Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

Abstract

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.

Strategic deployment of feature-based attentional gain in primate visual cortex

“…Interestingly, the right peak is modulated by not only a broad enhancement of as much as 22% to 26% but also a widening of the population activity profile (for details see S2 Text and S11 Fig). This is in agreement with a recently published analysis of our data, which similarly showed a widening of the peak corresponding to the attended pattern in a majority of the recorded neurons using a model-free approach [31]. Given a midlevel of the neuronal activity (see S2 Text and S12 Fig), ceiling effects as a reason for the reduced enhancement of the preferred stimulus can be ruled out.…”

Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field

“…While the perception of two directions under such conditions can be explained by assuming a particular decoding mechanism, our observed multiplexing of the individual stimulus representations provides other types of explanation for the apparent discrepancy between neural responses and perception. Additionally, the distinct encoding of the two motion surfaces through separate response states might also allow the visual system to separately manipulate the individual stimulus representations as apparent in the perceptual (Marshak and Sekuler, 1979) and physiological (Helmer et al, 2016) repulsion of the perceived angular separation in such transparent motion patterns.…”

Decomposing neural circuit function into information processing primitives

“…The quantification of IPPs allows to study information processing directly at the algorithmic level, in a way commensurable with, but “disembodied” from the specific circuit mechanisms producing it. This may allow detecting the action of specific cognitive processes (e.g., attentional modulation) through the identification of their informational signatures (e.g., boosted information modification) even when their effects are more general than a simple rescaling of tuning curves (Helmer et al, 2016). Furthermore, IPP analyses may allow detecting disruptions of primitive information processing itself, in absence of apparent “hardware” damage in the underlying circuits, thus providing a fundamental “software” explanation for widespread cognitive impairments in pathologies (Clawson et al, 2021).…”