Neuronal Responses Research Articles

The high-light-sensitivity mechanism and optogenetic properties of the bacteriorhodopsin-like channelrhodopsin GtCCR4

Channelrhodopsins are microbial light-gated ion channels that can control the firing of neurons in response to light. Among several cation channelrhodopsins identified in Guillardia theta (GtCCRs), GtCCR4 has higher light sensitivity than typical channelrhodopsins. Furthermore, GtCCR4 shows superior properties as an optogenetic tool, such as minimal desensitization. Our structural analyses of GtCCR2 and GtCCR4 revealed that GtCCR4 has an outwardly bent transmembrane helix, resembling the conformation of activated G-protein-coupled receptors. Spectroscopic and electrophysiological comparisons suggested that this helix bend in GtCCR4 omits channel recovery time and contributes to high light sensitivity. An electrophysiological comparison of GtCCR4 and the well-characterized optogenetic tool ChRmine demonstrated that GtCCR4 has superior current continuity and action-potential spike generation with less invasiveness in neurons. We also identified highly active mutants of GtCCR4. These results shed light on the diverse structures and dynamics of microbial rhodopsins and demonstrate the strong optogenetic potential of GtCCR4.

Nrf2 Deficiency Exacerbates Parkinson's Disease by Aggravating NLRP3 Inflammasome Activation in MPTP-Induced Mouse Models and LPS-Induced BV2 Cells.

Parkinson's disease (PD) is a movement disorder characterized by the progressive loss of dopamine neurons. Microglia-mediated neuroinflammation drives disease progression and becomes a critical factor in neuronal degeneration. Recent studies have found that nuclear factor-erythroid 2-related-2 (Nrf2) expression levels are reduced during aging and neurodegenerative diseases, but its regulatory mechanism on microglia-induced neuroinflammation has not been fully elucidated. In vivo, we used the intraperitoneal injection of the neurotoxic drug neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to establish an animal model of PD and, at the same time, administered Nrf2 inhibitors ML385 and dimethyl fumarate to regulate Nrf2 protein levels. In vitro, we used si-RNA to knock out the Nrf2 gene to intervene in BV2 cells and used lipopolysaccharide (LPS) to stimulate and induce the cell model. The study found that inhibition of Nrf2 expression aggravated the motor defects of PD mice, accompanied by a significant loss of dopaminergic neurons in the substantia nigra and striatum of the brain. In addition, after inhibition of Nrf2, the malondialdehyde (MDA) level in the substantia nigra of the midbrain of mice increased, and the levels of superoxide dismutase (SOD) and heme oxygenase-1 (HO-1) decreased, accompanied by the proliferation of microglia and astrocytes. In addition, the activation of the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome, the assembly of apoptosis-associated speck-like protein containing a CARD (ASC) protein in microglia, and the release of downstream inflammatory factors caspase-1 and interleukin (IL)-1β, were aggravated. At the cellular level, it was found that knocking out the expression of Nrf2 would aggravate the activation of NLRP3 inflammasomes and the assembly of ASC in LPS-induced BV2 cells. Inhibited Nrf2 activity can reduce the downstream antioxidant enzyme HO-1 and antioxidant levels, induce NLRP3 inflammasome activation and ASC protein assembly in microglia, and ultimately aggravate PD inflammatory response and dopamine neuron degeneration.

Dietary Natural Flavonoids: Intervention for MAO-B Against Parkinson's Disease.

