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Electrophysiology and instrumentation

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: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]

EPH-01 — Current clamp: command and response

Concept / theme Current clamp controls input and observes model voltage as the dependent variable.
Audience / context Undergraduate. Formal practical, workshop or modular course. Prior modules: FND-04, NPH-02.
Logistics 45 min; 2–3 per board; boards: 1; software: GUI; notebook for quantitative variants; prepared dataset: No; timing reference file for EPH-04. Equipment: Standard USB cable; worksheet/protocol card.
Mode / stages Guided experiment. Stages: 3–7, 10–11.
Spikeling relationship 1 — Direct physical implementation; 2 — Direct board + GUI; 7 — Conceptual analogy

Learning outcomes.

  • Identify and predict the principal behaviour described in current clamp controls input and observes model voltage as the dependent variable.

  • Configure or document apply defined current steps and document command, response and baseline and record command current a.u., Vm, total current.

  • Measure, calculate or compare apply defined current steps and document command, response and baseline using an explicit operational rule.

  • Interpret the result and state why no electrode, seal or access resistance is present; current is model-scaled

Roadmap.

1. Initial prediction or classification.

2. Configure the board, GUI, simulation or dataset and record metadata.

3. Apply defined current steps and document command, response and baseline.

4. Acquire or inspect command current a.u., Vm, total current.

5. Produce current-clamp protocol diagram and trace set.

6. Compare conditions or models and justify the chosen measurement.

7. Answer a limitation question: No electrode, seal or access resistance is present; current is model-scaled.

Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate apply defined current steps and document command, response and baseline. The reusable output is current-clamp protocol diagram and trace set.

Sources / connections / priority. Sources: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]. Natural follow-ons: NPH-03, EPH-02, MET-03. Development priority: Core module. Boundary: No electrode, seal or access resistance is present; current is model-scaled.

EPH-02 — Model voltage clamp and controller current

Concept / theme The GUI/firmware can command model voltage through feedback and expose the required controller current.
Audience / context Advanced undergraduate to master’s. Formal practical, workshop or modular course. Prior modules: EPH-01, FND-02.
Logistics 60 min; 2–3 per board; boards: 1; software: GUI; notebook for quantitative variants; prepared dataset: No; timing reference file for EPH-04. Equipment: Standard USB cable; worksheet/protocol card.
Mode / stages Board + GUI experiment. Stages: 3–7, 10–11.
Spikeling relationship 2 — Direct board + GUI; 7 — Conceptual analogy; 8 — External computational comparison

Learning outcomes.

  • Identify and predict the principal behaviour described in the GUI/firmware can command model voltage through feedback and expose the required controller current.

  • Configure or document step holding commands and inspect Vm tracking and clamp current and record command Vm, actual Vm, i_clamp, controller limits.

  • Measure, calculate or compare step holding commands and inspect Vm tracking and clamp current using an explicit operational rule.

  • Interpret the result and state why not suitable for attributing currents to channels, conductances or reversal potentials

Roadmap.

1. Initial prediction or classification.

2. Configure the board, GUI, simulation or dataset and record metadata.

3. Step holding commands and inspect Vm tracking and clamp current.

4. Acquire or inspect command Vm, actual Vm, i_clamp, controller limits.

5. Produce command–response plot and analogue-vs-biological clamp critique.

6. Compare conditions or models and justify the chosen measurement.

7. Answer a limitation question: Not suitable for attributing currents to channels, conductances or reversal potentials.

Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate step holding commands and inspect Vm tracking and clamp current. The reusable output is command–response plot and analogue-vs-biological clamp critique.

Sources / connections / priority. Sources: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]. Natural follow-ons: CMP-08, EPI-02. Development priority: Advanced specialised module. Boundary: Not suitable for attributing currents to channels, conductances or reversal potentials.

