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Understanding the circuit mechanisms of the visual code for natural scenes is a central goal of sensory neuroscience. We show that a three-layer network model predicts retinal natural scene responses with an accuracy nearing experimental limits. The model's internal structure is interpretable, as interneurons recorded separately and not modeled directly are highly correlated with model interneurons. Models fitted only to natural scenes reproduce a diverse set of phenomena related to motion encoding, adaptation, and predictive coding, establishing their ethological relevance to natural visual computation. A new approach decomposes the computations of model ganglion cells into the contributions of model interneurons, allowing automatic generation of new hypotheses for how interneurons with different spatiotemporal responses are combined to generate retinal computations, including predictive phenomena currently lacking an explanation. Our results demonstrate a unified and general approach to study the circuit mechanisms of ethological retinal computations under natural visual scenes.

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The ability for the brain to discriminate among visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability have been limited to either low-dimensional artificial stimuli or pure theoretical considerations without a realistic encoding model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we computed the Fisher information metric over stimuli to study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially across stimuli, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. By examining responses generated by the most discriminable stimuli we further found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes, retinal noise correlations are information-limiting rather than increasing information transmission as has been previously speculated. We additionally observed that sensitivity saturates less in the population than for single cells and that as a function of firing rate, Fisher information varies less than sensitivity. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximization.

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Cortical function relies on the balanced activation of excitatory and inhibitory neurons. However, little is known about the organization and dynamics of shaft excitatory synapses onto cortical inhibitory interneurons. Here, we use the excitatory postsynaptic marker PSD-95, fluorescently labeled at endogenous levels, as a proxy for excitatory synapses onto layer 2/3 pyramidal neurons and parvalbumin-positive (PV+) interneurons in the barrel cortex of adult mice. Longitudinal in vivo imaging under baseline conditions reveals that, although synaptic weights in both neuronal types are log-normally distributed, synapses onto PV+ neurons are less heterogeneous and more stable. Markov model analyses suggest that the synaptic weight distribution is set intrinsically by ongoing cell-type-specific dynamics, and substantial changes are due to accumulated gradual changes. Synaptic weight dynamics are multiplicative, i.e., changes scale with weights, although PV+ synapses also exhibit an additive component. These results reveal that cell-type-specific processes govern cortical synaptic strengths and dynamics.

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Neuromodulation imposes powerful control over brain function, and cAMP-dependent protein kinase (PKA) is a central downstream mediator of multiple neuromodulators. Although genetically encoded PKA sensors have been developed, single-cell imaging of PKA activity in living mice has not been established. Here, we used two-photon fluorescence lifetime imaging microscopy (2pFLIM) to visualize genetically encoded PKA sensors in response to the neuromodulators norepinephrine and dopamine. We screened available PKA sensors for 2pFLIM and further developed a variant (named tAKARα) with increased sensitivity and a broadened dynamic range. This sensor allowed detection of PKA activation by norepinephrine at physiologically relevant concentrations and kinetics, and by optogenetically released dopamine. In vivo longitudinal 2pFLIM imaging of tAKARα tracked bidirectional PKA activities in individual neurons in awake mice and revealed neuromodulatory PKA events that were associated with wakefulness, pharmacological manipulation, and locomotion. This new sensor combined with 2pFLIM will enable interrogation of neuromodulation-induced PKA signaling in awake animals. VIDEO ABSTRACT.

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Stoichiometric labeling of endogenous synaptic proteins for high-contrast live-cell imaging in brain tissue remains challenging. Here, we describe a conditional mouse genetic strategy termed endogenous labeling via exon duplication (ENABLED), which can be used to fluorescently label endogenous proteins with near ideal properties in all neurons, a sparse subset of neurons, or specific neuronal subtypes. We used this method to label the postsynaptic density protein PSD-95 with mVenus without overexpression side effects. We demonstrated that mVenus-tagged PSD-95 is functionally equivalent to wild-type PSD-95 and that PSD-95 is present in nearly all dendritic spines in CA1 neurons. Within spines, while PSD-95 exhibited low mobility under basal conditions, its levels could be regulated by chronic changes in neuronal activity. Notably, labeled PSD-95 also allowed us to visualize and unambiguously examine otherwise-unidentifiable excitatory shaft synapses in aspiny neurons, such as parvalbumin-positive interneurons and dopaminergic neurons. Our results demonstrate that the ENABLED strategy provides a valuable new approach to study the dynamics of endogenous synaptic proteins in vivo.

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