A Convolutional Subunit Model for Neuronal Responses in Macaque V1.

The response properties of neurons in the early stages of the visual system can be described using the rectified responses of a set of self-similar, spatially shifted linear filters. In macaque primary visual cortex (V1), simple cell responses can be captured with a single filter, whereas complex cells combine a set of filters, creating position invariance. These filters cannot be estimated using standard methods, such as spike-triggered averaging. Subspace methods like spike-triggered covariance can recover multiple filters but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside, rather than the filters themselves. Here, we assume a linear-nonlinear-linear-nonlinear (LN-LN) cascade model in which the first LN stage consists of shifted ("convolutional") copies of a single filter, followed by a common instantaneous nonlinearity. We refer to these initial LN elements as the "subunits" of the receptive field, and we allow two independent sets of subunits, each with its own filter and nonlinearity. The second linear stage computes a weighted sum of the subunit responses and passes the result through a final instantaneous nonlinearity. We develop a procedure to directly fit this model to electrophysiological data. When fit to data from macaque V1, the subunit model significantly outperforms three alternatives in terms of cross-validated accuracy and efficiency, and provides a robust, biologically plausible account of receptive field structure for all cell types encountered in V1. SIGNIFICANCE STATEMENT: We present a new subunit model for neurons in primary visual cortex that significantly outperforms three alternative models in terms of cross-validated accuracy and efficiency, and provides a robust and biologically plausible account of the receptive field structure in these neurons across the full spectrum of response properties.

Cortical correlates of human motion perception biases.

Human sensory perception is not a faithful reproduction of the sensory environment. For example, at low contrast, objects appear to move slower and flicker faster than veridical. Although these biases have been observed robustly, their neural underpinning is unknown, thus suggesting a possible disconnect of the well established link between motion perception and cortical responses. We used functional imaging to examine the encoding of speed in the human cortex at the scale of neuronal populations and asked where and how these biases are encoded. Decoding, voxel population, and forward-encoding analyses revealed biases toward slow speeds and high temporal frequencies at low contrast in the earliest visual cortical regions, matching perception. These findings thus offer a resolution to the disconnect between cortical responses and motion perception in humans. Moreover, biases in speed perception are considered a leading example of Bayesian inference because they can be interpreted as a prior for slow speeds. Therefore, our data suggest that perceptual priors of this sort can be encoded by neural populations in the same early cortical areas that provide sensory evidence.

Efficient and direct estimation of a neural subunit model for sensory coding.

Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of spatially shifted linear filters. These filters cannot be estimated using spike-triggered averaging (STA). Subspace methods such as spike-triggered covariance (STC) can recover multiple filters, but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside rather than the filters themselves. Here, we assume a linear-nonlinear-linear-nonlinear (LN-LN) cascade model in which the first linear stage is a set of shifted ('convolutional') copies of a common filter, and the first nonlinear stage consists of rectifying scalar nonlinearities that are identical for all filter outputs. We refer to these initial LN elements as the 'subunits' of the receptive field. The second linear stage then computes a weighted sum of the responses of the rectified subunits. We present a method for directly fitting this model to spike data, and apply it to both simulated and real neuronal data from primate V1. The subunit model significantly outperforms STA and STC in terms of cross-validated accuracy and efficiency.

Acoustic analysis of vocal development in a New World primate, the common marmoset (Callithrix jacchus).

In contrast to humans and songbirds, there is limited evidence of vocal learning in nonhuman primates. While previous studies suggested that primate vocalizations exhibit developmental changes, detailed analyses of the extent and time course of such changes across a species' vocal repertoire remain limited. In a highly vocal primate, the common marmoset (Callithrix jacchus), we studied developmental changes in the acoustic structure of species-specific communication sounds produced in a social setting. We performed detailed acoustic analyses of the spectral and temporal characteristics of marmoset vocalizations during development, comparing differences between genders and twin pairs, as well as with vocalizations from adult marmosets residing in the same colony. Our analyses revealed significant changes in spectral and temporal features as well as variability of particular call types over time. Infant and juvenile vocalizations changed progressively toward the vocalizations produced by adult marmosets. Call types observed early in development that were unique to infants disappeared gradually with age, while vocal exchanges with conspecifics emerged. Our observations clearly indicate that marmoset vocalizations undergo both qualitative and quantitative postnatal changes, establishing the basis for further studies to delineate contributions from maturation of the vocal apparatus and behavioral experience.