SummaryThe auditory system must represent sounds with a wide range of statistical properties. One important property is the spectrotemporal contrast in the acoustic environment: the variation in sound pressure in each frequency band, relative to the mean pressure. We show that neurons in ferret auditory cortex rescale their gain to partially compensate for the spectrotemporal contrast of recent stimulation. When contrast is low, neurons increase their gain, becoming more sensitive to small changes in the stimulus, although the effectiveness of contrast gain control is reduced at low mean levels. Gain is primarily determined by contrast near each neuron's preferred frequency, but there is also a contribution from contrast in more distant frequency bands. Neural responses are modulated by contrast over timescales of ∼100 ms. By using contrast gain control to expand or compress the representation of its inputs, the auditory system may be seeking an efficient coding of natural sounds.

It is often suggested that efficient neural codes for natural visual information should be 'sparse'. However, the term 'sparse' has been used in two different ways--firstly to describe codes in which few neurons are active at any time ('population sparseness'), and secondly to describe codes in which each neuron's lifetime response distribution has high kurtosis ('lifetime sparseness'). Although these ideas are related, they are not identical, and the most common measure of lifetime sparseness--the kurtosis of the lifetime response distributions of the neurons--provides no information about population sparseness. We have measured the population sparseness and lifetime kurtosis of several biologically inspired coding schemes. We used three measures of population sparseness (population kurtosis, Treves-Rolls sparseness and 'activity sparseness'), and found them to be in close agreement with one another. However, we also measured the lifetime kurtosis of the cells in each code. We found that lifetime kurtosis is uncorrelated with population sparseness for the codes we used. Lifetime kurtosis is not, therefore, a useful measure of the population sparseness of a code. Moreover, the Gabor-like codes, which are often assumed to have high population sparseness (since they have high lifetime kurtosis), actually turned out to have rather low population sparseness. Surprisingly, principal components filters produced the codes with the highest population sparseness.

Along the auditory pathway from auditory nerve to midbrain to cortex, individual neurons adapt progressively to sound statistics, enabling the discernment of foreground sounds, such as speech, over background noise.

The responses of simple cells in primary visual cortex to sinusoidal gratings can primarily be predicted from their spatial receptive fields, as mapped using spots or bars. Although this quasilinearity is well documented, it is not clear whether it holds for complex natural stimuli. We recorded from simple cells in the primary visual cortex of anesthetized ferrets while stimulating with flashed digitized photographs of natural scenes. We applied standard reverse-correlation methods to quantify the average natural stimulus that invokes a neuronal response. Although these maps cannot be the receptive fields, we find that they still predict the preferred orientation of grating for each cell very well (r = 0.91); they do not predict the spatial-frequency tuning. Using a novel application of the linear reconstruction method called regularized pseudoinverse, we were able to recover high-resolution receptive-field maps from the responses to a relatively small number of natural scenes. These receptive-field maps not only predict the optimum orientation of each cell (r = 0.96) but also the spatial-frequency optimum (r = 0.89); the maps also predict the tuning bandwidths of many cells. Therefore, our first conclusion is that the tuning preferences of the cells are primarily linear and constant across stimulus type. However, when we used these maps to predict the actual responses of the cells to natural scenes, we did find evidence of expansive output nonlinearity and nonlinear influences from outside the classical receptive fields, orientation tuning, and spatial-frequency tuning.

Area V2 is a major visual processing stage in mammalian visual cortex, but little is currently known about how V2 encodes information during natural vision. To determine how V2 represents natural images, we used a novel nonlinear system identification approach to obtain quantitative estimates of spatial tuning across a large sample of V2 neurons. We compared these tuning estimates with those obtained in area V1, in which the neural code is relatively well understood. We find two subpopulations of neurons in V2. Approximately one-half of the V2 neurons have tuning that is similar to V1. The other half of the V2 neurons are selective for complex features such as those that occur in natural scenes. These neurons are distinguished from V1 neurons mainly by the presence of stronger suppressive tuning. Selectivity in these neurons therefore reflects a balance between excitatory and suppressive tuning for specific features. These results provide a new perspective on how complex shape selectivity arises, emphasizing the role of suppressive tuning in determining stimulus selectivity in higher visual cortex.