Topic: surround suppression

Recurrent network models of excitatory (E) and inhibitory (I) neurons with supralinear activation functions have successfully explained several cortical computations, including response normalization and surround suppression. Unlike more abstract approaches, such networks allow direct comparisons with experimentally measured neural activities and synaptic strengths. However, the scope of these networks remained limited as their connectivity needed to be designed by hand or fitted by complex machine learning algorithms to yield stable activity and computations. Here we present a method to effic

Sensory neurons in many organisms and brain areas exhibit a form of local gain control that has been described as "divisive normalization'' (Carandini & Heeger, 2011) in which the stimulus-driven activity of each neuron is divided by a factor involving the summed activity of a group of neurons. Redundancy reduction has been proposed as normative explanation of divisive normalization, and previous work has used this principle for learning normalization models (Schwartz & Simoncelli, 2000). Specifically, redundancy in filtered representations of images can be reduced by dividing by an es

Spatial context plays an important role in visual processing and perception. A rich set of surround effects that involve nonlinear interactions between the center and surround have been found in Primary Visual Cortex (V1). A general summary is that surround suppression is prevalent and the amount of suppression depends on the visual similarity between the center and surround. In recent years, convolutional neural networks (CNNs) have successfully modeled neural behavior across the visual cortices. However, there have been limited studies addressing the multitude of contextual effects that have

Surround suppression is ubiquitous in visual processing and perception. Neurophysiology and modeling studies reveal a rich set of spatial context effects in the Primary Visual Cortex, whereby surrounding stimuli nonlinearly modulate (e.g., suppress) the responses of a center target stimulus. Studies suggest that the strength of surround suppression depends on the image statistics of the center and surround pattern. Namely, if the center and surround share similar statistics, the neural responses are more suppressed by the surround. This behavior has been connected to computational benefits suc

Causal inference (CI) has recently been proposed as a universal computational motif in the brain [Shams \& Beierholm 2020]. However, how CI is implemented by neural circuits, and its signatures in terms of single neuron responses, are still unclear. We have investigated this question in the context of complex motion processing. Motion perception deviates from retinal motion [Johansson 1973] a computation that can be understood in terms of hierarchical CI over which moving elements to integrate into coherent 'groups' vs segment into different ones [Gershman et al. 2016, Shivkumar et al. 202

Reliably identifying cell types is crucial for understanding neural systems (1). However, classifying the neurons recorded in large-scale population recordings into well-defined types remains a challenge. While some methods rely on genetic targeting or anatomy for classification, a functional approach used in sensory systems is to analyze neuronal responses to stimuli that elicit distinguishable responses across different types (2, 3, 4). Yet, previous approaches of this nature have been limited by a restricted set of stimuli and scalability issues, thus preventing comprehensive characterizati

Inhibitory neurons (I), akin to their excitatory (E) counterparts, adjust synaptic efficacies in response to neural activity. Contrary to the traditional view of inhibition as a purely global activity modulator, recent studies highlighted the importance of specificity in (E/I) wiring and motif structures. These motifs, encompassing mutual and lateral inhibition, take essential functional roles in tuning of receptive fields, learning of representations, predictive coding, and in the regulation of the degrees of freedom of the network’s dynamics (i.e. the network dimensionality). Despite numero

Perception requires integrating noisy dynamic visual information across the visual field to identify relevant stimuli and guide decisions. While temporal integration has been studied extensively in experiments with highly controlled visual stimuli and reverse-correlation techniques, the nonlinear mechanisms underlying spatial integration are often neglected. More concretely, neurons in the Middle Temporal area (MT) respond heterogeneously and nonlinearly to stimuli inside their receptive fields (RFs) [1-3], and these neurophysiological properties could mediate spatial suppression of motion obs

Pyramidal neurons have prominent apical dendrites that are hypothesised to modulatesomatic output. In the visual cortex, apical dendrites may receive contextual signals via top-down inputs and local inhibition. It is not known, however, what causal effects they play indriving or suppressing somatic activity, and how they shape a neuron’s visual tuning.We recorded activity of pyramidal neurons sparsely expressing GCaMP7s in layer 5 of visualcortex in awake mice before and after pruning their apical dendrites with two-photondendrotomy. Together with the neuron’s receptive field and orientation/d

Perceptual filling-in occurs when a blank patch in the periphery of a texture field perceptually disappears after sustained fixation. This process may be mediated by horizontal spreading of activity in the early visual cortex. To test this proposal, we assessed if the oblique effect and the surround suppression effect, known to modulate activity in early visual cortex, modulates filling-in. In a psychophysics and eye-tracking experiment, 63 participants reported filling-in while attending peripherally to a grey rectangle on a dynamic texture field. We varied the orientation of elements in the

Gamma oscillations are believed to arise from the interaction between excitation and inhibition (E-I) in the neural circuitry. However, the dependency of gamma power on E-I interaction is not well characterized. Increasing the drift-rate of the visual grating, which changes E-I balance, increases the peak frequency of gamma while power initially increases and later attenuates. Importantly, this power versus drift-rate tuning curve is altered for subjects with autism or premenstrual dysphoric disorder. A recent MEG study in humans (Orekhova et al., 2020) examined the combined effect of contrast