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Related Concept Videos

Neuronal Communication01:28

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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...
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Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of...
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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Reaction-diffusion in the NEURON simulator.

Robert A McDougal1, Michael L Hines, William W Lytton

  • 1Department of Neurobiology, Yale University New Haven, CT, USA.

Frontiers in Neuroinformatics
|December 4, 2013
PubMed
Summary
This summary is machine-generated.

NEURON's Reaction-Diffusion (rxd) module simulates neuronal response dynamics by integrating cell biology with electrophysiology. This Python tool enhances research into neurological health and disease through efficient, compiled reaction modeling.

Keywords:
computational neuroscienceneurodynamicsnumerical integrationpythonreaction-diffusion

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Area of Science:

  • Computational Neuroscience
  • Systems Biology
  • Biophysics

Background:

  • Understanding neuronal response dynamics is crucial for studying neurological health and disease.
  • Cell biological principles like genomics and signaling cascades significantly influence neuronal function.
  • Existing simulation tools may not fully integrate these diverse biological processes.

Purpose of the Study:

  • To introduce NEURON's Reaction-Diffusion (rxd) module for simulating neuronal dynamics.
  • To enable the integration of cell biological principles with electrophysiological models.
  • To support research on the role of reaction dynamics in neuronal function and dysfunction.

Main Methods:

  • Utilizing Python for specifying and simulating reaction-diffusion dynamics.
  • Overloading arithmetic operations for arbitrary reaction formula specification via Python syntax.
  • Transparent compilation into bytecode using NumPy for vectorized calculations.
  • Coupling NEURON's integrators with SciPy's sparse linear algebra library for simulations.

Main Results:

  • The rxd module allows for the specification of complex reaction dynamics using intuitive Python syntax.
  • Efficient simulation of coupled cell biological and electrophysiological processes is achieved through compiled bytecode and optimized libraries.
  • The tool supports the investigation of how molecular-level dynamics impact neuronal network behavior.

Conclusions:

  • NEURON's rxd module provides a powerful and flexible platform for computational neuroscience research.
  • The integration of reaction-diffusion modeling with electrophysiology facilitates a deeper understanding of neuronal function in health and disease.
  • This approach enables more comprehensive and accurate simulations of biological systems.