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The receptive field is a term originally coined by Sherrington (1906) to describe an area of the body surface where a stimulus could elicit a reflex. Hartline extended the term to sensory neurons defining the receptive field as a restricted region of visual space where a luminous stimulus could drive electrical responses in a retinal ganglion cell. In Hartline’s own words, ‘Responses can be obtained in a given optic nerve fiber only upon illumination of a certain restricted region of the retina, termed the receptive field of the fiber’. After Hartline (1938), the term receptive field has been extended to other neurons in the visual pathway, other sensory neurons and other sensory pathways.
A general definition that spans different types of neurons across sensory modalities could be expressed as follows. The receptive field is a portion of sensory space that can elicit neuronal responses when stimulated. The sensory space can be defined in a single dimension (e.g. carbon chain length of an odorant), two dimensions (e.g. skin surface) or multiple dimensions (e.g. space, time and tuning properties of a visual receptive field). The neuronal response can be defined as firing rate (i.e. number of action potentials generated by a neuron) or include also subthreshold activity (i.e. depolarizations and hyperpolarizations in membrane potential that do not generate action potentials).
Visual receptive fields
The receptive field of a visual neuron comprises a two-dimensional region in visual space whose size can range from a few minutes of arc (a dot in this page at reading distance) to tens of degrees (the entire page). The receptive field size increases at successive processing stages in the visual pathway and, at each processing stage, it increases with the distance from the point of fixation (eccentricity).
Retinal ganglion cells located at the center of vision, in the fovea, have the smallest receptive fields and those located in the visual periphery have the largest receptive fields. The large receptive field size of neurons in the visual periphery explains the poor spatial resolution of our vision outside the point of fixation (other factors are photoreceptor density and optical aberrations). To become aware of the poor spatial resolution in our retinal periphery, try to read this line of text while fixating your eyes in a single letter. The letter that you are fixating is being projected at the center of your fovea where the receptive fields of retinal ganglion cells are smallest. The letters that surround the point of fixation are being projected in the peripheral retina. You will notice that you can identify just a few letters surrounding the point of fixation and that you need to move your eyes if you want to read the entire line of text.
Modern studies have expanded the term receptive field to include a temporal dimension. The spatiotemporal receptive field describes the relation between the spatial region of visual space where neuronal responses are evoked and the temporal course of the response. The relation between the spatial and temporal dimensions of the receptive field is particularly important to understand direction selective responses from neurons in primary visual cortex (Adelson & Bergen, 1985; Reid, Soodak, & Shapley, 1987; Watson & Ahumada, 1983).
Direction selective neurons respond to some directions of movement better than others. For example, a neuron may respond to a vertical line moving leftwards but not moving rightwards. The direction selective neurons generate visual responses with different time delays at different regions of the receptive field. Some regions respond faster to visual stimuli than others. As a consequence of these differences in response timing, a line moving from a slow to a fast region generates a stronger response than a line moving from a fast to a slow region. When the line moves in the optimal direction, the slow region, which is stimulated first, responds approximately at the same time as the fast region, which is stimulated later. To make an analogy, imagine that two people are trying to say ‘response’ at the same time but one of them is speaking through a microphone that has a temporal delay of one second. The person that is using the delayed microphone has to say ‘response’ one second before the other for the two voices to fuse in unison. The result is a stronger ‘response’ than when the order is reversed.
Visual receptive fields are sometimes described as 3-dimensional volumes in visual space to include depth in addition to planar space. However, this use of ‘receptive field’ is less common and it is usually restricted to cortical neurons whose responses are modulated by visual depth.
Neurons at different stages in the visual pathway have receptive fields that differ not only in size but also in structure. The complexity of the receptive field structure, just as the receptive field size, increases at successive stages of the visual pathway. Most neurons in the retina and thalamus have small receptive fields that have a very basic organization, which resembles two concentric circles. This concentric receptive field structure is usually known as center-surround organization, a term that was originally coined by Kuffler (1953). On-center retinal ganglion cells respond to light spots surrounded by dark backgrounds like a star in a dark sky. Off-center retinal ganglion cells respond to dark spots surrounded by light backgrounds like a fly in a bright sky.
In primary visual cortex, receptive fields are much more diverse and more complicated than in the retina and thalamus. Only a few cortical receptive fields resemble the structure of thalamic receptive fields, while others have elongated subregions that respond to either dark or light spots, others respond similarly to light and dark spots through the entire receptive field and others do not respond to spots at all.
