47,136 research outputs found
Fast, scalable, Bayesian spike identification for multi-electrode arrays
We present an algorithm to identify individual neural spikes observed on
high-density multi-electrode arrays (MEAs). Our method can distinguish large
numbers of distinct neural units, even when spikes overlap, and accounts for
intrinsic variability of spikes from each unit. As MEAs grow larger, it is
important to find spike-identification methods that are scalable, that is, the
computational cost of spike fitting should scale well with the number of units
observed. Our algorithm accomplishes this goal, and is fast, because it
exploits the spatial locality of each unit and the basic biophysics of
extracellular signal propagation. Human intervention is minimized and
streamlined via a graphical interface. We illustrate our method on data from a
mammalian retina preparation and document its performance on simulated data
consisting of spikes added to experimentally measured background noise. The
algorithm is highly accurate
Online Unsupervised Multi-view Feature Selection
In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods
Home alone: autonomous extension and correction of spatial representations
In this paper we present an account
of the problems faced by a mobile robot given
an incomplete tour of an unknown environment,
and introduce a collection of techniques which can
generate successful behaviour even in the presence
of such problems. Underlying our approach is the
principle that an autonomous system must be motivated
to act to gather new knowledge, and to validate
and correct existing knowledge. This principle is
embodied in Dora, a mobile robot which features
the aforementioned techniques: shared representations,
non-monotonic reasoning, and goal generation
and management. To demonstrate how well this
collection of techniques work in real-world situations
we present a comprehensive analysis of the Dora
system’s performance over multiple tours in an indoor
environment. In this analysis Dora successfully
completed 18 of 21 attempted runs, with all but
3 of these successes requiring one or more of the
integrated techniques to recover from problems
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