Parkinson's disease (PD) stands as the second most common neurological disorder after Alzheimer's disease, primarily affecting the elderly population and significantly compromising their quality of life. The precise etiology of PD remains elusive, but recent research has shed light on potential factors, including the formation of α-synuclein aggregates, oxidative stress, neurotransmitter imbalances, and dopaminergic neurodegeneration in the substantia nigra pars compacta (SNpc) region of the brain, culminating in motor symptoms such as bradykinesia, akinesia, tremors, and rigidity. Monoamine oxidase (MAO) is an essential enzyme, comprising two isoforms, MAO-A and MAO-B, responsible for the oxidation of monoamines such as dopamine. Increased MAO-B activity is responsible for decreased dopamine levels in the SNpc region of mid brain which is remarkably associated with the pathogenesis of PD-like manifestations. Inhibitors of MAO-B enhance striatal neuronal responses to dopamine, making them valuable in treating PD, which involves dopamine deficiency. Clinically approved MAO-B inhibitors such as selegiline, L-deprenyl, pargyline, and rasagiline are employed in the management of neurodegenerative conditions associated with PD. Current therapeutic interventions including MAO-B inhibitors for PD predominantly aim to alleviate these motor symptoms but often come with a host of side effects that can be particularly challenging for the patients. While effective, they have limitations, prompting a search for alternative treatments, there is a growing interest in exploring natural products notably flavonoids as potential sources of novel MAO-B inhibitors. In line with that, the present review focuses on natural flavonoids of plant origin that hold promise as potential candidates for the development of novel MAO-B inhibitors. The discussion encompasses both invitro and invivo studies, shedding light on their potential therapeutic applications. Furthermore, this review underscores the significance of exploring natural products as valuable reservoirs of MAO-B inhibitors, offering new avenues for drug development and addressing the pressing need for improved treatments in PD-like pathological conditions. The authors of this review majorly explore the neuroprotective potential of natural flavonoids exhibiting notable MAO-B inhibitory activity and additionally multi-targeted approaches in the treatment of PD with clinical evidence and challenges faced in current therapeutic approaches.

Localized and Long-Lasting Adaptation in Dragonfly Target-Detecting Neurons.

Some visual neurons in the dragonfly (Hemicordulia tau) optic lobe respond to small, moving targets, likely underlying their fast pursuit of prey and conspecifics. In response to repetitive targets presented at short intervals, the spiking activity of these "small target motion detector" (STMD) neurons diminishes over time. Previous experiments limited this adaptation by including intertrial rest periods of varying durations. However, the characteristics of this effect have never been quantified. Here, using extracellular recording techniques lasting for several hours, we quantified both the spatial and temporal properties of STMD adaptation. We found that the time course of adaptation was variable across STMD units. In any one STMD, a repeated series led to more rapid adaptation, a minor accumulative effect more akin to habituation. Following an adapting stimulus, responses recovered quickly, though the rate of recovery decreased nonlinearly over time. We found that the region of adaptation is highly localized, with targets displaced by ∼2.5° eliciting a naive response. Higher frequencies of target stimulation converged to lower levels of sustained response activity. We determined that adaptation itself is a target-tuned property, not elicited by moving bars or luminance flicker. As STMD adaptation is a localized phenomenon, dependent on recent history, it is likely to play an important role in closed-loop behavior where a target is foveated in a localized region for extended periods of the pursuit duration.

Neuronal firing rate diversity lowers the dimension of population covariability.

Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the activity of neurons. These two features define the quality of neuronal representation. We link these two aspects of population response using a recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Using our relation we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.

Strong Solutions of the Neuronal Responses System

We explore a connected reaction-diffusion system involving neural responses within limited convex open domains . Utilizing the Classical Faedo-Galerkin technique and employing compactness arguments, we establish the existence, uniqueness, and continuous dependence on initial data for strong solutions.

Foundation model of neural activity predicts response to new stimulus types and anatomy.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

Calmodulin Triggers Activity-Dependent rRNA Biogenesis via Interaction with DDX21.