EPH-03 — Analogue, digital and event signals

Concept / theme Continuous variables and discrete events support different measurements and network functions.
Audience / context School to undergraduate. Formal practical, workshop or modular course. Prior modules: FND-04.
Logistics 45 min; 2–3 per board; boards: 1; software: GUI; notebook for quantitative variants; prepared dataset: No; timing reference file for EPH-04. Equipment: Standard USB cable; worksheet/protocol card.
Mode / stages Instructor-LED practical. Stages: 3–7, 10–11.
Spikeling relationship 1 — Direct physical implementation; 2 — Direct board + GUI

Learning outcomes.

  • Identify and predict the principal behaviour described in continuous variables and discrete events support different measurements and network functions.

  • Configure or document observe analogue Vm, digital spike TTL, stimulus output and trigger and record amplitude, edge timing, event identity.

  • Measure, calculate or compare observe analogue Vm, digital spike TTL, stimulus output and trigger using an explicit operational rule.

  • Interpret the result and state why TTL represents an event; analogue output is scaled model Vm

Roadmap.

1. Initial prediction or classification.

2. Configure the board, GUI, simulation or dataset and record metadata.

3. Observe analogue Vm, digital spike TTL, stimulus output and trigger.

4. Acquire or inspect amplitude, edge timing, event identity.

5. Produce signal-type comparison table and wiring diagram.

6. Compare conditions or models and justify the chosen measurement.

7. Answer a limitation question: TTL represents an event; analogue output is scaled model Vm.

Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate observe analogue Vm, digital spike TTL, stimulus output and trigger. The reusable output is signal-type comparison table and wiring diagram.

Sources / connections / priority. Sources: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]. Natural follow-ons: NET-01, DAT-01, MET-02. Development priority: Core module. Boundary: TTL represents an event; analogue output is scaled model Vm.

EPH-04 — Sampling and timebase validation

Concept / theme Every temporal result depends on the true sample interval, timestamps and dropped-sample behaviour.
Audience / context Undergraduate to doctoral methods. Formal practical, workshop or modular course. Prior modules: FND-04.
Logistics 60 min; 2–3 per board; boards: 1; software: GUI; notebook for quantitative variants; prepared dataset: No; timing reference file for EPH-04. Equipment: Standard USB cable; worksheet/protocol card.
Mode / stages Methodology + acquisition exercise. Stages: 3–7, 10–11.
Spikeling relationship 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 7 — Conceptual analogy

Learning outcomes.

  • Identify and predict the principal behaviour described in every temporal result depends on the true sample interval, timestamps and dropped-sample behaviour.

  • Configure or document measure packet/sample cadence against a known periodic stimulus and compare firmware/GUI settings and exported time and record sample count, reference period, inferred dt, missing packets.

  • Measure, calculate or compare measure packet/sample cadence against a known periodic stimulus and compare firmware/GUI settings and exported time using an explicit operational rule.

  • Interpret the result and state why final reference method and acceptable tolerance require empirical validation on each release/computer

Roadmap.

1. Initial prediction or classification.

2. Configure the board, GUI, simulation or dataset and record metadata.

3. Measure packet/sample cadence against a known periodic stimulus and compare firmware/GUI settings and exported time.

4. Acquire or inspect sample count, reference period, inferred dt, missing packets.

5. Produce validated timing certificate and metadata entry for the session.

6. Compare conditions or models and justify the chosen measurement.

7. Answer a limitation question: Final reference method and acceptable tolerance require empirical validation on each release/computer.

Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate measure packet/sample cadence against a known periodic stimulus and compare firmware/GUI settings and exported time. The reusable output is validated timing certificate and metadata entry for the session.

Sources / connections / priority. Sources: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]. Natural follow-ons: All timing-dependent NPH, DAT, IMG and EXT modules. Development priority: Core module. Boundary: Final reference method and acceptable tolerance require empirical validation on each release/computer.

