Biology subjects

Perrinet, L. U.

Publications and source records attributed to Perrinet, L. U..

5 recordsLinked to original sources

bioRxiv2021

Pooling in a predictive model of V1 explains functional and structural diversity across species

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of cortical orientation maps in higher mammals V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a model of V1 based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence of complex cells as well as cortical orientation maps in V1, as observed in distinct species of mammals. By using different pooling functions, our model developed complex cells in networks that exhibit orientation maps (e.g., like in carnivores and primates) or not (e.g., rodents and lagomorphs). The SDPC can therefore be viewed as a unifying framework that explains the diversity of structural and functional phenomena observed in V1. In particular, we show that orientation maps emerge naturally as the most cost-efficient structure to generate complex cells under the predictive coding principle. SignificanceCortical orientation maps are among the most fascinating structures observed in higher mammals brains: In such maps, similar orientations in the input image activate neighboring cells in the cortical surface. However, the computational advantage brought by these structures remains unclear, as some species (rodents and lagomorphs) completely lack orientation maps. In this study, we introduce a computational model that links the presence of orientation maps to a class of nonlinear neurons called complex cells. In particular, we propose that the presence or absence orientation maps correspond to different strategies employed by different species to generate invariance to complex stimuli.

neuroscience↗

bioRxiv2021

Dynamical processing of orientation precision in the primary visual cortex

In our daily visual environment, the primary visual cortex (V1) processes distributions of oriented features as the basis of our visual computations. Changes of the global, median orientation of such inputs form the basis of our canonical knowledge about V1. However, another overlooked but defining characteristic of these sensory variables is their precision, which characterizes the level of variance in the input to V1. Such variability is an intrinsic part of natural images, yet it remains unclear if and how V1 accounts for the changes in orientation precision to achieve its robust orientation recognition performances. Here, we used naturalistic stimuli to characterize the response of V1 neurons to quantified variations of orientation precision. We found that about thirty percent of the recorded neurons showed a form of invariant responses to input precision. While feedforward mechanisms failed to account for the existence of these resilient neurons, neuronal competition within V1 explained the extent to which a neuron is invariant to precision. Using a decoding algorithm, we showed that the existence of such neurons in the population response of V1 can serve to encode both the orientation and its precision in the V1 population activity, which improves the robustness of the overall neural code. These precision-specific neurons operate with slow recurrent cortical dynamics, which supports the notion of predictive precisionweighted processes in V1.

neuroscience↗

bioRxiv2019

Humans adapt their anticipatory eye movements to the volatility of visual motion properties

Animal behavior constantly adapts to changes, for example when the statistical properties of the environment change unexpectedly. For an agent that interacts with this volatile setting, it is important to react accurately and as quickly as possible. It has already been shown that when a random sequence of motion ramps of a visual target is biased to one direction (e.g. right or left), human observers adapt their eye movements to accurately anticipate the targets expected direction. Here, we prove that this ability extends to a volatile environment where the probability bias could change at random switching times. In addition, we also recorded the explicit prediction of the next outcome as reported by observers using a rating scale. Both results were compared to the estimates of a probabilistic agent that is optimal in relation to the assumed generative model. Compared to the classical leaky integrator model, we found a better match between our probabilistic agent and the behavioral responses, both for the anticipatory eye movements and the explicit task. Furthermore, by controlling the level of preference between exploitation and exploration in the model, we were able to fit for each individuals experimental dataset the most likely level of volatility and analyze inter-individual variability across participants. These results prove that in such an unstable environment, human observers can still represent an internal belief about the environmental contingencies, and use this representation both for sensory-motor control and for explicit judgments. This work offers an innovative approach to more generically test the diversity of human cognitive abilities in uncertain and dynamic environments. Author summaryUnderstanding how humans adapt to changing environments to make judgments or plan motor responses based on time-varying sensory information is crucial for psychology, neuroscience and artificial intelligence. Current theories for how we deal with the environments uncertainty, that is, in response to the introduction of some randomness change, mostly rely on the behavior at equilibrium, long after after a change. Here, we show that in the more ecological case where the context switches at random times all along the experiment, an adaptation to this volatility can be performed online. In particular, we show in two behavioral experiments that humans can adapt to such volatility at the early sensorimotor level, through their anticipatory eye movements, but also at a higher cognitive level, through explicit ratings. Our results suggest that humans (and future artificial systems) can use much richer adaptive strategies than previously assumed.

neuroscience↗

bioRxiv2019

A dual foveal-peripheral visual processing model implements efficient saccade selection

We develop a visuomotor model that implements visual search as a focal accuracy-seeking policy, with the targets position and category drawn independently from a common generative process. Consistently with the anatomical separation between the ventral versus dorsal pathways, the model is composed of two pathways, that respectively infer what to see and where to look. The "What" network is a classical deep learning classifier, that only processes a small region around the center of fixation, providing a "foveal" accuracy. In contrast, the "Where" network processes the full visual field in a biomimetic fashion, using a log-polar retinotopic encoding, which is preserved up to the action selection level. The foveal accuracy is used to train the "Where" network. After training, the "Where" network provides an "accuracy map" that serves to guide the eye toward peripheral objects. The comparison of both networks accuracies amounts to either select a saccade or to keep the eye at the center to identify the target. We test this setup on a simple task of finding a digit in a large, cluttered image. Our simulation results demonstrate the effectiveness of this approach, increasing by one order of magnitude the radius of the visual field toward which the agent can detect and recognize a target, either through a single saccade or with multiple ones. Importantly, our log-polar treatment of the visual information exploits the strong compression rate performed at the sensory level, providing ways to implement visual search in a sub-linear fashion, in contrast with mainstream computer vision.

neuroscience↗

bioRxiv2018

Speed-Selectivity in Retinal Ganglion Cells is Modulated by the Complexity of the Visual Stimulus

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, is remain unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently. Interestingly, these properties were observed throughout different populations of RGCs. Thus, our results reveal that the response at the level of RGCs is modulated by the naturalness of the stimulus - in particular for motion - which suggests that the tuning to the statistics of natural images already emerges at the level of the retina.

neuroscience↗