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Related Experiment Video

Updated: Feb 27, 2026

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

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How Adaptation Makes Low Firing Rates Robust.

Arthur S Sherman¹, Joon Ha²

¹Laboratory of Biological Modeling, National Institutes of Health, 12A South Drive, Bethesda, MD, 20892, USA. arthurs@mail.nih.gov.

Journal of Mathematical Neuroscience

|June 26, 2017

Summary

Adding adaptation current to neurons linearizes firing rate curves, enabling robust low-frequency firing. Strong adaptation is key, not necessarily the SNIC, for this effect in neural models.

Keywords:

Adaptation Firing rate SNIC bifurcation

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Published on: April 24, 2017

Area of Science:

Computational Neuroscience
Mathematical Biology
Systems Neuroscience

Background:

Type 1 neurons with a single negative input current (SNIC) model low firing frequencies.
The square-root-like f-I curve of SNIC neurons limits low firing rates to a narrow current range.
Adaptation currents can linearize the f-I curve, enabling robust low firing rates.

Purpose of the Study:

To investigate the role of adaptation in linearizing neuronal firing rate (f-I) curves.
To determine if a SNIC is necessary for linearization and robust low-frequency firing.
To explore the conditions under which adaptation leads to linearization in different neuron models.

Main Methods:

Simulations using a simplified Hindmarsh-Rose neuron model with negative feedback on adaptation current.
Analysis of a type 2 neuron model with a Hopf bifurcation.
Modeling using the conductance-based Morris-Lecar model with negative feedback on adaptation conductance.

Main Results:

A SNIC contributes to linearization, but strong adaptation strength is crucial for linearization over a large current interval.
Type 2 neurons can also exhibit linearization with strong adaptation, indicating a SNIC is not essential.
Sufficiently strong adaptation, which stretches the steep region near threshold, is more fundamental than a SNIC.
In the Morris-Lecar model, strong adaptive conductance is necessary and sufficient for linearization of type 2 f-I curves, provided the adaptation current's driving force is independent of input current.

Conclusions:

Strong adaptation is the primary mechanism for linearizing neuronal f-I curves and achieving robust low-frequency firing.
The presence of a SNIC is not a prerequisite for linearization.
Adaptation's effectiveness depends on its strength and, in some models, the driving force of the adaptation current.