Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

Sanzeni, Alessandro; Palmigiano, Agostina; Nguyen, Tuan H.; Luo, Junxiang; Nassi, Jonathan J.; Reynolds, John H.; Histed, Mark H.; Miller, Kenneth D.; Brunel, Nicolas

doi:10.1016/j.neuron.2023.09.018

Cited by 4 publications

(2 citation statements)

References 80 publications

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“…These might correspond to a previously reported set of neurons in mouse dLGN, amounting to 30-40%, with poor or unclear visual feature selectivity (63,56). Given that this subpopulation also contained an over-representation of low-firing neurons, their relatively stronger modulation (see also (58)) might be indicative of a tight inhibitory / excitatory coupling in the recorded network (64). Such tight coupling in the thalamo-cortico-thalamic network might be mechanistically achieved through joint modulations of dLGN relay cells, and inhibitory neurons in dLGN and the thalamic reticular nucleus by CT feedback and neuromodulation (1,4,7,9).…”

Section: /31supporting

confidence: 81%

The combination of stimulus-driven and modulatory inputs in visual thalamus depend on visual responsiveness and stimulus type

Schmors,

Kotkat,

Bauer

et al. 2023

Preprint

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In the dorsolateral geniculate nucleus (dLGN) of the thalamus, retinal signals are combined with non-retinal inputs such as corticothalamic (CT) feedback and behavioural state. How these shape dLGN activity remains an open question. We recorded extracellular responses in dLGN of awake mice to a movie stimulus, while photosuppressing CT feedback, tracking locomotion and pupil size. To assess the relative impact of retinal and non-retinal inputs, we fit single neuron responses using generalized linear models. While including CT feedback and behavioural state as predictors significantly improved the model's overall performance, the improvement was especially pronounced for a sub-population of poorly visually responsive neurons. In addition, the impact of CT feedback was faster and more prevalent in the absence of a patterned visual stimulus. Finally, for CT feedback-sensitive dLGN neurons, visual stimuli could be discriminated better when CT feedback was suppressed. Together, these results show that effects of non-retinal inputs in dLGN depend on visual responsiveness and stimulus context.

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Section: /31supporting

confidence: 81%

The combination of stimulus-driven and modulatory inputs in visual thalamus depend on visual responsiveness and stimulus type

Schmors,

Kotkat,

Bauer

et al. 2023

Preprint

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show abstract

“…There are multiple levels at which homeostatic results can be observed. Firstly, there is population homeostasis, in which the stimulus-average firing rate of an entire population of neurons remains constant, without individual neurons necessarily holding their rates constant (Slomowitz et al, 2015; Sanzeni et al, 2023). Secondly, there is what we term cluster homeostasis .…”

Section: Introductionmentioning

confidence: 99%

Efficient coding explains neural response homeostasis and stimulus-specific adaptation

Young,

Ahmadian

2023

Preprint

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Neurons are typically sensitive to a small fraction of stimulus space. If the environment changes, making certain stimuli more prevalent, neurons sensitive to those stimuli would respond more often and therefore, if the stimulus-response mapping remains fixed, have a higher average firing rate. However, sufficiently prolonged exposure to the new environment typically causes such neurons to adapt by responding less vigorously. If adaptation consistently returns the average firing rate of neurons, or populations of similarly tuned neurons, to its value prior to environmental shift, it is termed firing rate homeostasis. In sensory cortex adaptation is typically also stimulus specific, with neurons maintaining or even increasing their responsiveness to stimuli far from over-represented ones. Here, we present a normative explanation of firing rate homeostasis grounded in the efficient coding principle. Unlike previous theories based on efficient coding, we formulate the problem in a computation-agnostic manner, enabling our framework to apply far from the sensory periphery. We show that homeostasis can provide an optimal solution to a trade-off between coding fidelity and the metabolic cost of neural firing. We provide quantitative conditions necessary for the optimality of firing rate homeostasis, and predict how adaptation should deviate from homeostasis when these conditions are violated. Based on biological estimates of relevant parameters, we show that these conditions do hold in areas of cortex where homeostatic adaptation has been observed. Finally, we apply our framework to distributed distributional codes, a specific computational theory of neural representations serving Bayesian inference. We show that the resultant coding scheme can be accomplished by divisive normalisation with adaptive weights. We further demonstrate how homeostatic coding, coupled with such Bayesian neural representations, explains stimulus-specific adaptation, as observed,e.g., in the primary visual cortex.

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Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

O’Rawe,

Zhou,

et al. 2023

Neuron

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Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

Cited by 4 publications

References 80 publications

The combination of stimulus-driven and modulatory inputs in visual thalamus depend on visual responsiveness and stimulus type

The combination of stimulus-driven and modulatory inputs in visual thalamus depend on visual responsiveness and stimulus type

Efficient coding explains neural response homeostasis and stimulus-specific adaptation

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

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