Hubel and Wiesel (1962) provided the first characterization of receptive fields in primary visual cortex and the first classification of cortical cells based on their receptive field structures. Some cortical cells respond to light and dark spots in different subregions of the receptive field and the arrangement of these subregions can be used to predict the responses of the cell to visual stimuli such as lines, bars or squared shapes. Cells with separate subregions that respond to either light or dark spots are called simple cells. All other cells in visual cortex that do not have separate subregions (the majority of the cells) are called complex cells (Martinez & Alonso, 2003).
Since Hubel and Wiesel (1962), other methods to classify cortical receptive fields have been proposed. However, to this date, no classification method has been widely adopted by the entire scientific community. Among all the classification methods after Hubel and Wiesel, the one that has been most widely used is based on the responses of cortical neurons to sinusoidal drifting gratings. Some cortical neurons respond to the sinusoidal changes in luminance by generating a rectified sinusoidal response (which is a rough linear replica of the stimulus) while others respond by increasing the mean firing rate. A quantitative measurement of response linearity can be obtained by Fourier analysis. Response linearity is bimodally distributed (Skottun et al., 1991) but the significance of this bimodal distribution is still a matter of debate (Mechler & Ringach, 2002; Priebe, Mechler, Carandini, & Ferster, 2004).
The great diversity of receptive fields in primary visual cortex makes it difficult to correlate neuronal classes with receptive field properties, as is currently possible in the retina (e.g. Masland, 2001). Neurons in primary visual cortex can respond selectively to different attributes of the visual scene such as line orientation, direction of movement, luminance contrast, stimulus velocity, color, retinal disparity and spatial frequency (frequency of black and white stripes in a degree of visual space).
Most neurons in primary visual cortex respond to moving lines and are selective to line orientation. Some neurons are sharply tuned to orientation and fail to respond to lines that are just a bit tilted from their preferred orientation while other cortical neurons are broadly tuned and respond to a broad range of orientations. The selectivity of each neuron to line orientation and other parameters is determined to a great extent by the receptive field structure. A very active area of research aims to build realistic models of receptive field structures that can explain neuronal responses to different stimuli. The most successful models to date were built for neurons at the earliest stages of the visual pathway. For example, the receptive fields of retinal and thalamic neurons can be modeled quite accurately with a difference of Gaussians (DOG, Rodieck, 1965). Also, the receptive fields of visual cortical neurons that receive direct input from the thalamus can be modeled with Gabor functions (Jones & Palmer, 1987). More complex models combine multiple functions to accurately reproduce the response of a neuron to different stimuli. These receptive field models aim to provide information about all possible stimuli that would best drive neuronal responses (e.g. Bonin, Mante, & Carandini, 2005; Rust, Schwartz, Movshon, & Simoncelli, 2005).
The receptive field size of neurons in primary visual cortex depends strongly on the stimulus contrast. The size can be more than two times larger when measured with low contrast stimuli than when measured with high contrast stimuli. Moreover, when measured with high contrast stimuli, the neural responses to low contrast lines can be increased by presenting collinear high contrast lines outside of the receptive field (Polat, Mizobe, Pettet, Kasamatsu, & Norcia, 1998; Sceniak, Ringach, Hawken, & Shapley, 1999).
Neurons in higher cortical areas have large receptive fields and can be more selective to the identity of the stimulus than to its physical location. For example, neurons in the inferotemporal cortex respond selectively to objects and faces (Bruce, Desimone, & Gross, 1981; Desimone, Albright, Gross, & Bruce, 1984; Tsao & Livingstone, 2008) and a remarkable example of selectivity for stimulus identity was recently reported for a neuron in temporal cortex. This neuron responded selectively to the identity ‘Halle Berry’, either presented as a face or simply as the written name of the actress (Quiroga, Reddy, Kreiman, Koch, & Fried, 2005).
Somatosensory receptive fields
The receptive fields of somatosensory neurons share much in common with the receptive fields of visual neurons. As for visual neurons, the somatosensory receptive fields comprise a restricted 2-dimensional region of space where a stimulus can evoke a neuronal response. In somatosensory neurons, however, space refers to a region of the body and the stimulus can be touch, vibration, temperature or pain.