EPH-05 — Noise, filtering and signal-to-noise

Concept / theme Noise affects detection, estimation and reproducibility; filtering changes both noise and signal.
Audience / context Undergraduate. Formal practical, workshop or modular course. Prior modules: DAT-02; EPH-04 for filter design.
Logistics 60 min; 2–3 per board; boards: 1; software: GUI; notebook for quantitative variants; prepared dataset: No; timing reference file for EPH-04. Equipment: Standard USB cable; worksheet/protocol card.
Mode / stages Hybrid experiment. Stages: 3–7, 10–11.
Spikeling relationship 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 7 — Conceptual analogy

Learning outcomes.

  • Identify and predict the principal behaviour described in noise affects detection, estimation and reproducibility; filtering changes both noise and signal.

  • Configure or document vary noise input; optionally compare raw and notebook-filtered traces and record baseline sd, spike detectability, waveform distortion.

  • Measure, calculate or compare vary noise input; optionally compare raw and notebook-filtered traces using an explicit operational rule.

  • Interpret the result and state why generated gaussian current is only one noise model; causal noise source cannot be inferred from trace shape alone

Roadmap.

1. Initial prediction or classification.

2. Configure the board, GUI, simulation or dataset and record metadata.

3. Vary noise input; optionally compare raw and notebook-filtered traces.

4. Acquire or inspect baseline sd, spike detectability, waveform distortion.

5. Produce snr table and filter-justification note.

6. Compare conditions or models and justify the chosen measurement.

7. Answer a limitation question: Generated Gaussian current is only one noise model; causal noise source cannot be inferred from trace shape alone.

Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate vary noise input; optionally compare raw and notebook-filtered traces. The reusable output is snr table and filter-justification note.

Sources / connections / priority. Sources: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]. Natural follow-ons: SEN-05, DAT-03, EXT-03. Development priority: Core module. Boundary: Generated Gaussian current is only one noise model; causal noise source cannot be inferred from trace shape alone.

EPH-06 — Calibration, dynamic range and artefacts

Concept / theme A measurement pipeline has baseline, range, saturation and clipping limits that must be documented.
Audience / context Advanced undergraduate. Formal practical, workshop or modular course. Prior modules: FND-04, EPH-04.
Logistics 60 min; 2–3 per board; boards: 1; software: GUI; notebook for quantitative variants; prepared dataset: No; timing reference file for EPH-04. Equipment: Standard USB cable; worksheet/protocol card.
Mode / stages Methodology experiment. Stages: 3–7, 10–11.
Spikeling relationship 1 — Direct physical implementation; 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 7 — Conceptual analogy

Learning outcomes.

  • Identify and predict the principal behaviour described in a measurement pipeline has baseline, range, saturation and clipping limits that must be documented.

  • Configure or document probe low/high inputs, offsets and GUI display/export behaviour and record minimum/maximum readable values, clipping, baseline drift.

  • Measure, calculate or compare probe low/high inputs, offsets and GUI display/export behaviour using an explicit operational rule.

  • Interpret the result and state why the module characterises the platform, not biological membrane dynamic range

Roadmap.

1. Initial prediction or classification.

2. Configure the board, GUI, simulation or dataset and record metadata.

3. Probe low/high inputs, offsets and GUI display/export behaviour.

4. Acquire or inspect minimum/maximum readable values, clipping, baseline drift.

5. Produce qc checklist and usable-range chart.

6. Compare conditions or models and justify the chosen measurement.

7. Answer a limitation question: The module characterises the platform, not biological membrane dynamic range.

Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate probe low/high inputs, offsets and GUI display/export behaviour. The reusable output is qc checklist and usable-range chart.

Sources / connections / priority. Sources: [T1 Ch.2; T2 Chs.9–10; M1; G3–G5, G9]. Natural follow-ons: MET-04, DAT-02, EPI-01. Development priority: High-priority extension. Boundary: The module characterises the platform, not biological membrane dynamic range.