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Excitability and threshold

This experiment teaches the core electrophysiology idea of excitability: a neuron can switch abruptly from silent to spiking when input drive crosses a threshold-like boundary.

In Spikeling, you will reproduce this using a current-clamp style workflow:

  • control input drive (DC injection, steps, stimulus strength)
  • observe Vm and spikes
  • quantify the transition point and how it depends on neuron mode, noise, and stimulus waveform

If you need background first, see: Concepts → Threshold and excitability.

Learning goals

By the end of this experiment, students should be able to:

  • define excitability in terms of input–output transformation
  • locate an approximate threshold for spiking under current clamp
  • explain why threshold can be state dependent (history and adaptation)
  • predict how noise alters threshold and spike probability
  • compare excitability across different neuron modes

What you need

  • Spikeling hardware (or Emulator mode)
  • Spikeling GUI
  • Optional: second unit for synaptic threshold comparisons

Recommended signals to display:

  • Vm
  • Total input current (Itot / Input Current, if available)
  • Stimulus (if using stimulus protocols)

Practical notes

  • Start with small currents/stimulus strengths.
  • Keep noise off for the first pass (you will add it later).

Part A — Threshold with direct DC injection (fastest demonstration)

Concept: in current clamp, the simplest protocol is “hold a constant injected current and observe Vm”.

Steps (hardware)

  1. Set the current injection potentiometer to the centre detent (0 current).
  2. Confirm Vm shows a stable baseline.
  3. Turn the potentiometer slowly right (depolarising) until spikes begin.
  4. Turn slightly back left until spiking stops.
  5. Repeat once to confirm reproducibility.

Steps (emulator)

  1. Start emulator and open Neuron Parameters.
  2. Increase Patch clamp injected current slowly until spikes begin.
  3. Reduce slightly until spiking stops.
  4. Repeat once.

Record / report

  • the approximate injection level where spikes first appear (“threshold injection”)
  • Vm baseline and Vm just below threshold
  • whether the transition is sharp or gradual

Teaching tip

Ask students to predict before turning the knob: “What should Vm do as I increase depolarising current? When do spikes appear?”

Part B — Threshold with a step protocol (more measurable)

Concept: threshold depends on timing; a step stimulus gives you a clean “before/after” window.

Setup

  • Use the GUI Steps stimulus routed to Current.
  • Choose a simple baseline → step → baseline waveform.

Steps

  1. Ensure injected DC is near 0 (or at a fixed small bias).
  2. Start with a low step strength (no spikes).
  3. Increase step strength in small increments until spikes occur during the step.
  4. Run each amplitude 2–3 times.

Record / measure

  • minimal step amplitude that evokes spikes
  • spike latency (time from step onset to first spike)
  • spike count during the step

Expected observations

  • below threshold: Vm depolarises but no spikes
  • at threshold: occasional spikes (often variable)
  • above threshold: reliable spiking

Part C — Threshold is not always a fixed number (state dependence)

In many neuron models (and biological neurons), the effective threshold depends on the neuron’s state and recent history.

Demonstration 1: adaptation changes threshold-like behaviour

  1. Drive the neuron into sustained spiking for a few seconds.
  2. Return to a near-threshold condition.
  3. Repeat the same step stimulus as in Part B.

Observation: the neuron may require a larger step (or spikes may become less reliable) after recent spiking.

Demonstration 2: bias current shifts threshold

  1. Set a small constant depolarising bias.
  2. Re-run the step series.

Observation: threshold step amplitude decreases as baseline Vm is closer to spiking.

Part D — Noise makes threshold probabilistic

Concept: near threshold, noise can trigger spikes even when the mean input is below the deterministic threshold.

Steps

  1. Set injected current so the neuron is just below threshold (no spikes).
  2. Increase Noise gradually.
  3. Observe when intermittent spikes appear.
  4. Increase noise further to see spike probability/rate rise.

Measure (simple)

  • spike rate vs noise level
  • time to first spike after noise is increased (optional)

Interpretation

  • threshold becomes a probability boundary, not a single line
  • spikes appear via fluctuation-driven threshold crossings

Part E — Compare excitability across neuron modes

Concept: neuron modes differ in intrinsic dynamics; the same input can produce different outputs.

Steps

  1. Choose two or three modes (e.g., regular spiking-like, fast spiking-like, bursting-like).
  2. For each mode:
  3. find threshold with DC injection (Part A), or
  4. find minimal step amplitude that evokes spiking (Part B)
  5. Compare:
  6. threshold injection/step
  7. firing rate above threshold
  8. adaptation strength

Report

A simple table works well:

Mode Threshold (DC or step) Firing rate above threshold Adaptation (qualitative)
Mode A
Mode B
Mode C

Discussion prompts (useful for lab reports)

  • Why might “threshold” depend on recent spiking?
  • How does adding noise change the meaning of threshold?
  • What does it mean for a neuron to be “more excitable”?
  • If two modes have the same threshold, can their firing patterns still differ?

Extensions (optional)

  • Strength–duration curve: vary step duration as well as amplitude.
  • Spike-triggered averages: align Vm or current around spikes.
  • Synaptic threshold: use an excitatory synapse input as the drive and ask how much presynaptic spiking is needed to recruit postsynaptic spikes.