Similar to visual neurons, the receptive fields of somatosensory neurons are smaller in the regions of the body where the perceptual spatial resolution is highest. The fingertips have the highest spatial resolution (and the smallest receptive fields) while the thigh and calf region have the lowest spatial resolution (and largest receptive fields). The spatial resolution to light-touch stimulation can be evaluated by measuring two-point discrimination thresholds. The subject has to report whether the skin is touched either with one or two pointy objects that are closely spaced. When the distance between the two objects is small, it is not possible to reliably distinguish between one or two objects touching. The minimum distance that is required to distinguish two pointy objects is called the two point discrimination threshold. The two point discrimination threshold is less than 5 mm at the finger tips and is about 40 mm at the thigh.
As in the visual system, the receptive fields in the somatosensory thalamus have center-surround organization and those in the somatosensory cortex have more complex receptive field structures that make the neurons selective to the orientation and direction of motion of a stimulus.
Auditory receptive fields
The auditory sensory epithelium responds selectively to the sound frequency and not to the spatial location of the stimulus as is the case in the visual and somatosensory systems. Consequently, it is sound frequency that defines the auditory receptive fields at the earliest stages of sensory processing.
Whereas in the visual and somatosensory systems the spatial receptive fields are constructed directly from connections originating in the sensory epitheliums (receptors in retina and skin), in the auditory system, the spatial receptive fields have to be synthesized by specific circuits that compare differences in stimulus intensity and timing between the two ears. Therefore, in auditory physiology, the term receptive field is frequently used with two different meanings. As a first meaning, an auditory receptive field can refer to the range of sound frequencies that most optimally stimulate the neuron (auditory spectrotemporal receptive field). As a second meaning, an auditory receptive field can refer to the region in auditory space where a stimulus can evoke a neuronal response (auditory spatial receptive field).
The sensory organ in the auditory system, the cochlea, has a precise representation of sound frequency, which is organized like a piano scale: lower tones are represented at the apex of the cochlea and higher tones at the base. Neurons at different stages of the auditory pathway can be very sensitive to small variations in sound frequency and their responses can have different time courses. Both the frequency range and time course of the response are quantitatively represented in the spectrotemporal receptive field.
In contrast, the auditory spatial receptive field resembles more the visual and somatosensory receptive fields in that it represents an area of space where a stimulus (sound) generates a neuronal response. Like with visual and somatosensory receptive fields, spatial receptive fields at early stages in the auditory pathway have center-surround organization. For example, some auditory neurons in the midbrain respond to sounds presented at a defined region of auditory space, which is the receptive field center, and the response is reduced when the stimulus is presented in a region surrounding the center, which is the receptive field surround. The center-surround receptive fields of auditory neurons cover a much larger region of space than visual and somatosensory receptive fields with similar center-surround organization. Auditory spatial receptive fields tend to be located in front of the animal and they can be restricted to a single quadrant in the contralateral side of the brain where the neuron is recorded (Knudsen & Konishi, 1978).
Olfactory receptive fields
The receptive fields of olfactory neurons have been much less studied than the receptive fields of neurons in other sensory systems. The olfactory receptive fields are particularly difficult to characterize because the odor parameters that define the olfactory space are poorly known. Recent evidence indicates that olfactory receptive fields are mapped along the dimension of molecular carbon chain length. The receptive fields of cells at early stages of olfactory processing often include inhibitory surrounds to the longest and shortest effective chain lengths (Wilson, 2001).
Auditory spatial receptive field: The region of space where a sound can generate a response in an auditory neuron.
Auditory spectrotemporal receptive field: Spectrum of sound frequencies that generate a response in an auditory neuron (represented as a function of the time-course of the response).
Broadly tuned: Refers to neurons that respond similarly to wide range of variations within a given stimulus dimension. For example, neurons that have broad orientation tuning respond similarly to all line orientations ( Figure 5b). Neurons that have broad spatial frequency tuning respond similarly to a wide range of spatial frequencies.
Cochlea: A portion of the inner ear which is a spiraled, hollow, conical chamber of bone. The auditory sensory neurons are located inside the cochlea.
Complex cells: Neurons in the primary visual cortex that cannot be classified as simple cells (they do not respond to light and dark spots in different regions of the receptive field).
Direction selective: Neurons that respond strongly to a specific direction of movement and fail to respond (or respond weaker) to the opposite direction.
