Near-optimal integration of orientation information across saccades.

We perceive a stable environment despite the fact that visual information is essentially acquired in a sequence of snapshots separated by saccadic eye movements. The resolution of these snapshots varies-high in the fovea and lower in the periphery-and thus the formation of a stable percept presumably relies on the fusion of information acquired at different resolutions. To test if, and to what extent, foveal and peripheral information are integrated, we examined human orientation-discrimination performance across saccadic eye movements. We found that humans perform best when an oriented target is visible both before (peripherally) and after a saccade (foveally), suggesting that humans integrate the two views. Integration relied on eye movements, as we found no evidence of integration when the target was artificially moved during stationary viewing. Perturbation analysis revealed that humans combine the two views using a weighted sum, with weights assigned based on the relative precision of foveal and peripheral representations, as predicted by ideal observer models. However, our subjects displayed a systematic overweighting of the fovea, relative to the ideal observer, indicating that human integration across saccades is slightly suboptimal.

A thesaurus for a neural population code.

Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns.

Faithful Representation of Tactile Intensity under Different Contexts Emerges from the Distinct Adaptive Properties of the First Somatosensory Relay Stations.

Adaptation allows neurons to respond to a wide range of stimulus intensities. However, it also leads to ambiguity as the representation of the external world depends on the context. We recorded neurons from Wistar rats' brainstem nuclei belonging to two major somatosensory pathways (lemniscal and paralemniscal) and explored the way in which they encode noisy stimuli under different contexts. We found that although their unadapted intensity-response curves are very similar, the adapted curves of the two pathways are distinctively different as they are optimized for encoding different intensity ranges. Lemniscal neurons most faithfully encoded stimuli when the background intensity was high, whereas paralemniscal cells best encoded stimuli under low intensity context. Intracellular recordings indicate that these differences emerge already at the synaptic level. We suggest that the two pathways synergistically improve the ability of this system to encode a wide range of intensities during natural stimulation, potentially reducing the inherent ambiguity of adaptive coding.

Sparse low-order interaction network underlies a highly correlated and learnable neural population code.

Information is carried in the brain by the joint activity patterns of large groups of neurons. Understanding the structure and function of population neural codes is challenging because of the exponential number of possible activity patterns and dependencies among neurons. We report here that for groups of ~100 retinal neurons responding to natural stimuli, pairwise-based models, which were highly accurate for small networks, are no longer sufficient. We show that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons. Additionally, we show that the interaction network is organized in a hierarchical and modular manner, which hints at scalability. Our results suggest that learnability may be a key feature of the neural code.

The architecture of functional interaction networks in the retina.

Sensory information is represented in the brain by the joint activity of large groups of neurons. Recent studies have shown that, although the number of possible activity patterns and underlying interactions is exponentially large, pairwise-based models give a surprisingly accurate description of neural population activity patterns. We explored the architecture of maximum entropy models of the functional interaction networks underlying the response of large populations of retinal ganglion cells, in adult tiger salamander retina, responding to natural and artificial stimuli. We found that we can further simplify these pairwise models by neglecting weak interaction terms or by relying on a small set of interaction strengths. Comparing network interactions under different visual stimuli, we show the existence of local network motifs in the interaction map of the retina. Our results demonstrate that the underlying interaction map of the retina is sparse and dominated by local overlapping interaction modules.

Intensity-dependent adaptation of cortical and thalamic neurons is controlled by brainstem circuits of the sensory pathway.

Current views of sensory adaptation in the rat somatosensory system suggest that it results mainly from short-term synaptic depression. Experimental and theoretical studies predict that increasing the intensity of sensory stimulation, followed by an increase in firing probability at early sensory stages, is expected to attenuate the response at later stages disproportionately more than weaker stimuli, due to greater depletion of synaptic resources and the relatively slow recovery process. This may lead to coding ambiguity of stimulus intensity during adaptation. In contrast, we found that increasing the intensity of repetitive whisker stimulation entails less adaptation in cortical neurons. In a series of recordings, from the trigeminal ganglion to the thalamus, we pinpointed the source of the unexpected pattern of adaptation to the brainstem trigeminal complex. We suggest that low-level sensory processing counterbalances later effects of short-term synaptic depression by increasing the throughput of high-intensity sensory inputs.

The V1 population gains normalization.

In this issue of Neuron, Busse et al. describe the population response to superimposed visual stimuli while Sit et al. examine the spatiotemporal evolution of cortical activation in response to small visual stimuli. Surprisingly, these two studies of V1 report that a single gain control model accounts for their results.

Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons.

Sustained stimulation of sensory organs results in adaptation of the neuronal response along the sensory pathway. Whether or not cortical adaptation affects equally excitation and inhibition is poorly understood. We examined this question using patch recordings of neurons in the barrel cortex of anesthetized rats while repetitively stimulating the principal whisker. We found that inhibition adapts more than excitation, causing the balance between them to shift toward excitation. A comparison of the latency of thalamic firing and evoked excitation and inhibition in the cortex strongly suggests that adaptation of inhibition results mostly from depression of inhibitory synapses rather than adaptation in the firing of inhibitory cells. The differential adaptation of the evoked conductances that shifts the balance toward excitation may act as a gain mechanism which enhances the subthreshold response during sustained stimulation, despite a large reduction in excitation.