Calcium imaging¶
Curriculum status
These entries are architecture specifications, not final lesson plans. They define teaching intent, logistics, outputs and scientific boundaries; release-ready settings, worksheets, notebooks and answer keys still require empirical validation.
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Family source cluster: [P2; O3; T2 Ch.6; G6]
IMG-01 — Spikes to calcium to fluorescence¶
| Concept / theme | The imaging GUI is a forward model from known electrical events to indirect fluorescence observations. |
|---|---|
| Audience / context | Undergraduate. Formal practical, workshop or modular course. Prior modules: DAT-03 helpful. |
| Logistics | 45 min; 1–3; boards: 0–1; software: Spikeling imaging GUI; Jupyter; prepared dataset: GUI-generated or prepared imaging traces. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | GUI simulation workflow. Stages: 6–11. |
| Spikeling relationship | 6 — GUI simulation workflow; 2 — Direct board + GUI; 7 — Conceptual analogy |
Learning outcomes.
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Identify and predict the principal behaviour described in the imaging GUI is a forward model from known electrical events to indirect fluorescence observations.
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Configure or document drive the pipeline from board/emulator and identify each transformation stage and record Vm, detected spikes, calcium and fluorescence.
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Measure, calculate or compare drive the pipeline from board/emulator and identify each transformation stage using an explicit operational rule.
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Interpret the result and state why synthetic calcium/fluorescence does not validate a real indicator or optical system
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Drive the pipeline from board/emulator and identify each transformation stage.
4. Acquire or inspect Vm, detected spikes, calcium and fluorescence.
5. Produce forward-model diagram and aligned traces.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Synthetic calcium/fluorescence does not validate a real indicator or optical system.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate drive the pipeline from board/emulator and identify each transformation stage. The reusable output is forward-model diagram and aligned traces.
Sources / connections / priority. Sources: [P2; O3; T2 Ch.6; G6]. Natural follow-ons: IMG-02, IMG-03. Development priority: Core module. Boundary: Synthetic calcium/fluorescence does not validate a real indicator or optical system.
IMG-02 — Frame rate and temporal filtering¶
| Concept / theme | Camera sampling and indicator kinetics blur and discretise fast electrical events. |
|---|---|
| Audience / context | Advanced undergraduate. Formal practical, workshop or modular course. Prior modules: IMG-01, EPH-04. |
| Logistics | 60 min; 1–3; boards: 0–1; software: Spikeling imaging GUI; Jupyter; prepared dataset: GUI-generated or prepared imaging traces. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | GUI + Jupyter. Stages: 6–11. |
| Spikeling relationship | 6 — GUI simulation workflow; 5 — Recorded-dataset analysis; 8 — External computational comparison |
Learning outcomes.
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Identify and predict the principal behaviour described in camera sampling and indicator kinetics blur and discretise fast electrical events.
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Configure or document vary frame rate and kinetic constants for the same ground-truth spike train and record frame times, spike times, ca/f traces.
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Measure, calculate or compare vary frame rate and kinetic constants for the same ground-truth spike train using an explicit operational rule.
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Interpret the result and state why timing fallback and active parameter values require validation
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Vary frame rate and kinetic constants for the same ground-truth spike train.
4. Acquire or inspect frame times, spike times, ca/f traces.
5. Produce temporal-resolution comparison and missed/merged-event count.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Timing fallback and active parameter values require validation.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate vary frame rate and kinetic constants for the same ground-truth spike train. The reusable output is temporal-resolution comparison and missed/merged-event count.
Sources / connections / priority. Sources: [P2; O3; T2 Ch.6; G6]. Natural follow-ons: IMG-05. Development priority: High-priority extension. Boundary: Timing fallback and active parameter values require validation.
IMG-03 — Baseline, ΔF/F, noise and bleaching¶
| Concept / theme | Fluorescence normalisation and background processes can alter apparent response size. |
|---|---|
| Audience / context | Advanced undergraduate. Formal practical, workshop or modular course. Prior modules: IMG-01, DAT-02. |
| Logistics | 60 min; 1–3; boards: 0–1; software: Spikeling imaging GUI; Jupyter; prepared dataset: GUI-generated or prepared imaging traces. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | GUI simulation + analysis. Stages: 6–11. |
| Spikeling relationship | 6 — GUI simulation workflow; 5 — Recorded-dataset analysis |
Learning outcomes.
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Identify and predict the principal behaviour described in fluorescence normalisation and background processes can alter apparent response size.
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Configure or document manipulate baseline, pmt/noise and bleaching settings; calculate δf/f and record f0, f, δf/f, noise and bleach factor.
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Measure, calculate or compare manipulate baseline, pmt/noise and bleaching settings; calculate δf/f using an explicit operational rule.
