1,072 research outputs found
Spike Clustering and Neuron Tracking over Successive Time Windows
This paper introduces a new methodology for tracking signals from individual neurons over time in multiunit extracellular recordings. The core of our strategy relies upon an extension of a traditional mixture model approach, with parameter optimization via expectation-maximimization (EM), to incorporate clustering results from the preceding time period in a Bayesian manner. EM initialization is also achieved by utilizing these prior clustering results. After clustering, we match the current and prior clusters to track persisting neurons. Applications of this spike sorting method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results
A Miniature Robot for Isolating and Tracking Neurons in Extracellular Cortical Recordings
This paper presents a miniature robot device and control algorithm that can autonomously position electrodes in cortical tissue for isolation and tracking of extracellular signals of individual neurons. Autonomous electrode positioning can significantly enhance the efficiency and quality of acute electrophysiolgical experiments aimed at basic understanding of the nervous system. Future miniaturized systems of this sort could also overcome some of the inherent difficulties in estabilishing long-lasting neural interfaces that are needed for practical realization of neural prostheses. The paper describes the robot's design and summarizes the overall structure of the control system that governs the electrode positioning process. We present a new sequential clustering algorithm that is key to improving our system's performance, and which may have other applications in robotics. Experimental results in macaque cortex demonstrate the validity of our approach
Expectation propagation on the diluted Bayesian classifier
Efficient feature selection from high-dimensional datasets is a very
important challenge in many data-driven fields of science and engineering. We
introduce a statistical mechanics inspired strategy that addresses the problem
of sparse feature selection in the context of binary classification by
leveraging a computational scheme known as expectation propagation (EP). The
algorithm is used in order to train a continuous-weights perceptron learning a
classification rule from a set of (possibly partly mislabeled) examples
provided by a teacher perceptron with diluted continuous weights. We test the
method in the Bayes optimal setting under a variety of conditions and compare
it to other state-of-the-art algorithms based on message passing and on
expectation maximization approximate inference schemes. Overall, our
simulations show that EP is a robust and competitive algorithm in terms of
variable selection properties, estimation accuracy and computational
complexity, especially when the student perceptron is trained from correlated
patterns that prevent other iterative methods from converging. Furthermore, our
numerical tests demonstrate that the algorithm is capable of learning online
the unknown values of prior parameters, such as the dilution level of the
weights of the teacher perceptron and the fraction of mislabeled examples,
quite accurately. This is achieved by means of a simple maximum likelihood
strategy that consists in minimizing the free energy associated with the EP
algorithm.Comment: 24 pages, 6 figure
Bayesian clustering and tracking of neuronal signals for autonomous neural interfaces
This paper introduces a new, unsupervised method for sorting and tracking the non-stationary spike signals of individual neurons in multi-unit extracellular recordings. While this method may be applied to a variety of problems that arise in the field of neural interfaces, its development is motivated by a new class of autonomous neural recording devices. The core of the proposed strategy relies upon an extension of a traditional expectation-maximization (EM) mixture model optimization to incorporate clustering results from the preceding recording interval in a Bayesian manner. Explicit filtering equations for the case of a Gaussian mixture are derived. Techniques using prior data to seed the EM iterations and to select the appropriate model class are also developed. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
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