Statistics of visual responses in primate inferotemporal cortex to object stimuli

Abstract

We have characterized selectivity and sparseness in anterior inferotemporal cortex, using a large data set. Responses were collected from 674 monkey inferotemporal cells, each stimulated by 806 object photographs. This 806 × 674 matrix was examined in two ways: columnwise, looking at responses of a single neuron to all images (single-neuron selectivity), and rowwise, looking at the responses of all neurons caused by a single image (population sparseness). Selectivity and sparseness were measured as kurtosis of probability distributions. Population sparseness exceeded single-neuron selectivity, with specific values dependent on the size of the data sample. This difference was principally caused by inclusion, within the population, of neurons with a variety of dynamic ranges (standard deviations of responses over all images). Statistics of large responses were examined by quantifying how quickly the upper tail of the probability distribution decreased (tail heaviness). This analysis demonstrated that population responses had heavier tails than single-neuron responses, consistent with the difference between sparseness and selectivity measurements. Population responses with spontaneous activity subtracted had the heaviest tails, following a power law. The very light tails of single-neuron responses indicate that the critical feature for each neuron is simple enough to have a high probability of occurring within a limited stimulus set. Heavy tails of population responses indicate that there are a large number of different critical features to which different neurons are tuned. These results are inconsistent with some structural models of object recognition that posit that objects are decomposed into a small number of standard features.

Sample Skewness as a Statistical Measurement of Neuronal Tuning Sharpness

“…For large nonparametric sets of complex stimuli, the distributions of firing rates for neurons look similar to the example unimodal distributions presented in Figures 2A and 2D, with distributions such as exponential or gamma (Franco, Rolls, Aggelopoulos, & Jerez, 2007; Lehky, Kiani, Esteky, & Tanaka, 2011). For parametric sets of stimuli such as disparity and orientation, there are more complex bimodal distributions of firing rates.…”

Image familiarization sharpens response dynamics of neurons in inferotemporal cortex

“…The novel images, like the familiar ones, subtended approximately 4° and represented objects extracted from their background. It was feasible to use a small number of arbitrarily selected images because most TE neurons are broadly tuned 49, 50 . Trials conformed to eight conditions: four alternation conditions and four gap conditions.…”

Sparseness, Antisparseness and Anything in Between: The Operating Point of a Neuron Determines Its Computational Repertoire

“…Because some experimental evidence suggests that cortical neurons exhibit an exponential output firing rate distribution (Baddeley et al, 1997; but see Franco et al (2007) and Lehky et al (2011) for evidence for sparse but non-exponential firing rate distributions), and because an exponential distribution has maximum entropy on an unbounded interval, Triesch with parameterμ (Triesch, 2005a). Later, he adapts α and β by minimising the Kullback-Leibler divergence between f R (r) and g R (r) =μ −1 exp(−r/μ) (Triesch, 2005b(Triesch, , 2007, defined by…”

“…Franco et al, 2007;Lehky et al, 2011). Perhaps the simplest choice for g R (r) to achieve sparseness is…”