Protein synthesis in response to neuronal activity, known as activity-dependent translation, is critical for synaptic plasticity and memory formation. However, the signaling cascades that couple neuronal activity to the translational events remain elusive. In this study, we identified the role of calmodulin (CaM), a conserved Ca2+-binding protein, in ribosomal RNA (rRNA) biogenesis in neurons. We found the CaM-regulated rRNA synthesis is Ca2+-dependent and necessary for nascent protein synthesis and axon growth in hippocampal neurons. Mechanistically, CaM interacts with nucleolar DEAD (Asp-Glu-Ala-Asp) box RNA helicase (DDX21) in a Ca2+-dependent manner to regulate nascent rRNA transcription within nucleoli. We further found CaM alters the conformation of DDX21 to liberate the DDX21-sequestered RPA194, the catalytic subunit of RNA polymerase I, to facilitate transcription of ribosomal DNA. Using high-throughput screening, we identified the small molecules batefenterol and indacaterol that attenuate the CaM-DDX21 interaction and suppress nascent rRNA synthesis and axon growth in hippocampal neurons. These results unveiled the previously unrecognized role of CaM as a messenger to link the activity-induced Ca2+ influx to the nucleolar events essential for protein synthesis. We thus identified the ability of CaM to transmit information to the nucleoli of neurons in response to stimulation.

Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys.

The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and therefore unlikely to directly reflect the formation of perceptual decisions.

The influence of form on motion signal processing in the ventral intraparietal area of macaque monkeys

The visual system relies on both motion and form signals to perceive the direction of self-motion, yet the coordination mechanisms between these two elements in this process remain elusive. In the current study, we employed heading perception as a model to delve into the interaction characteristics between form and motion signals. We recorded the responses of neurons in the ventral intraparietal area (VIP), an area with strong heading selectivity, to motion-only, form-only, and combined stimuli of simulated self-motion. Intriguingly, VIP neurons responded to form-only cues defined by Glass patterns, although they exhibited no tuning selectivity. In combined condition, introducing a small offset between form and motion cues significantly enhanced neuronal sensitivity to motion cues. However, with a larger offset, the enhancement effect on sensitivity became comparatively smaller. Moreover, we observed that the influence of form cues on neuronal response to motion cues is more pronounced in the later stage (1–2 s) of stimulation, with a relatively smaller effect in the early stage (0–1 s). This suggests a dynamic interaction between motion and form cues over time for heading perception. In summary, our study uncovered that in area VIP, form information plays a role in constructing accurate self-motion perception. This adds valuable insights into the complex dynamics of how the brain integrates motion and form cues for the perception of one's own movements.

Bidirectional generative adversarial representation learning for natural stimulus synthesis.

Thousands of species use vocal signals to communicate with one another. Vocalizations carry rich information, yet characterizing and analyzing these complex, high-dimensional signals is difficult and prone to human bias. Moreover, animal vocalizations are ethologically relevant stimuli whose representation by auditory neurons is an important subject of research in sensory neuroscience. A method that can efficiently generate naturalistic vocalization waveforms would offer an unlimited supply of stimuli with which to probe neuronal computations. Although unsupervised learning methods allow for the projection of vocalizations into low-dimensional latent spaces learned from the waveforms themselves, and generative modeling allows for the synthesis of novel vocalizations for use in downstream tasks, we are not aware of any model that combines these tasks to synthesize naturalistic vocalizations in the waveform domain for stimulus playback. In this paper, we demonstrate BiWaveGAN: a bidirectional generative adversarial network (GAN) capable of learning a latent representation of ultrasonic vocalizations (USVs) from mice. We show that BiWaveGAN can be used to generate, and interpolate between, realistic vocalization waveforms. We then use these synthesized stimuli along with natural USVs to probe the sensory input space of mouse auditory cortical neurons. We show that stimuli generated from our method evoke neuronal responses as effectively as real vocalizations, and produce receptive fields with the same predictive power. BiWaveGAN is not restricted to mouse USVs but can be used to synthesize naturalistic vocalizations of any animal species and interpolate between vocalizations of the same or different species, which could be useful for probing categorical boundaries in representations of ethologically relevant auditory signals.NEW & NOTEWORTHY A new type of artificial neural network is presented that can be used to generate animal vocalization waveforms and interpolate between them to create new vocalizations. We find that our synthetic naturalistic stimuli drive auditory cortical neurons in the mouse equally well and produce receptive field features with the same predictive power as those obtained with natural mouse vocalizations, confirming the quality of the stimuli produced by the neural network.

