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Synaptic noise

Synaptic noise is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Synaptic noise rather than just read about it. In short: Synaptic noise refers to the constant bombardment of synaptic activity in neurons. This occurs in the background of a cell when potentials are produced without the nerve stimulation of an action potential, and are due to the inherently random nature of synapses.

Key takeaways

  • Synaptic noise belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Synaptic noise to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Synaptic noise from memory before moving on to harder problems.

Reference excerpt

Synaptic noise refers to the constant bombardment of synaptic activity in neurons. This occurs in the background of a cell when potentials are produced without the nerve stimulation of an action potential, and are due to the inherently random nature of synapses. These random potentials have similar time courses as excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs), yet they lead to variable neuronal responses. The variability is due to differences in the discharge times of action potentials.

Causes Many types of noise exist in cells. First, there is intrinsic noise and extrinsic, or synaptic, noise. Within each category there are two further divisions of noise – voltage noise or temporal noise. Intrinsic voltage noise is due to random changes in the membrane potential of a cell, and intrinsic temporal noise is caused by variations in spike generation timing. The following sections give explanations about the causes of synaptic noise.

Quantal release Both synaptic voltage and temporal noise are due to the probability associated with transmitter release. In an action potential, calcium channels are opened by depolarization and release Ca2+ ions into the presynaptic cell. This causes neurotransmitters, which are kept in vesicles, to be released into the synapse. Vesicles are released in quanta – packets that contain roughly 7,000 molecules of transmitters. The likelihood of quanta being released is assigned a probability that increases when the action potential arrives at synaptic terminals, and progressively decreases to a lower, resting value. Therefore, the uncertainty involved in the exact timing of neurotransmitter release is a cause for synaptic temporal noise. Furthermore, the strength of the postsynaptic response varies based on the number of quanta released. Quantal release results in the inconsistent strength and timing of a response, and this is cause for synaptic voltage noise.

Background activity Another cause of noise is due to the exocytosis of neurotransmitters from the synaptic terminals that provide input to a given neuron. This occurrence happens in the background while a cell is at resting membrane potential. Since it is happening in the background, the release is not due to a signal, but is random. This unpredictability adds to the synaptic noise level. Synaptic noise shows up as miniature postsynaptic current, which is observed without any presynaptic input. These spontaneous currents are due to randomly released neurotransmitter vesicles. This is caused by the stochastic "opening of intracellular Ca2+ stores, synaptic Ca2+-channel noise, spontaneous triggering of the vesicle-release pathway, or spontaneous fusion of a vesicle with the membrane."

Chemical sensing Chemical sensing, such as that of taste and smell which rely on an external chemical stimulus, is affected by thermodynamics. Chemical molecules arrive at the appropriate receptor at random times based on the rate of diffusion of these particles. Also, receptors can't perfectly count the number of signaling molecules that pass through. These two factors are additional causes of synaptic noise.

How the CNS manages noise The central nervous system (CNS) deals with noise in two ways – averaging and prior knowledge.

Averaging Averaging occurs whenever redundant information is given to a sensory input or generated by the CNS itself. When several units of cellular processing carry the same signal but are affected by different sources of noise, averaging can counter the noise. This occurrence can be seen when sensory inputs couple to work together or overlap, so that they can take an average of incoming signals and random stimuli. Averaging is also seen at divergent synapses, where one signal provides input to many neurons. It can be advantageous to send a signal multiple times over many axons and combine the information at the end, rather than to send the signal once over a single, long, noisy neuron. This means that in order for the fidelity of the signal to be preserved, the initial signal must be reliable. At the final destination, signals are averaged and noise can be offset.

Prior knowledge Prior knowledge is also used when facing noise. In sensory neurons that receive redundant and structured signals, sensory processing can differentiate the signal from noise. This occurrence is known as the matched filter principle, whereby a neuron can use past experience about an expected input to distinguish noise from the actual signal and consequently reduce the impact of noise.

In the hippocampus The significance of synaptic noise has become clear through ongoing research of the brain, specifically the hippocampus. The hippocampus is a region of the forebrain in the medial temporal lobe closely associated with memory formation and recollection. Gamma and theta oscillations, released during exploratory activities, create modulated rhythms that transform into prolonged excitation, and furthermore into memories or improper potentiation. These oscillations can be partially composed of synaptic currents or synaptic noise. There is recent evidence that supports the role of synaptic noise in the signal functions within the hippocampus, and therefore in memories, whether solidifying or interfering. This focus is greatly reliant on stochastic resonance. From notable research by Stacey and Durand, synaptic noise has been credited for enhanced detection of weak or distal synaptic inputs within the hippocampus. Using a computer model, subthreshold currents were simulated in the CA3 region that directly correlated with increased CA1 action potential activity when small currents were introduced. This is an example of a commonly ostracized natural occurrence that dampens important signals can now be studied and utilized for therapeutic reasons to aid neural plasticity. Common injuries in the hippocampus region can result in schizophrenia, epilepsy, Parkinson's and Alzheimer's diseases. Synaptic noise may be part of the development of these illnesses, however, sufficient research has not been conducted. A possible relevance is the inability of synaptic noise to fine-tune or regulate proper summation into a message. If weak signals cannot be enhanced with existing noise, synaptic plasticity is compromised, and memory and personality will be impacted. The research of Stacey and Durand helped shape this new direction in the analysis and pharmaceutical development to combat hippocampal illnesses.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Synaptic noise

Start with the simplest possible case. Write down what Synaptic noise claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Synaptic noise before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Synaptic noise ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Synaptic noise

In research
Synaptic noise appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Synaptic noise in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Synaptic noise is common in secondary-school and first-year university syllabi. It links to neighbouring topics Neural synapse, so understanding it makes those chapters shorter.
In everyday life
Look for Synaptic noise outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Synaptic noise in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Synaptic noise means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Synaptic noise out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Synaptic noise in simple terms?

Synaptic noise refers to the constant bombardment of synaptic activity in neurons. This occurs in the background of a cell when potentials are produced without the nerve stimulation of an action potential, and are due to the inherently random nature of synapses.

Why does Synaptic noise matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Synaptic noise?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Synaptic noise.

Tags

  • Neural synapse

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