3,160 research outputs found
Python for Large-Scale Electrophysiology
Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation (“dimstim”); one for electrophysiological waveform visualization and spike sorting (“spyke”); and one for spike train and stimulus analysis (“neuropy”). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience
A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task
A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory
Bifurcations in valveless pumping techniques from a coupled fluid-structure-electrophysiology model in heart development
We explore an embryonic heart model that couples electrophysiology and
muscle-force generation to flow induced using a fluid-structure
interaction framework based on the immersed boundary method. The propagation of
action potentials are coupled to muscular contraction and hence the overall
pumping dynamics. In comparison to previous models, the electro-dynamical model
does not use prescribed motion to initiate the pumping motion, but rather the
pumping dynamics are fully coupled to an underlying electrophysiology model,
governed by the FitzHugh-Nagumo equations. Perturbing the diffusion parameter
in the FitzHugh-Nagumo model leads to a bifurcation in dynamics of action
potential propagation. This bifurcation is able to capture a spectrum of
different pumping regimes, with dynamic suction pumping and peristaltic-like
pumping at the extremes. We find that more bulk flow is produced within the
realm of peristaltic-like pumping.Comment: 11 pages, 13 figures. arXiv admin note: text overlap with
arXiv:1610.0342
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model
Recurrent major mood episodes and subsyndromal mood instability cause
substantial disability in patients with bipolar disorder. Early identification
of mood episodes enabling timely mood stabilisation is an important clinical
goal. Recent technological advances allow the prospective reporting of mood in
real time enabling more accurate, efficient data capture. The complex nature of
these data streams in combination with challenge of deriving meaning from
missing data mean pose a significant analytic challenge. The signature method
is derived from stochastic analysis and has the ability to capture important
properties of complex ordered time series data. To explore whether the onset of
episodes of mania and depression can be identified using self-reported mood
data.Comment: 12 pages, 3 tables, 10 figure
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