Effects of V1 surround modulation tuning on visual saliency and the tilt illusion

Contextual modulation emerges by integrating feedforward and feedback processing in mouse visual cortex

Sensory systems use context to infer meaning. Accordingly, context profoundly influences neural responses to sensory stimuli. However, a cohesive understanding of the circuit mechanisms governing contextual effects across different stimulus conditions is still lacking. Here we present a unified circuit model of mouse visual cortex that accounts for the main standard forms of contextual modulation. This data-driven and biologically realistic circuit, including three primary inhibitory cell types, sheds light on how bottom-up, top-down, and recurrent inputs are integrated across retinotopic space to generate contextual effects in layer 2/3. We establish causal relationships between neural responses, geometrical features of the inputs, and the connectivity patterns. The model not only reveals how a single canonical cortical circuit differently modulates sensory response depending on context but also generates multiple testable predictions, offering insights that apply to broader neural circuitry.

Visual surround suppression at the neural and perceptual levels

Surround suppression was initially identified as a phenomenon at the neural level in which stimuli outside the neuron's receptive field alone cannot activate responses but can modulate neural responses to stimuli covered inside the receptive field. Subsequent studies showed that surround suppression is not only a critical property of neurons across species and brain areas but also has been found in visual perceptions. More importantly, surround suppression varies across individuals and shows significant differences between normal controls and patients with certain mental disorders. Here, we combined results from related literature and summarized the findings derived from physiological and psychophysical evidence. We first outline the basic properties of surround suppression in the visual system and perceptions. Then, we mainly summarize the differences in perceptual surround suppression among different human subjects. Our review suggests that there is no consensus regarding whether the strength of perceptual surround suppression could be used as an effective index to distinguish particular populations. Then, we summarized the similar mechanisms for surround suppression and cognitive impairments to further explore the potential clinical applications of surround suppression. A clearer understanding of the mechanisms of surround suppression in neural responses and perceptions is necessary for facilitating its clinical applications.

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.