Physiological properties of auditory neurons responding to omission deviants in the anesthetized rat

SummaryThe detection of novel, low probability events in the environment is critical for survival. To perform this vital task, our brain is continuously building and updating a model of the outside world; an extensively studied phenomenon commonly referred to as predictive coding. Predictive coding posits that the brain is continuously extracting regularities from the environment to generate predictions. These predictions are then used to supress neuronal responses to redundant information, filtering those inputs, which then automatically enhances the remaining, unexpected inputs.We have recently described the ability of auditory neurons to generate predictions about expected sensory inputs by detecting their absence in an oddball paradigm using omitted tones as deviants. Here, we studied the responses of individual neurons to omitted tones by presenting individual sequences of repetitive pure tones, using both random and periodic omissions, presented at both fast and slow rates in the inferior colliculus and auditory cortex neurons of anesthetized rats. Our goal was to determine whether feature-specific dependence of these predictions exists. Results showed that omitted tones could be detected at both high (8 Hz) and slow repetition rates (2 Hz), with detection being more robust at the non-lemniscal auditory pathway.

Selective optogenetic inhibition of Gαq or Gαi signaling by minimal RGS domains disrupts circuit functionality and circuit formation

Optogenetic techniques provide genetically targeted, spatially and temporally precise approaches to correlate cellular activities and physiological outcomes. In the nervous system, G protein-coupled receptors (GPCRs) have essential neuromodulatory functions through binding extracellular ligands to induce intracellular signaling cascades. In this work, we develop and validate an optogenetic tool that disrupts Gαq signaling through membrane recruitment of a minimal regulator of G protein signaling (RGS) domain. This approach, Photo-induced Gα Modulator-Inhibition of Gαq (PiGM-Iq), exhibited potent and selective inhibition of Gαq signaling. Using PiGM-Iq we alter the behavior of Caenorhabditis elegans and Drosophila with outcomes consistent with GPCR-Gαq disruption. PiGM-Iq changes axon guidance in cultured dorsal root ganglia neurons in response to serotonin. PiGM-Iq activation leads to developmental deficits in zebrafish embryos and larvae resulting in altered neuronal wiring and behavior. Furthermore, by altering the minimal RGS domain, we show that this approach is amenable to Gαi signaling. Our unique and robust optogenetic Gα inhibiting approaches complement existing neurobiological tools and can be used to investigate the functional effects neuromodulators that signal through GPCR and trimeric G proteins.

Nonlinearities and timescales in neural models of temporal interference stimulation.

In temporal interference (TI) stimulation, neuronal cells react to two interfering sinusoidal electric fields with a slightly different frequency ( , in the range of about 1-4 kHz, in the range of about 1-100 Hz). It has been previously observed that for the same input intensity, the neurons do not react to a purely sinusoidal signal at or . This study seeks a better understanding of the largely unknown mechanisms underlying TI neuromodulation. To this end, single-compartment models are used to simulate computationally the response of neurons to the sinusoidal and TI waveform. This study compares five different neuron models: Hodgkin-Huxley (HH), Frankenhaeuser-Huxley (FH), along with leaky, exponential, and adaptive-exponential integrate-and-fire (IF). It was found that IF models do not entirely reflect the experimental behavior while the HH and FH model did qualitatively replicate the observed neural responses. Changing the time constants and steady state values of the ion gates in the FH model alters the response to both the sinusoidal and TI signal, possibly reducing the firing threshold of the sinusoidal input below that of the TI input. The results show that in the modified (simplified) model, TI stimulation is not qualitatively impacted by nonlinearities in the current-voltage relation. In contrast, ion channels have a significant impact on the neuronal response. This paper offers insights into neuronal biophysics and computational models of TI stimulation.

Dense and persistent odor representations in the olfactory bulb of awake mice.

