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Related Concept Videos

Neural Circuits01:25

Neural Circuits

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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.
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Postsynaptic Potential (PSP)01:32

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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.
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Integration of Synaptic Events01:28

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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...
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Propagation of Action Potentials01:23

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Electrical Synapses01:28

Electrical Synapses

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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.
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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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Poisson-like spiking in circuits with probabilistic synapses.

Rubén Moreno-Bote1

  • 1Research Unit, Parc Sanitari Sant Joan de Déu and Universitat de Barcelona, Esplugues de Llobregat, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Esplugues de Llobregat, Barcelona, Spain.

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Neuronal networks with probabilistic synaptic transmission exhibit robust Poisson-like firing variability across a wide range of rates. This synaptic noise is a sufficient mechanism for cortical spiking variability.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Cortical neuronal activity exhibits Poisson-like variability across a wide range of firing rates.
  • The underlying mechanisms for this broad-range variability remain largely unknown.

Purpose of the Study:

  • To investigate the mechanisms responsible for Poisson-like neuronal firing variability in cortical networks.
  • To determine if probabilistic synaptic transmission can account for observed variability.

Main Methods:

  • Simulated neuronal networks with probabilistic synaptic transmission.
  • Analysis of firing rate variability and Fano factor under varying network parameters.
  • Comparison with other potential sources of variability like synaptic delays and jitter.

Main Results:

  • Networks with probabilistic synapses robustly generated Poisson-like variability over several orders of magnitude in firing rate without fine-tuning.
  • Other variability sources (delays, jitter) failed to produce Poisson-like variability at high rates.
  • Probabilistic synapses predict Fano factor constancy of synaptic conductances.

Conclusions:

  • Synaptic noise, specifically probabilistic transmission, is a robust and sufficient mechanism for cortical-like spiking variability.
  • Recurrent amplification is crucial for other variability sources to achieve high-rate Poisson-like behavior.
  • Findings suggest synaptic noise is a key determinant of neuronal firing patterns in the cortex.