DOG: A function that results from the difference of two Gaussian functions. DOG stands for difference of Gaussians.
Eccentricity: The distance between the receptive field center of a given neuron and the center of vision (point of fixation or fovea).
Electrical responses: Electrical activity that neurons generate in response to a sensory stimulus. The term ‘electrical response’ is usually reserved for membrane depolarizations that lead to action potentials (also called spikes) in an individual neuron. Such spikes can be extracellularly recorded with microelectrodes, which is the technique most frequently used to map neuronal receptive fields.
Fourier analysis: A mathematical method to characterize general functions by sums of simpler circular functions (sinusoidal and cosinusoidal functions). It is used to extract specific frequency components from the PSTHs of the neuronal responses and measure the amplitude and phase of each component.
Fovea: A small region of the retina (~1 mm diameter for the human fovea) where cone photoreceptors are most densely packed to provide the highest visual acuity.
Gabor functions: Functions that result from the multiplication of a sinusoidal function with a Gaussian function.
Mean firing rate: Total number of spikes averaged over time and over multiple stimulus presentations. It is usually measured in spikes per second.
Minute of arc: One sixtieth of a visual degree.
Receptive field: A specific region of sensory space in which an appropriate stimulus can drive an electrical response in a sensory neuron.
Rectified sinusoid: A sinusoidal function that is half-wave rectified (all negative values are set to zero).
Retinal ganglion cell: Neuron located in the inner part of the retina (part facing the pupil), which carries visual information from the eye to the deep structures of the brain.
Sharply tuned: Refers to neurons that respond only to a narrow range of variations in a given stimulus dimension. For example, neurons that have sharp orientation tuning respond only to a narrow range of line orientations ( Figure 5a). Neurons that have sharp spatial frequency tuning respond only to a narrow range of spatial frequencies.
Simple cells: Neurons in the primary visual cortex, whose receptive fields have separate subregions that respond either to light or dark spots. The discovery of simple cells in visual cortex and the first use of the term ‘simple cell’ date back to Hubel and Wiesel (1962). Hubel and Wiesel defined simple cells as cells in the cat primary visual cortex whose receptive fields meet four different criteria: 1) the receptive fields can be subdivided into distinct excitatory and inhibitory regions; 2) there is summation within the separate excitatory and inhibitory parts; 3) there is antagonism between excitatory and inhibitory regions; 4) it is possible to predict the responses to stationary or moving spots of various shapes from a map of the excitatory and inhibitory areas.
Spatial frequency: Frequency of black and white stripes in a degree of visual space. It is measured in cycles per degree. One cycle is a set of a black and a white stripe. For example, a pattern of black-white-black-white stripes contained in a degree has a spatial frequency of 2 cycles per degree.
Spatiotemporal receptive field: A spatial receptive field plotted at different time delays between stimulus and neuronal response.
Visual degree: The amount of visual space covered by a cone of 1 degree angle with its apex located at the fovea of the retina. One visual degree covers a circle of 1 cm diameter when the distance between the eye and the fixation point is 57 cm. When the distance is 114 cm distance, one visual degree covers a circle of 2 cm diameter.
Visual periphery: Part of the visual field that is projected on the non-foveal retina (also called peripheral retina).
- Adelson, E H and Bergen, J R (1985). Spatiotemporal energy models for the perception of motion. J Opt Soc Am A 2(2): 284-299. doi:10.1364/josaa.2.000284.
- Bonin, V; Mante, V and Carandini, M (2005). The suppressive field of neurons in lateral geniculate nucleus. J Neurosci 25(47): 10844-10856. doi:10.1523/jneurosci.3562-05.2005.
- Bruce, C; Desimone, R and Gross, C G (1981). Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. J Neurophysiol 46(2): 369-384.
- Chen, Y et al. (2009). The linearity and selectivity of neuronal responses in awake visual cortex. J Vision 9(9): 1-17. doi:10.1167/9.9.12.
- Desimone, R; Albright, T D; Gross, C G and Bruce, C (1984). Stimulus-selective properties of inferior temporal neurons in the macaque. J Neurosci 4(8): 2051-2062.
- Hartline, H K (1938). The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. Am J Physiol 121: 400-415.
- Hubel, D H and Wiesel, T N (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol 160: 106-154.
- Jones, J P and Palmer, L A (1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6): 1233-1258.
