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Neuronal tuning

Neuronal tuning is a biology 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 Neuronal tuning rather than just read about it. In short: In neuroscience, neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through experience.

Key takeaways

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

Reference excerpt

In neuroscience, neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through experience. Neuronal tuning can be strong and sharp, as observed in primary visual cortex (area V1), or weak and broad, as observed in neural ensembles. Single neurons are hypothesized to be simultaneously tuned to several modalities, such as visual, auditory, and olfactory. Neurons hypothesized to be tuned to different signals are often hypothesized to integrate information from the different sources. In computational models called neural networks, such integration is the major principle of operation. The best examples of neuronal tuning can be seen in the visual, auditory, olfactory, somatosensory, and memory systems, although due to the small number of stimuli tested the generality of neuronal tuning claims is still an open question.

Visually Tuned System Accepted neuronal tuning models suggest that neurons respond to different degrees based on the similarity between the optimal stimulus of the neuron and the given stimulus. (Teller (1984), however, has challenged the "detector" view of neurons on logical grounds) The first major evidence of neuronal tuning in the visual system was provided by Hubel and Wiesel in 1959. They discovered that oriented slits of light were the most effective (of a very small set tested) stimuli for striate cortex “simple cell” neurons. Other neurons, “complex cells," responded best to lines of a certain orientation moving in a specific direction. Overall, the V1 neurons were found to be selectively tuned to certain orientations, sizes, positions, and forms. Hubel and Wiesel won the Nobel Prize in Physiology or Medicine in 1981 for their discoveries concerning information processing in the visual system. (More recently, Carandini et al (2005) have pointed out that the distinction between "simple" and "complex" cells may not be a valid one, observing that "simple and complex cells may not form a dichotomy at all.") While these simple cells in V1 respond to oriented bars through small receptive fields, the optimal visual stimulus becomes increasing complex as one moves toward the anterior of the brain. Neurons in area V4 are selectively tuned to different wavelengths, hues, and saturations of color. The middle temporal or area V5 is specifically tuned to the speed and direction of moving stimuli. At the apex of the ventral stream called the inferotemporal cortex, neurons became tuned to complex stimuli, such as faces. The specific tuning of intermediate neurons in the ventral stream is less clear, because the range of form variety that can be utilized for probing is nearly infinite. In the anterior part of the ventral stream, various regions appear to be tuned selectively to identify body parts (extrastriate body area), faces (fusiform face area) (according to a recent paper by Adamson and Troiani (2018) regions of the fusiform face area respond equally to "food"), moving bodies (posterior superior temporal sulcus), or even scenes (parahippocampal place area). Neuronal tuning in these areas requires fine discrimination among complex patterns in each relevant category for object recognition. Recent findings suggest that this fine discrimination is a function of expertise and the individual level of categorization with stimuli. Specifically, work has been done by Gauthier et al (2001) to show fusiform face area (FFA) activation for birds in bird experts and cars in car experts when compared to the opposing stimuli. Gauthier et al (2002) also utilized a new class of objects called Greebles and trained people to recognize them at individual levels. After training, the FFA was tuned to distinguish between this class of objects as well as faces. Curran et al (2002) similarly trained people in a less structured class of objects called "blobs" and showed FFA selective activation for them. Overall, neurons can be tuned selectively discriminate between certain sets of stimuli that are experienced regularly in the world.

Tuning in Other Systems Neurons in other systems also become selectively tuned to stimuli. In the auditory system, different neurons may respond selectively to the frequency (pitch), amplitude (loudness), and/or complexity (uniqueness) of sounds. In the olfactory system, neurons may be tuned to certain kinds of smells. In the gustatory system, different neurons may respond selectively to different components of food: sweet, sour, salty, and bitter. In the somatosensory system, neurons may be selectively tuned to different types of pressure, temperature, bodily position, and pain. This tuning in the somatosensory system also provides feedback to the motor system so that it may selectively tune neurons to respond in specific ways to given stimuli. Finally, the encoding and storage of information in both short-term and long-term memory requires the tuning of neurons in complex ways such that information may be later retrieved.

References

Worked examples

Example 1 — a first encounter with Neuronal tuning

Start with the simplest possible case. Write down what Neuronal tuning claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, 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 Neuronal tuning 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 Neuronal tuning 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 Neuronal tuning

In research
Neuronal tuning appears in biology 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 Neuronal tuning 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
Neuronal tuning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Neurophysiology, so understanding it makes those chapters shorter.
In everyday life
Look for Neuronal tuning 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 Neuronal tuning in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Neuronal tuning 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 Neuronal tuning out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Neuronal tuning in simple terms?

In neuroscience, neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through exper…

Why does Neuronal tuning matter?

Because it connects several biology 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 Neuronal tuning?

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 Neuronal tuning.

Tags

  • Neurophysiology

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