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Related Concept Videos

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...
Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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...
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...

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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Normal forms for some classes of sequential spiking neural P systems.

Tao Song, Linqiang Pan, Keqin Jiang

    IEEE Transactions on Nanobioscience
    |August 27, 2013
    PubMed
    Summary

    This study explores restricted spiking neural P systems (SN P systems). We demonstrate that these systems, when operating sequentially based on maximum spike count, achieve Turing universality for computation.

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    Area of Science:

    • Theoretical Computer Science
    • Computational Neuroscience
    • Biologically Inspired Computing

    Background:

    • Spiking neural P systems (SN P systems) model neuronal communication using spikes.
    • Standard SN P systems operate in a fully parallel manner.
    • Previous research has explored variations of SN P systems.

    Purpose of the Study:

    • To investigate the computational power of restricted SN P systems.
    • To analyze SN P systems that are simple or almost simple.
    • To examine SN P systems operating in a sequential mode dictated by maximum spike count.

    Main Methods:

    • Consideration of SN P systems with one rule per neuron (simple) or all but one neuron (almost simple).
    • Implementation of a sequential firing mechanism where neurons with the maximum number of spikes fire at each step.
    • Analysis of the computational capabilities of these restricted systems as number generating and accepting devices.

    Main Results:

    • Demonstrated that simple SN P systems operating sequentially are Turing universal.
    • Proved that almost simple SN P systems operating sequentially are also Turing universal.
    • Established Turing universality for both number generating and accepting functionalities.

    Conclusions:

    • Restricting SN P systems to be simple or almost simple and enforcing sequential firing based on maximum spikes does not diminish their computational power.
    • These findings enhance the understanding of SN P system universality and computational capabilities.
    • The results represent an improvement over previous findings in the field.