Neuron Tuning

[Charise Zelnick]

Inneuroscience, 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.[1] Neuronal tuning can be strong and sharp, as observed in primary visual cortex (area V1) (but see Carandini et al 2005 [2]), 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.

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.[8] Neurons in area V4 are selectively tuned to different wavelengths, hues, and saturations of color.[9] The middle temporal or area V5 is specifically tuned to the speed and direction of moving stimuli.[9] At the apex of the ventral stream called the inferotemporal cortex, neurons became tuned to complex stimuli, such as faces.[8] 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.[10]

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"),[11] moving bodies (posterior superior temporal sulcus), or even scenes (parahippocampal place area).[9] Neuronal tuning in these areas requires fine discrimination among complex patterns in each relevant category for object recognition.[10] 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.[12] Gauthier et al (2002) also utilized a new class of objects called Greebles and trained people to recognize them at individual levels.[13] After training, the FFA was tuned to distinguish between this class of objects as well as faces.[13] Curran et al (2002) similarly trained people in a less structured class of objects called "blobs" and showed FFA selective activation for them.[14] Overall, neurons can be tuned selectively discriminate between certain sets of stimuli that are experienced regularly in the world.

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.[9] In the olfactory system, neurons may be tuned to certain kinds of smells.[9] In the gustatory system, different neurons may respond selectively to different components of food: sweet, sour, salty, and bitter.[9] In the somatosensory system, neurons may be selectively tuned to different types of pressure, temperature, bodily position, and pain.[9] 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.[9] 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.[9]

Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neuron's role is to encode the stimulus at the tuning curve peak, because high firing rates are the neuron's most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding.

Copyright: 2006 Butts and Goldman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Since the earliest studies of sensory systems [1], the contribution of individual neurons to sensory function has been assessed by measuring their responses to a relevant set of stimuli. The standard tool in performing this characterization is the neuronal tuning curve: a plot of the average firing rate of the neuron as a function of relevant stimulus parameters. Tuning curves have provided the first-order description of virtually every sensory system, from orientation columns in the vertebrate visual cortex, to place cells in the hippocampus and wind-detecting neurons in the cricket cercal system [2]. Despite their ubiquitous application and straightforward formulation, the interpretation of tuning curves remains an issue of debate.

The most common interpretation of tuning curves is that the stimuli at their peak, which evoke the highest firing rates, are most important to a neuron. For example, orientation columns in the visual cortex are typically labeled by the orientation that evokes the most activity at each location, effectively identifying each neuron with a single stimulus at its tuning curve peak. Such a reduction has strong intuitive appeal because high firing rates are most distinguishable from background firing and other noise in the system.

In contrast, many studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve, because in these regions, small changes in the stimulus result in the largest changes in firing rate [2,3]. From this perspective, the peak of the tuning curve is a particularly insensitive region of the neuron's response because the slope at the peak is zero.

To illustrate the effect of noise on the encoding of stimuli, consider the canonical example of a sensory neuron characterized by a bell-shaped tuning curve function f(θ), representing the average firing rate of the neuron as a function of a stimulus parameter θ (Figure 1A, thick line). Across multiple presentations of the same stimulus, the neuron will have a distribution of responses sampled from p(rθ): the probability of r given stimulus θ. The width of this distribution represents the neuronal variability (Figure 1A, error bars and lines). Neuronal variability can be due to factors such as intrinsic noise, integration time of the neural response, and aspects of the stimulus not represented by the parameter θ (see Materials and Methods and Protocol S1).

(A) Typical tuning curve of a neuron, with mean firing rate (thick line) and standard deviation (thin lines) shown as a function of the stimulus parameter θ. These are reproduced as thin lines for reference in (B) and (D). In this example, the standard deviation of the firing rate for a given value of θ increases with increasing firing rate from a baseline value, although the particular form of noise chosen does not qualitatively affect our results. (B and D) The SSI(θ) is maximum in regions of high slope in the low-noise case (B), and maximum at the tuning curve peak in the high-noise case (D). (C and E) The specific information (solid line) in the low- and high-noise cases shown as a function of normalized firing rate. p(rθ) is shown for reference at θS (left) and θ0 = 0 (right).

How can variability affect how well a stimulus is encoded by a neuron? Intuitively, a stimulus is well encoded if it evokes unambiguous responses [6]. An unambiguous response is one that could only be evoked by a small number of stimuli so that the stimulus is readily identified when this response appears. For example, a modest firing rate might unambiguously represent a particular stimulus when there is no background activity, but will become more ambiguous as background activity increases.

Well encoded stimuli can thus be identified by their association with unambiguous responses as defined by isp(r). We therefore use the stimulus-specific information (SSI) [6] as our measure of stimulus encoding:

The results described below using the SSI are calculated numerically for a given tuning curve and model of variability (see Materials and Methods). Although we focus on the SSI metric because of its straightforward interpretation in terms of the relationship between stimulus and response in encoding, the results described below are also obtained with other information-based metrics, as demonstrated in Protocol S2.

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