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Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Action Potentials01:41

Action Potentials

Overview
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Electrical Synapses01:28

Electrical Synapses

Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...

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Related Experiment Video

Updated: Jun 30, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Spiking without Resets: Continuous Integrate-and-Fire Dynamics in Neuronal Circuits.

Roberto Fenollosa1, Juan Bisquert1

  • 1Instituto de Tecnología Química (ITQ), Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, 46022, València, Spain.

The Journal of Physical Chemistry Letters
|June 29, 2026
PubMed
Summary

Spiking behavior in artificial neurons can emerge from a continuous dynamical system without explicit reset rules. This novel approach utilizes a memristor

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Last Updated: Jun 30, 2026

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • The leaky integrate-and-fire (LIF) model is a standard for neuronal spiking dynamics.
  • LIF models typically require explicit reset mechanisms or negative differential resistance.
  • Alternative mechanisms for spike generation are actively sought.

Purpose of the Study:

  • To investigate if spike-like behavior can arise in a continuous dynamical system.
  • To explore a novel mechanism for spike generation without traditional reset rules.
  • To analyze the role of nonlinear coupling in emergent spiking.

Main Methods:

  • Studied a minimal resistive-capacitive circuit.
  • Coupled the circuit to a conductance-activated quasi-linear memristor.
  • Analyzed the system's dynamics using an internal state variable.

Main Results:

  • Demonstrated that spiking emerges from nonlinear coupling between the state variable and its voltage-dependent equilibrium.
  • Showed that spiking onset is not governed by static current-voltage characteristics.
  • Found that the emergence of spiking is sensitive to excitation frequency, not sharp thresholds.

Conclusions:

  • Spike-like dynamics can be generated within a fully continuous framework.
  • Nonlinear coupling in memristive circuits offers a new paradigm for artificial neuron design.
  • This approach bypasses the need for explicit reset mechanisms in neuronal modeling.