Neuronal population interactions between brain areas
Most brain functions involve interactions among multiple, distinct areas or nuclei. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Using a population approach, we found that interactions between early visual cortical areas (V1 and V2) occur through a low-dimensional bottleneck, termed a communication subspace. In this talk, I will focus on the statistical methods we have developed for studying interactions between brain areas. First, I will describe Delayed Latents Across Groups (DLAG), designed to disentangle concurrent, bi-directional (i.e., feedforward and feedback) interactions between areas. Second, I will describe an extension of DLAG applicable to three or more areas, and demonstrate its utility for studying simultaneous Neuropixels recordings in areas V1, V2, and V3. Our results provide a framework for understanding how neuronal population activity is gated and selectively routed across brain areas.
Euclidean coordinates are the wrong prior for primate vision
The mapping from the visual field to V1 can be approximated by a log-polar transform. In this domain, scale is a left-right shift, and rotation is an up-down shift. When fed into a standard shift-invariant convolutional network, this provides scale and rotation invariance. However, translation invariance is lost. In our model, this is compensated for by multiple fixations on an object. Due to the high concentration of cones in the fovea with the dropoff of resolution in the periphery, fully 10 degrees of visual angle take up about half of V1, with the remaining 170 degrees (or so) taking up the other half. This layout provides the basis for the central and peripheral pathways. Simulations with this model closely match human performance in scene classification, and competition between the pathways leads to the peripheral pathway being used for this task. Remarkably, in spite of the property of rotation invariance, this model can explain the inverted face effect. We suggest that the standard method of using image coordinates is the wrong prior for models of primate vision.
Orientation selectivity in rodent V1: theory vs experiments
Neurons in the primary visual cortex (V1) of rodents are selective to the orientation of the stimulus, as in other mammals such as cats and monkeys. However, in contrast with those species, their neurons display a very different type of spatial organization. Instead of orientation maps they are organized in a “salt and pepper” pattern, where adjacent neurons have completely different preferred orientations. This structure has motivated both experimental and theoretical research with the objective of determining which aspects of the connectivity patterns and intrinsic neuronal responses can explain the observed behavior. These analysis have to take into account also that the neurons of the thalamus that send their outputs to the cortex have more complex responses in rodents than in higher mammals, displaying, for instance, a significant degree of orientation selectivity. In this talk we present work showing that a random feed-forward connectivity pattern, in which the probability of having a connection between a cortical neuron and a thalamic neuron depends only on the relative distance between them is enough explain several aspects of the complex phenomenology found in these systems. Moreover, this approach allows us to evaluate analytically the statistical structure of the thalamic input on the cortex. We find that V1 neurons are orientation selective but the preferred orientation of the stimulus depends on the spatial frequency of the stimulus. We disentangle the effect of the non circular thalamic receptive fields, finding that they control the selectivity of the time-averaged thalamic input, but not the selectivity of the time locked component. We also compare with experiments that use reverse correlation techniques, showing that ON and OFF components of the aggregate thalamic input are spatially segregated in the cortex.
Binocular combination of light
The brain combines signals across the eyes. This process is well-characterized for the perceptual anatomical pathway through V1 that primarily codes contrast, where interocular normalization ensures that responses are approximately equal for monocular and binocular stimulation. But we have much less understanding of how luminance is combined binocularly, both in the cortex and in subcortical structures that govern pupil diameter. Here I will describe the results of experiments using a novel combined EEG and pupillometry paradigm to simultaneously index binocular combination of luminance flicker in parallel pathways. The results show evidence of a more linear process than for spatial contrast, that may reflect different operational constraints in distinct anatomical pathways.
What does the primary visual cortex tell us about object recognition?
Object recognition relies on the complex visual representations in cortical areas at the top of the ventral stream hierarchy. While these are thought to be derived from low-level stages of visual processing, this has not been shown, yet. Here, I describe the results of two projects exploring the contributions of primary visual cortex (V1) processing to object recognition using artificial neural networks (ANNs). First, we developed hundreds of ANN-based V1 models and evaluated how their single neurons approximate those in the macaque V1. We found that, for some models, single neurons in intermediate layers are similar to their biological counterparts, and that the distributions of their response properties approximately match those in V1. Furthermore, we observed that models that better matched macaque V1 were also more aligned with human behavior, suggesting that object recognition is derived from low-level. Motivated by these results, we then studied how an ANN’s robustness to image perturbations relates to its ability to predict V1 responses. Despite their high performance in object recognition tasks, ANNs can be fooled by imperceptibly small, explicitly crafted perturbations. We observed that ANNs that better predicted V1 neuronal activity were also more robust to adversarial attacks. Inspired by this, we developed VOneNets, a new class of hybrid ANN vision models. Each VOneNet contains a fixed neural network front-end that simulates primate V1 followed by a neural network back-end adapted from current computer vision models. After training, VOneNets were substantially more robust, outperforming state-of-the-art methods on a set of perturbations. While current neural network architectures are arguably brain-inspired, these results demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in computer vision applications and results in better models of the primate ventral stream and object recognition behavior.