- Knudsen, E I and Konishi, M (1978). Center-surround organization of auditory receptive fields in the owl. Science 202(4369): 778-780. doi:10.1126/science.715444.
- Kuffler, S W (1953). Discharge patterns and functional organization of mammalian retina. J Neurophysiol 16(1): 37-68.
- Martinez, L M and Alonso, J M (2003). Complex receptive fields in primary visual cortex. Neuroscientist 9(5): 317-331. doi:10.1177/1073858403252732.
- Masland, R H (2001). Neuronal diversity in the retina. Curr Opin Neurobiol 11(4): 431-436. doi:10.1016/s0959-4388(00)00230-0.
- Mechler, F and Ringach, D L (2002). On the classification of simple and complex cells. Vision Res 42(8): 1017-1033. doi:10.1016/s0042-6989(02)00025-1.
- Movshon, J A; Thompson, I D and Tolhurst, D J (1978). Spatial summation in the receptive fields of simple cells in the cat's striate cortex. J Physiol 283: 53-77.
- Polat, U; Mizobe, K; Pettet, M W; Kasamatsu, T and Norcia, A M (1998). Collinear stimuli regulate visual responses depending on cell's contrast threshold. Nature 391(6667): 580-584.
- Priebe, N J; Mechler, F; Carandini, M and Ferster, D (2004). The contribution of spike threshold to the dichotomy of cortical simple and complex cells. Nat Neurosci 7(10): 1113-1122. doi:10.1038/nn1310.
- Quiroga, R Q; Reddy, L; Kreiman, G; Koch, C and Fried, I (2005). Invariant visual representation by single neurons in the human brain. Nature 435(7045): 1102-1107. doi:10.1038/nature03687.
- Reid, R C; Soodak, R E and Shapley, R M (1987). Linear mechanisms of directional selectivity in simple cells of cat striate cortex. Proc Natl Acad Sci U S A 84(23): 8740-8744. doi:10.1073/pnas.84.23.8740.
- Rodieck, R W (1965). Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Res 5(11): 583-601. doi:10.1016/0042-6989(65)90033-7.
- Rust, N C; Schwartz, O; Movshon, J A and Simoncelli, E P (2005). Spatiotemporal elements of macaque v1 receptive fields. Neuron 46(6): 945-956. doi:10.1016/j.neuron.2005.05.021.
- Sceniak, M P; Ringach, D L; Hawken, M J and Shapley, R (1999). Contrast's effect on spatial summation by macaque V1 neurons. Nat Neurosci 2(8): 733-739.
- Sherrington, C S (1906). The integrative action of the nervous system. C Scribner and Sons, New York.
- Skottun, B C et al. (1991). Classifying simple and complex cells on the basis of response modulation. Vision Res 31(7-8): 1079-1086. doi:10.1016/0042-6989(91)90033-2.
- Tsao, D Y and Livingstone, M S (2008). Mechanisms of face perception. Annu Rev Neurosci 31: 411-437. doi:10.1146/annurev.neuro.30.051606.094238.
- Watson, A B and Ahumada, A J (1983). A look at motion in the frequency domain. NASA Technical Memorandum, 84352.
- Wilson, D A (2001). Receptive fields in the rat piriform cortex. Chem Senses 26(5): 577-584. doi:10.1093/chemse/26.5.577.
- Olaf Sporns (2007) Complexity. Scholarpedia, 2(10):1623.
- Rodolfo Llinas (2008) Neuron. Scholarpedia, 3(8):1490.
- John Dowling (2007) Retina. Scholarpedia, 2(12):3487.
- S. Murray Sherman (2006) Thalamus. Scholarpedia, 1(9):1583.
- Hubel, D H (1988). Eye, Brain and Vision. Henry Holt and Company Inc, http://hubel.med.harvard.edu/bcontex.htm.
- Hubel, D H and Wiesel, T N (2005). Brain and Visual perception: The story of a 25-year collaboration. New York, Oxford University press.
- Kandel, E R; Schwartz, J H and Jessell, T M (2000). Principles of Neural Science. McGraw Hill, USA.
- Simoncelli, E P; Paninski, L; Pilow, J and Schwartz, O (2004). Characterization of neural responses with stochastic stimuli. In M. Gazzaniga (Ed.), The new cognitive neurosciences, 3rd edition: MIT Press.