Neural Data Analysis on iBOTS Group - Research Software Engineering Training

Local Field Potential (LFP) Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Local field potentials (LFPs) are extracellular voltage signals generated by the combined transmembrane currents of neuronal populations. Their interpretation depends on understanding how recording conditions, preprocessing choices, and nearby neural processes shape the signal. This topic connects LFP analysis with spike and behavioral data.

Why This Matters

Reliable LFP analysis requires attention to signal quality, neurophysiological interpretation, and reproducible computation. Filtering choices, line noise, synchronization, reference schemes, and behavioral alignment all influence power spectra and coherence estimates. This topic connects analytical methods with the practical checks needed to interpret results with confidence.

Calcium Imaging Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Calcium imaging produces movies in which changes in fluorescence provide an indirect measure of neural activity. Transforming raw movies into interpretable signals involves motion correction, region selection, trace extraction, neuropil correction, and careful interpretation of the relationship between fluorescence and neural spiking.

Why This Matters:

Calcium imaging analysis involves a chain of interdependent decisions, including file conversion, downsampling, motion correction, segmentation, neuropil correction, trace extraction, and quality control. Each decision affects subsequent results and what can be interpreted from them. This topic helps you understand the full workflow, compare open-source tools, and document analysis choices so that results remain traceable from raw movies to final figures.

Neural Spike Analysis

Mon, 01 Jan 0001 00:00:00 +0000

How do we extract, measure, and model spike trains from neuronal recordings? Using real and simulated electrophysiological data, you will investigate the firing patterns of individual neurons and neural populations. You will explore descriptive techniques such as raster plots, peristimulus time histograms (PSTHs), and cross-correlograms, alongside statistical approaches including Poisson-based generative models and coherence-based analyses.

Why This Matters

Spike analysis turns raw voltage traces into interpretable measurements of neural activity, so every step matters: filtering, detection, sorting, quality control, alignment, summary statistics, and modeling. Early processing decisions affect raster plots, PSTHs, cross-correlograms, and population-level interpretations. This topic connects an intuitive understanding of the methods with reproducible Python workflows.

(M)EEG Analysis

Mon, 01 Jan 0001 00:00:00 +0000

EEG and MEG provide non-invasive views of neural activity with millisecond-scale temporal resolution. EEG measures electrical potential differences at the scalp, whereas MEG measures magnetic fields associated with neural currents. In both modalities, each sensor captures a mixture of activity from multiple neural sources, making the relationship between recorded signals and underlying processes an important part of the analysis.

Why This Matters

Analyzing EEG and MEG data requires careful alignment of experimental events, sensor recordings, preprocessing decisions, and statistical interpretations. MNE-Python provides a powerful open-source toolkit, but scientifically sound conclusions depend on understanding how choices about artifact handling, epoching, time-frequency analysis, source separation, connectivity, and decoding affect the results. This topic connects these concepts and theoretical foundations to transparent, reproducible computational workflows.