement, and object- and subject-cleft sentences contrasted to isolate wh-movement. Then, functional magnetic resonance imaging data from regions common to both movement types were entered into a dynamic causal modeling analysis to examine effective connectivity for wh-movement and NP-movement. Results showed greater left inferior frontal gyrus activation for Wh > NP-movement, but no activation for NP > Wh-movement. Both types of movement elicited activity in the opercular part of the left inferior frontal gyrus, left posterior superior temporal gyrus, and left medial superior frontal gyrus. The dynamic causal modeling analyses indicated that neither movement type significantly modulated the connection from the left inferior frontal gyrus to the left posterior superior temporal gyrus, nor vice-versa, suggesting no connectivity differences between wh- and NP-movement. These findings support the idea that increased complexity of wh-structures, compared to sentences with NP-movement, requires greater engagement of cognitive resources via increased neural activity in the left inferior frontal gyrus, but both movement types engage similar neural networks.This work was supported by the NIH-NIDCD, Clinical Research Center Grant, P50DC012283 (PI: CT), and the Graduate Research Grant and School of Communication Graduate Ignition Grant from Northwestern University (awarded to EE). (P50DC012283 - NIH-NIDCD, Clinical Research Center Grant; Graduate Research Grant and School of Communication Graduate Ignition Grant from Northwestern University)Published versio
Biological Principles in Self-Organization of Young Brain - Viewed from Kohonen Model
Variants of the Kohonen model are proposed to study biological principles of
self-organization in a model of young brain. We suggest a function to measure
aquired knowledge and use it to auto-adapt the topology of neuronal
connectivity, yielding substantial organizational improvement relative to the
standard model. In the early phase of organization with most intense learning,
we observe that neural connectivity is of Small World type, which is very
efficient to organize neurons in response to stimuli. In analogy to human brain
where pruning of neural connectivity (and neuron cell death) occurs in early
life, this feature is present also in our model, which is found to stabilize
neuronal response to stimuli
Towards a Multi-Subject Analysis of Neural Connectivity
Directed acyclic graphs (DAGs) and associated probability models are widely
used to model neural connectivity and communication channels. In many
experiments, data are collected from multiple subjects whose connectivities may
differ but are likely to share many features. In such circumstances it is
natural to leverage similarity between subjects to improve statistical
efficiency. The first exact algorithm for estimation of multiple related DAGs
was recently proposed by Oates et al. 2014; in this letter we present examples
and discuss implications of the methodology as applied to the analysis of fMRI
data from a multi-subject experiment. Elicitation of tuning parameters requires
care and we illustrate how this may proceed retrospectively based on technical
replicate data. In addition to joint learning of subject-specific connectivity,
we allow for heterogeneous collections of subjects and simultaneously estimate
relationships between the subjects themselves. This letter aims to highlight
the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2
Neural Connectivity with Hidden Gaussian Graphical State-Model
The noninvasive procedures for neural connectivity are under questioning.
Theoretical models sustain that the electromagnetic field registered at
external sensors is elicited by currents at neural space. Nevertheless, what we
observe at the sensor space is a superposition of projected fields, from the
whole gray-matter. This is the reason for a major pitfall of noninvasive
Electrophysiology methods: distorted reconstruction of neural activity and its
connectivity or leakage. It has been proven that current methods produce
incorrect connectomes. Somewhat related to the incorrect connectivity
modelling, they disregard either Systems Theory and Bayesian Information
Theory. We introduce a new formalism that attains for it, Hidden Gaussian
Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden
by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS
is equivalent to a frequency domain Linear State Space Model (LSSM) but with
sparse connectivity prior. The mathematical contribution here is the theory for
high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS
can attenuate the leakage effect in the most critical case: the distortion EEG
signal due to head volume conduction heterogeneities. Its application in EEG is
illustrated with retrieved connectivity patterns from human Steady State Visual
Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence
for noninvasive procedures of neural connectivity: concurrent EEG and
Electrocorticography (ECoG) recordings on monkey. Open source packages are
freely available online, to reproduce the results presented in this paper and
to analyze external MEEG databases
Efficient Deep Feature Learning and Extraction via StochasticNets
Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1508.0546
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
Traffic prediction plays an important role in evaluating the performance of
telecommunication networks and attracts intense research interests. A
significant number of algorithms and models have been put forward to analyse
traffic data and make prediction. In the recent big data era, deep learning has
been exploited to mine the profound information hidden in the data. In
particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network
(RNN) schemes, has attracted a lot of attentions due to its capability of
processing the long-range dependency embedded in the sequential traffic data.
However, LSTM has considerable computational cost, which can not be tolerated
in tasks with stringent latency requirement. In this paper, we propose a deep
learning model based on LSTM, called Random Connectivity LSTM (RCLSTM).
Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the
formation of neural network, which is that the neurons are connected in a
stochastic manner rather than full connected. So, the RCLSTM, with certain
intrinsic sparsity, have many neural connections absent (distinguished from the
full connectivity) and which leads to the reduction of the parameters to be
trained and the computational cost. We apply the RCLSTM to predict traffic and
validate that the RCLSTM with even 35% neural connectivity still shows a
satisfactory performance. When we gradually add training samples, the
performance of RCLSTM becomes increasingly closer to the baseline LSTM.
Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits
even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure
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