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Patch-clamp-style labs

This page provides a set of patch-clamp-inspired laboratory activities designed for Spikeling. The goal is to teach the logic of electrophysiology (current clamp thinking, excitability, adaptation, synaptic integration) using a reproducible hardware+GUI workflow.

These labs are written for university-level teaching:

  • quick to run (typically 10–30 minutes per activity)
  • easy to scaffold (intro → prediction → run → interpret)
  • compatible with hardware and emulator mode

If you want a minimal setup sequence first, see: Quickstart → First experiment.

What students should practise

Across the labs, students will practise:

  • identifying baseline Vm, threshold, and spiking regimes
  • predicting responses before changing a parameter (“hypothesis-first”)
  • designing simple protocols to test excitability and adaptation
  • measuring outputs (spike count, firing rate, latency, jitter)
  • connecting stimulation choice (waveform, frequency, amplitude) to interpretation
  • recording and exporting data for analysis (CSV → Python)

General workflow (applies to all labs)

  1. Start from a known baseline
  2. current injection at 0 (centre detent / patch clamp neutral)
  3. stimulus off

  4. noise off (unless the lab is about noise)

  5. Choose one neuron mode and keep it fixed

  6. The neuron mode controls intrinsic dynamics.

  7. Changing mode mid-lab is useful, but only after students have drawn conclusions with one stable mode.

  8. Apply an input

  9. DC injection (Patch clamp / current injection potentiometer), or
  10. programmed stimulus from the GUI (Steps, sine/triangle, chirp, noise), or

  11. synaptic input from another unit / emulator auxiliary neuron.

  12. Observe Vm and spikes

  13. Vm tells the state; spikes are discrete events.

  14. Record

  15. Record a short segment (10–30 s) once the behaviour is stable.

  16. Export to CSV.

  17. Measure one thing

  18. spike count, firing rate, latency, adaptation index, etc.

  19. Interpret

  20. connect “what I changed” to “what I saw”.

Teaching rhythm

The fastest way to build intuition is: predict → test → explain. Make students state the expected change before moving a slider.

Lab 0 — Warm-up: finding threshold (current clamp intuition)

Question: How much injected current is needed to make the neuron spike?

Inputs: DC current injection (Patch clamp / current injection potentiometer)

Steps

  1. Ensure stimulus and noise are off.
  2. Increase injected current until Vm begins to spike.
  3. Reduce slightly until spiking stops again.
  4. Repeat once to confirm reproducibility.

What to record / measure

  • threshold current (approximate)
  • Vm just below threshold vs just above threshold
  • spike latency (time from start of injection to first spike)

Expected observations

  • Below threshold: Vm shifts but no spikes.
  • Near threshold: small differences can change outcome.
  • Above threshold: sustained spiking.

Lab 1 — Step protocol: excitability and firing rate

Question: How does firing rate depend on input amplitude?

Inputs: GUI Steps stimulus routed as Current (or manual DC steps)

Steps

  1. Route stimulus to Current.
  2. Choose a simple step: baseline → step → baseline (e.g., 1–2 s step).
  3. Run multiple step amplitudes (increasing strength).
  4. Keep neuron mode fixed.

What to measure

  • spike count during the step
  • mean firing rate during the step (spikes / second)

Expected observations

  • increased step amplitude increases firing rate
  • spiking may show adaptation (rate decreases over time during the step)

Lab 2 — Spike-frequency adaptation (early vs late rate)

Question: Does firing rate remain constant under constant drive?

Inputs: constant DC injection or a long step

Steps

  1. Apply a sustained depolarising current that produces stable spiking.
  2. Keep it constant for 5–10 seconds.
  3. Record and export.

What to measure

  • firing rate in an early window (e.g., first 1–2 s)
  • firing rate in a late window (e.g., last 1–2 s)
  • adaptation index: (early − late) / early

Expected observations

  • rate typically decreases over time in adapting modes
  • some modes adapt weakly or not at all (fast-spiking-like behaviour)

Lab 3 — Subthreshold dynamics and resonance (optional extension)

Question: Does the neuron respond preferentially to certain frequencies?

Inputs: sine wave or chirp stimulus at subthreshold amplitude

Steps

  1. Set injected current so Vm is below threshold (no spikes).
  2. Apply a small sine wave (or chirp) stimulus.
  3. Observe the amplitude of Vm oscillations as frequency changes.

What to look for

  • Vm response may grow at certain frequency ranges (resonance-like behaviour)
  • responses may depend strongly on neuron mode

Note

This is an advanced conceptual lab. If short on time, run it as a demonstration.

Lab 4 — Noise and stochastic spiking

Question: Can noise alone trigger spikes near threshold?

Inputs: Noise + near-threshold bias current

Steps

  1. Bias Vm just below threshold using injected current.
  2. Increase Noise gradually.
  3. Observe when intermittent spikes appear.

What to measure

  • spike rate as a function of noise level
  • spike-time jitter (variability)

Expected observations

  • spikes appear probabilistically at sufficient noise levels
  • rate increases with noise amplitude
  • spike timing becomes less precise

Lab 5 — Synaptic input as a current source (two-unit coupling)

Question: How do excitatory vs inhibitory synapses affect postsynaptic spiking?

Inputs: presynaptic spiking from another unit (or emulator auxiliary neuron)

Steps (hardware)

  1. Connect: Axon output (Unit A) → Synapse input (Unit B).
  2. Make Unit A spike reliably (inject current).
  3. Set synapse gain on Unit B:
  4. positive (excitatory) then negative (inhibitory)
  5. Observe changes in Unit B Vm and spiking.

Steps (emulator)

  1. Configure Auxiliary Neuron 1 to spike.
  2. Enable Synapse main neuron.
  3. Set synapse sign/gain and observe the main neuron output.

What to measure

  • probability of postsynaptic spikes per presynaptic spike train
  • change in baseline Vm under sustained synaptic drive
  • suppression effects under inhibition

Lab 6 — “Design your own protocol” (capstone mini-lab)

Question: Can students propose and test a hypothesis about excitability?

Example hypotheses

  • “This neuron responds more strongly to low frequencies than high frequencies.”
  • “Inhibition can prevent spiking even when DC current is above threshold.”
  • “Noise increases spike-time variability but not mean Vm.”

Requirements (student checklist)

  • write a one-sentence hypothesis
  • choose a stimulus waveform that tests it (Steps, sine, chirp, noise)
  • record one dataset
  • compute one metric
  • interpret results in 3–5 sentences

Practical notes (common issues)

Start simple

If results are confusing, disable everything except one input pathway: no noise, no light, no synapses — then add one pathway at a time.

  • If traces clip or look unstable, reduce stimulus strength and confirm grounding/cables.
  • If the device stops streaming, press Reset and re-connect in the GUI.
  • For teaching at scale, use emulator mode first to standardise expectations.