Behaviorally relevant decision coding in primary somatosensory cortex neurons

Buetfering, Christina; Zhang, Zihui; Pitsiani, Margarita; Smallridge, John; Boven, Ellen; McElligott, Sacha; Häusser, Michael

doi:10.1038/s41593-022-01151-0

Cited by 27 publications

(19 citation statements)

References 68 publications

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“…In our model, AVA performs a role similar to that of a "decision neuron" with respect to reversals [44]. This is consistent with our previous observation that AVA's calcium activity more closely reflects the animal's decision to reverse, and is less reflective of the strength of the stimulus (e.g.…”

Section: Discussionsupporting

confidence: 89%

Inhibitory motor signals gate mechanosensory processing in C. eleagns

Kumar¹,

Sharma²,

Leifer³

2023

Preprint

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Animals must integrate sensory cues with their current behavioral context to generate a suitable response, but how this integration occurs is poorly understood. Here we report that Caenorhabditis elegans uses inhibitory signals from turning-associated neurons to rapidly modulate mechanosensory processing depending on the animal's behavioral context. Using high-throughput optogenetic perturbations triggered on behavior, we show that turning associated neurons SAA, RIV and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Adding inhibition of SAA, RIV and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Seperately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV and/or SMB gate mechanosensory signals upstream of neuron AVA, and identify putative synapses and receptors where this gating occurs. We conclude that C. elegans relies on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity of motor-related neural activity patterns across the brain, including in sensory processing areas.

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Section: Discussionsupporting

confidence: 89%

Inhibitory motor signals gate mechanosensory processing in C. eleagns

Kumar¹,

Sharma²,

Leifer³

2023

Preprint

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“…We find that a large number of neurons carry significant levels of both stimulus and choice information. This significantly expands on previous work which segregated neurons based on stimulus and choice representation 8,34,35 . Furthermore, the level of stimulus information used to inform choice in expert mice is similar to that recently reported in A1 37 .…”

Section: Discussionsupporting

confidence: 63%

“…This approach should be of great importance in identifying promising targets for manipulation to test the causal relationship between neuronal information and behavioral performance on a related task 29 . While a growing body of work demonstrates that the manipulation of a few dozens of cortical neurons is sufficient to modulate behavior in sensory-guided tasks 8,49–51 , it remains unclear why targeting so few neurons has such an effect. Our work suggests that a common feature of such neurons could be that they carry sensory information used to inform choice, offering concrete future avenues for cracking the neural code.…”

Section: Discussionmentioning

confidence: 99%

“…Neuronal activity in the vibrissal primary somatosensory cortex (vS1) of mice successfully trained on a sensory task reflects not only sensory stimuli but also various types of behavior-related information 1,2 , including information about behavioral choice [3][4][5][6][7][8] . Insight into learning-related changes in cortical neuronal activity is key to understanding how the brain enables flexible behavior.…”

Section: Introductionmentioning

confidence: 99%

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Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning

Panniello

Gillon

Maffulli

et al. 2022

Preprint

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Cortical neurons in primary sensory cortex carry not only sensory but also behavior-related information. However, it remains unclear how these types of information emerge and are integrated with one another over learning and what the relative contribution of activity in individual cells versus neuronal populations is in this process. Current evidence supports two opposing views of learning-related changes: 1) sensory information increases in primary cortex or 2) sensory information remains stable in primary cortex but its readout efficiency in association cortices increases. Here, we investigate these questions in primary sensory cortex during learning of a sensory task. Over the course of weeks, we imaged neuronal activity at different depths within layers 2 and 3 of the mouse vibrissal primary somatosensory cortex (vS1) before, during, and after training on a whisker-based object-localization task. We leveraged information theoretical analysis to quantify stimulus and behavior-related information in vS1 and estimate how much neural activity encoding sensory information is used to inform perceptual choices as sensory learning progresses. We also quantified the extent to which these types of information are supported by an individual neuron or population code. We found that, while sensory information rises progressively from the start of training, choice information is only present in the final stages of learning and is increasingly supported by a population code. Moreover, we demonstrate that not only the increase in available information, but also a more efficient readout of such information in primary sensory cortex mediate sensory learning. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

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“…Connectivity matrices implementing lateral inhibition predict that the strength of synaptic connections is proportional to the similarity in stimulus selectivity of the pre- and the postsynaptic neuron,

. These synaptic currents decorrelate the activity of E neurons with similar stimulus selectivity, which has been empirically observed in primary sensory cortices [55] , [56] . Moreover, such connections can be learned with biologically plausible local learning rules [57] .…”

Section: Computational Methods For Encoding Of Informationsupporting

confidence: 62%