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Deep brain stimulation is an effective therapy for Parkinson's disease (PD) that has enabled microelectrode recordings from single-unit cells in the sub-thalamic nucleus (STN) of the basal ganglia. This rare data is important to develop detailed characterizations of spiking activity to understand the pathophysiology of PD. Despite the point process nature of neuronal spiking activity, point process...
Identification of multiple simultaneously recorded neural spike train recordings is an important task in understanding neuronal dependency, functional connectivity, and temporal causality in neural systems. An assessment of the functional connectivity in a group of ensemble cells was performed using a regularized point process generalized linear model (GLM) that incorporates temporal smoothness or...
Multiphoton calcium fluorescence imaging has gained prominence as a valuable tool for the study of brain cells, but the corresponding analytical regimes remain rather naive. In this paper, we develop a statistical framework that facilitates principled quantitative analysis of multiphoton images. The proposed methods discriminate the stimulus-evoked response of a neuron from the background firing and...
In this paper, we compare and validate different probabilistic models of human heart beat intervals for assessment of the electrocardiogram data recorded with varying conditions in posture and pharmacological autonomic blockade. The models are validated using the adaptive point process filtering paradigm and Kolmogorov-Smirnov test. The inverse Gaussian model was found to achieve the overall best...
Neural systems encode representations of biological signals in the firing patterns of their spike trains. Spike trains are point process time-series and their codes are both dynamic and stochastic. Although the signal is often continuous, its representation in the nervous systems is as a high-dimensional point process time-series. Because neural spike trains are point processes, standard signal processing...
Learning is a dynamic process generally defined as a change in behavior as a result of experience. Understanding how processes at the molecular and neuronal levels integrate so that an organism can learn is a central question in neuroscience. Most learning experiments consist of a sequence of trials. During each trial, a subject is given a fixed amount of time to execute a task and the resulting performance...
Motor prosthetic algorithms were recently proposed to combine target and path information to drive reaching arm movements to a static goal. In this paper, we extend two approaches to support goals that may themselves evolve over the duration of the reaching movement. The resulting probabilistic and control-based dynamic-goal reach state equations represent an intermediate level of user flexibility...
This paper addresses the problem of estimating reaching movements. We derive a Bayesian-optimal discrete-time state equation to support real-time filters that incorporate observations about the target position and arm trajectory. The resulting algorithm is compatible with any filtering method, such as point process or Kalman filters, and any recording modality, such as multielectrode arrays, intracortical...
One of the many challenges in long-term decoding from chronically implanted electrodes involves tracking changes in the firing properties of the neural ensemble while simultaneously reconstructing the desired signal. We provide an approach to this problem based on adaptive point process filtering. In particular, we construct a lock-step adaptive filter built upon stochastic models for: a) the receptive...
Developing optimal strategies for constructing and testing decoding algorithms is an important question in computational neuroscience, In this field, decoding algorithms are mathematical methods that model ensemble neural spiking activity as they dynamically represent a biological signal. We present a recursive decoding algorithm based on a Bayesian point process model of individual neuron spiking...
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