Receptive field
The term receptive field was first used by Sherrington in 1906 to describe the area of skin from which a scratch reflex could be elicited in a dog.
The weights learned by the linear model are the STRF, and represent the specific acoustic pattern that causes modulation in the firing rate of the neuron.
STRFs can also be understood as the transfer function that maps an acoustic stimulus input to a firing rate response output.
Large receptive fields allow the cell to detect changes over a wider area, but lead to a less precise perception.
However, the neurons are able to discriminate fine detail due to patterns of excitation and inhibition relative to the field which leads to spatial resolution.
For example, the receptive field of a single photoreceptor is a cone-shaped volume comprising all the visual directions in which light will alter the firing of that cell.
Traditionally, visual receptive fields were portrayed in two dimensions (e.g., as circles, squares, or rectangles), but these are simply slices, cut along the screen on which the researcher presented the stimulus, of the volume of space to which a particular cell will respond.
Studies based on perception do not give the full picture of the understanding of visual phenomena, so the electrophysiological tools must be used, as the retina, after all, is an outgrowth of the brain.
Each ganglion cell or optic nerve fiber bears a receptive field, increasing with intensifying light.
[6]: 188 Each receptive field is arranged into a central disk, the "center", and a concentric ring, the "surround", each region responding oppositely to light.
Retinal ganglion cell receptive fields convey information about discontinuities in the distribution of light falling on the retina; these often specify the edges of objects.
For complex-cell receptive fields, a correctly oriented bar of light might need to move in a particular direction in order to excite the cell.
For example, in the inferotemporal cortex, receptive fields cross the midline of visual space and require images such as radial gratings or hands.
This property was one of the earliest major results obtained through fMRI (Kanwisher, McDermott and Chun, 1997); the finding was confirmed later at the neuronal level (Tsao, Freiwald, Tootell and Livingstone, 2006).
[8][9][10] It is described how idealised models of receptive fields similar to the biological receptive fields[11][12] found in the retina, the LGN and the primary visual cortex can be derived from structural properties of the environment in combination with internal consistency to guarantee consistent representation of image structures over multiple spatial and temporal scales.
So, in a neural network context, the receptive field is defined as the size of the region in the input that produces the feature.
As an example, in motion-based tasks, like video prediction and optical flow estimation, large motions need to be captured (displacements of pixels in a 2D grid), so an adequate receptive field is required.
When used in this sense, the term adopts a meaning reminiscent of receptive fields in actual biological nervous systems.
The use of receptive fields in this fashion is thought to give CNNs an advantage in recognizing visual patterns when compared to other types of neural networks.