Role of primary visual cortex (V1) in visual awareness: insights from blindsight
A dynamical model of the visual cortex
In the past several years, I have been involved in building a biologically realistic model of the monkey visual cortex. Work on one of the input layers (4Ca) of the primary visual cortex (V1) is now nearly complete, and I would like to share some of what I have learned with the community. After a brief overview of the model and its capabilities, I would like to focus on three sets of results that represent three different aspects of the modeling. They are: (i) emergent E-I dynamics in local circuits; (ii) how visual cortical neurons acquire their ability to detect edges and directions of motion, and (iii) a view across the cortical surface: nonequilibrium steady states (in analogy with statistical mechanics) and beyond.
Design principles of adaptable neural codes
Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory systems should dynamically allocate limited resources when the goal is to optimally infer changing latent states of the environment, rather than reconstruct incoming stimuli. We use these ideas to explore dynamic tradeoffs between the efficiency and speed of sensory adaptation schemes, and the downstream computations that these schemes might support. Finally, we derive families of codes that balance these competing objectives, and we demonstrate their close match to experimentally-observed neural dynamics during sensory adaptation. These results provide a unifying perspective on adaptive neural dynamics across a range of sensory systems, environments, and sensory tasks.
Rhythmic Attentional Sampling: Spatial selection and beyond
A Cortical Circuit for Audio-Visual Predictions
Team work makes sensory streams work: our senses work together, learn from each other, and stand in for one another, the result of which is perception and understanding. Learned associations between stimuli in different sensory modalities can shape the way we perceive these stimuli (Mcgurk and Macdonald, 1976). During audio-visual associative learning, auditory cortex is thought to underlie multi-modal plasticity in visual cortex (McIntosh et al., 1998; Mishra et al., 2007; Zangenehpour and Zatorre, 2010). However, it is not well understood how processing in visual cortex is altered by an auditory stimulus that is predictive of a visual stimulus and what the mechanisms are that mediate such experience-dependent, audio-visual associations in sensory cortex. Here we describe a neural mechanism by which an auditory input can shape visual representations of behaviorally relevant stimuli through direct interactions between auditory and visual cortices. We show that the association of an auditory stimulus with a visual stimulus in a behaviorally relevant context leads to an experience-dependent suppression of visual responses in primary visual cortex (V1). Auditory cortex axons carry a mixture of auditory and retinotopically-matched visual input to V1, and optogenetic stimulation of these axons selectively suppresses V1 neurons responsive to the associated visual stimulus after, but not before, learning. Our results suggest that cross-modal associations can be stored in long-range cortical connections and that with learning these cross-modal connections function to suppress the responses to predictable input.
Interactions between neurons during visual perception and restoring them in blindness
I will discuss the mechanisms that determine whether a weak visual stimulus will reach consciousness or not. If the stimulus is simple, early visual cortex acts as a relay station that sends the information to higher visual areas. If the stimulus arrives at a minimal strength, it will be stored in working memory. However, during more complex visual perceptions, which for example depend on the segregation of a figure from the background, early visual cortex’ role goes beyond a simply relay. It now acts as a cognitive blackboard and conscious perception depends on it. Our results also inspire new approaches to create a visual prosthesis for the blind, by creating a direct interface with the visual cortex. I will discuss how high-channel-number interfaces with the visual cortex might be used to restore a rudimentary form of vision in blind individuals.
Arousal modulates retinal output
Neural responses in the visual system are usually not purely visual but depend on behavioural and internal states such as arousal. This dependence is seen both in primary visual cortex (V1) and in subcortical brain structures receiving direct retinal input. In this talk, I will show that modulation by behavioural state arises as early as in the output of the retina.To measure retinal activity in the awake, intact brain, we imaged the synaptic boutons of retinal axons in the superficial superior colliculus (sSC) of mice. The activity of about half of the boutons depended not only on vision but also on running speed and pupil size, regardless of retinal illumination. Arousal typically reduced the boutons’ visual responses to preferred direction and their selectivity for direction and orientation.Arousal may affect activity in retinal boutons by presynaptic neuromodulation. To test whether the effects of arousal occur already in the retina, we recorded from retinal axons in the optic tract. We found that, in darkness, more than one third of the recorded axons was significantly correlated with running speed. Arousal had similar effects postsynaptically, in sSC neurons, independent of activity in V1, the other main source of visual inputs to colliculus. Optogenetic inactivation of V1 generally decreased activity in collicular neurons but did not diminish the effects of arousal. These results indicate that arousal modulates activity at every stage of the visual system. In the future, we will study the purpose and the underlying mechanisms of behavioural modulation in the early visual system
Human color perception and double-opponent cells in V1 cortex
Time is of the essence: active sensing in natural vision reveals novel mechanisms of perception
n natural vision, active vision refers to the changes in visual input resulting from self-initiated eye movements. In this talk, I will present studies that show that the stimulus-related activity during active vision differs substantially from that occurring during classical flashed-stimuli paradigms. Our results uncover novel and efficient mechanisms that improve visual perception. In a general way, the nervous system appears to engage in sensory modulation mechanisms, precisely timed to self-initiated stimulus changes, thus coordinating neural activity across different cortical areas and serving as a general mechanism for the global coordination of visual perception.