Biology subjects

Glickfeld, L.

Publications and source records attributed to Glickfeld, L..

4 recordsLinked to original sources

bioRxiv2020

Mouse higher visual areas provide both distributed and discrete contributions to visually guided behaviors

Cortical parallel processing streams segregate many diverse features of a sensory scene. However, some features are distributed across streams, begging the question of whether and how such distributed representations contribute to perception. We determined the necessity of primary visual cortex (V1) and three key higher visual areas (LM, AL and PM) for perception of orientation and contrast, two features that are robustly encoded across all four areas. Suppressing V1, LM or AL decreased sensitivity for both orientation discrimination and contrast detection, consistent with a role for these areas in sensory perception. In comparison, suppressing PM selectively increased false alarm rates during contrast detection, without any effect on orientation discrimination. This effect was not retinotopically-specific, suggesting a distinct role for PM in the regulation of noise during decision-making. Thus, we find that distributed representations in the visual system can nonetheless support specialized perceptual roles for higher visual cortical areas.

neuroscience↗

bioRxiv2019

Unique spatial integration in mouse primary visual cortex and higher visual areas

Neurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the areas unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

neuroscience↗

bioRxiv2018

Contribution of sensory encoding to measured bias

Perceptual decision-making is a complex process that involves sensory integration followed by application of a cognitive threshold. Signal detection theory (SDT) provides a mathematical framework for attributing the underlying neurobiological processes to these distinct phases of perceptual decision-making. In particular, SDT reveals the sensitivity (d) of the neuronal response distributions and the bias (c) of the decision criterion, which are commonly thought to reflect sensory and cognitive processes, respectively. However, neuronal representations of bias have been observed in sensory areas, suggesting that some changes in bias are due to effects on sensory encoding. To directly test whether sensory encoding can influence bias, we optogenetically manipulated neuronal excitability in primary visual cortex (V1) during a detection task. Increasing excitability in V1 significantly decreased behavioral bias, while decreasing excitability had the opposite effect. To determine whether this change in bias is consistent with the effects on sensory encoding, we made extracellular recordings from V1 neurons in passively viewing mice. Indeed, we found that optogenetic manipulation of excitability shifted the neuronal bias in the same direction as the behavioral bias, despite using a fixed artificial decision criterion to predict hit and false alarm rates from the neuronal firing rates. To test the generality these effects, we also manipulated the quality of V1 encoding by changing stimulus contrast or inter-stimulus interval. These stimulus manipulations also resulted in consistent changes in bias measured both behaviorally and neuronally. Thus, changes in sensory encoding are sufficient to drive changes in bias measured using SDT.

neuroscience↗

bioRxiv2018

Neuronal adaptation reveals a suboptimal decoding of orientation tuned populations in the mouse visual cortex

Sensory information is encoded by populations of cortical neurons. Yet, it is unknown how this information is used for even simple perceptual choices such as discriminating orientation. To determine the computation underlying this perceptual choice, we took advantage of the robust adaptation in the mouse visual system. We find that adaptation increases animals thresholds for orientation discrimination. This was unexpected since optimal computations that take advantage of all available sensory information predict that the shift in tuning and increase in signal-to-noise ratio in the adapted condition should improve discrimination. Instead, we find that the effects of adaptation on behavior can be explained by the appropriate reliance of the perceptual choice circuits on target preferring neurons, but the failure to discount neurons that prefer the distractor. This suggests that to solve this task the circuit has adopted a suboptimal strategy that discards important task-related information to implement a feed-forward visual computation.

neuroscience↗