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Interpret the result and state why δf/f depends on baseline definition; simulated bleaching/noise are didactic models
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Manipulate baseline, PMT/noise and bleaching settings; calculate ΔF/F.
4. Acquire or inspect f0, f, δf/f, noise and bleach factor.
5. Produce normalisation/qc figure and artefact log.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: ΔF/F depends on baseline definition; simulated bleaching/noise are didactic models.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate manipulate baseline, pmt/noise and bleaching settings; calculate δf/f. The reusable output is normalisation/qc figure and artefact log.
Sources / connections / priority. Sources: [P2; O3; T2 Ch.6; G6]. Natural follow-ons: IMG-04, IMG-05. Development priority: High-priority extension. Boundary: ΔF/F depends on baseline definition; simulated bleaching/noise are didactic models.
IMG-04 — Indicator kinetics, affinity and saturation¶
| Concept / theme | Indicator properties trade temporal response, sensitivity and dynamic range. |
|---|---|
| Audience / context | Master’s. Formal practical, workshop or modular course. Prior modules: IMG-02, IMG-03. |
| Logistics | 60 min; 1–3; boards: 0–1; software: Spikeling imaging GUI; Jupyter; prepared dataset: GUI-generated or prepared imaging traces. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | GUI simulation. Stages: 6–11. |
| Spikeling relationship | 6 — GUI simulation workflow; 8 — External computational comparison |
Learning outcomes.
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Identify and predict the principal behaviour described in indicator properties trade temporal response, sensitivity and dynamic range.
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Configure or document compare selected indicator presets or parameter sets under identical spikes and record kd, hill coefficient, rise/decay, df/fmax.
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Measure, calculate or compare compare selected indicator presets or parameter sets under identical spikes using an explicit operational rule.
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Interpret the result and state why verify active GUI parameters; preset values are model assumptions, not calibration of a physical indicator
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Compare selected indicator presets or parameter sets under identical spikes.
4. Acquire or inspect kd, hill coefficient, rise/decay, df/fmax.
5. Produce indicator comparison matrix.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Verify active GUI parameters; preset values are model assumptions, not calibration of a physical indicator.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate compare selected indicator presets or parameter sets under identical spikes. The reusable output is indicator comparison matrix.
Sources / connections / priority. Sources: [P2; O3; T2 Ch.6; G6]. Natural follow-ons: IMG-05. Development priority: Advanced specialised module. Boundary: Verify active GUI parameters; preset values are model assumptions, not calibration of a physical indicator.
IMG-05 — ROI, neuropil and spike inference against ground truth¶
| Concept / theme | Imaging analysis estimates cellular signals and hidden spikes from indirect noisy fluorescence. |
|---|---|
| Audience / context | Master’s/doctoral methods. Formal practical, workshop or modular course. Prior modules: IMG-03, DAT-03, STA-02. |
| Logistics | 60 min; 1–3; boards: 0–1; software: Spikeling imaging GUI; Jupyter; prepared dataset: GUI-generated or prepared imaging traces. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Prepared-dataset/Jupyter. Stages: 6–11. |
| Spikeling relationship | 5 — Recorded-dataset analysis; 6 — GUI simulation workflow; 8 — External computational comparison |
Learning outcomes.
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Identify and predict the principal behaviour described in imaging analysis estimates cellular signals and hidden spikes from indirect noisy fluorescence.
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Configure or document use prepared roi/neuropil traces or simplified synthetic data; deconvolve and compare with known spikes and record f, fneu, corrected f, inferred events, true events.
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Measure, calculate or compare use prepared roi/neuropil traces or simplified synthetic data; deconvolve and compare with known spikes using an explicit operational rule.
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Interpret the result and state why spikeling GUI does not generate full movies/motion; roi/neuropil work needs prepared data and inference cannot recover exact spike counts reliably
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Use prepared ROI/neuropil traces or simplified synthetic data; deconvolve and compare with known spikes.
4. Acquire or inspect f, fneu, corrected f, inferred events, true events.
5. Produce inference performance report and uncertainty statement.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Spikeling GUI does not generate full movies/motion; ROI/neuropil work needs prepared data and inference cannot recover exact spike counts reliably.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate use prepared roi/neuropil traces or simplified synthetic data; deconvolve and compare with known spikes. The reusable output is inference performance report and uncertainty statement.
Sources / connections / priority. Sources: [P2; O3; T2 Ch.6; G6]. Natural follow-ons: EPI-01, EXT-05. Development priority: Advanced specialised module. Boundary: Spikeling GUI does not generate full movies/motion; ROI/neuropil work needs prepared data and inference cannot recover exact spike counts reliably.