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Related Concept Videos

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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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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Linear equations form the foundation of many algebraic and real-world applications, characterized by their simplicity and utility. A linear equation is an algebraic statement in which each term is either a constant or a product of a constant and a single variable. These equations represent straight lines when plotted on a Cartesian coordinate plane, reflecting a constant rate of change between two quantities.A typical linear equation in one variable has the form: ax + b = c, where a, b, and c...
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A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
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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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Fast Micro-iontophoresis of Glutamate and GABA: A Useful Tool to Investigate Synaptic Integration
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Linearization of excitatory synaptic integration at no extra cost.

Danielle Morel1, Chandan Singh2, William B Levy3

  • 1Physics Department, Emory & Henry College, Emory, VA, 24327, USA.

Journal of Computational Neuroscience
|January 27, 2018
PubMed
Summary

Linear summation in neural computation may be costly. This study shows voltage-gated conductances can linearize synaptic activation with minimal energy cost in neuron models.

Keywords:
Biophysical modelMetabolic costMixed-cation channelSodium channelVoltage-gated conductances

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Linear summation of synaptic inputs is a common assumption in neural computation models.
  • Achieving linear summation may require biophysical mechanisms that increase energy expenditure.
  • The energy cost of linearizing synaptic activation in neurons is not well understood.

Purpose of the Study:

  • To quantify the energy cost associated with linearizing dendritically localized synaptic activation using voltage-gated conductances.
  • To identify specific voltage-gated conductances that can achieve linear summation with minimal energy cost.
  • To compare the energy costs of these active models to a passive neuron model.

Main Methods:

  • Utilized a simple neuron model incorporating voltage-gated conductances.
  • Examined various combinations of voltage-gated conductances to achieve linearization.
  • Quantified the energy costs of these models compared to a purely passive model.

Main Results:

  • Several combinations of voltage-gated conductances were identified that effectively linearize synaptic activation.
  • Four specific models demonstrating linearization are presented.
  • In some cases, the energy costs were minimal or even negligible compared to a passive model.

Conclusions:

  • Voltage-gated conductances offer a viable mechanism for achieving linear summation in neural computation.
  • The energy cost of linearizing synaptic activation may not be prohibitive, with some models showing minimal additional expenditure.
  • These findings have implications for understanding the efficiency of neural processing and the design of energy-efficient artificial neural networks.