Recording and analysis of neural activity is often biased toward detecting sparse subsets of highly active neurons, masking important signals carried in low magnitude and variable responses. To investigate the contribution of seemingly noisy activity to odor encoding, we used mesoscale calcium imaging from mice of both sexes to record odor responses from the dorsal surface of bilateral olfactory bulbs (OBs). The outer layer of the mouse OB is comprised of dendrites organized into discrete "glomeruli", which are defined by odor receptor-specific sensory neuron input. We extracted activity from a large population of glomeruli and used logistic regression to classify odors from individual trials with high accuracy. We then used add-in and drop-out analyses to determine subsets of glomeruli necessary and sufficient for odor classification. Classifiers successfully predicted odor identity even after excluding sparse, highly active glomeruli, indicating that odor information is redundantly represented across a large population of glomeruli. Additionally, we found that Random Forest feature selection informed by Gini Inequality (RFGI) reliably ranked glomeruli by their contribution to overall odor classification. RFGI provided a measure of "feature importance" for each glomerulus that correlated with intuitive features like response magnitude. Finally, in agreement with previous work, we found that odor information persists in glomerular activity after odor offset. Together, our findings support a model of olfactory bulb odor coding where sparse activity is sufficient for odor identification, but information is widely, redundantly available across a large population of glomeruli, with each glomerulus representing information about more than one odor.Significance statement This study leverages meso-scale imaging and machine learning to investigate how odor information is first represented in the brain. Typically, recordings of neuronal activity focus on active individual cells, potentially overlooking broader variations in neuronal responses across populations. Our results demonstrate that a considerable amount of olfactory information is redundantly distributed across a large proportion of olfactory bulb glomeruli. Even after excluding a majority of glomeruli, odor identification remained possible. These findings indicate that, although a few glomeruli are sufficient for odor recognition, an abundance of additional information is represented across a broad population. Understanding how the brain manages redundant olfactory information will shed light on its adaptive mechanisms for navigating diverse real-world circumstances and responding to fluctuating internal states.

Transitions in dynamical regime and neural mode underlie perceptual decision-making.

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes, the first dominated by sensory inputs, and the second dominated by the autonomous dynamics, with flow in a direction (i.e., "neural mode") largely orthogonal to that in the first regime. We propose that the second regime corresponds to decision commitment. We developed a simplified model that approximates the coupled transition in dynamics and neural mode and allows precise inference, from each trial's neural activity, of a putative internal decision commitment time in that trial. The simplified model captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. The putative neurally-inferred commitment times ("nTc") occurred at times broadly distributed across trials, and not time-locked to stimulus onset, offset, or response onset. Nevertheless, when trials were aligned to nTc, behavioral analysis showed that, as predicted by a decision commitment time, sensory evidence before nTc affected the subjects' decision, but evidence after nTc did not. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest the moment of commitment to be a useful entry point for dissecting mechanisms underlying rapid changes in internal state.

Histone deacetylase inhibition expands cellular proteostasis repertoires to enhance neuronal stress resilience.

Neurons are long-lived, terminally differentiated cells with limited regenerative capacity. Cellular stressors such as endoplasmic reticulum (ER) protein folding stress and membrane trafficking stress accumulate as neurons age and accompany age-dependent neurodegeneration. Current strategies to improve neuronal resilience are focused on using factors to reprogram neurons or targeting specific proteostasis pathways. We discovered a different approach. In an unbiased screen for modifiers of neuronal membrane trafficking defects, we unexpectedly identified a role for histone deacetylases (HDACs) in limiting cellular flexibility in choosing cellular pathways to respond to diverse types of stress. Genetic or pharmacological inactivation of HDACs resulted in improved neuronal health in response to ER protein folding stress and endosomal membrane trafficking stress in C. elegans and mammalian neurons. Surprisingly, HDAC inhibition enabled neurons to activate latent proteostasis pathways tailored to the nature of the individual stress, instead of generalized transcriptional upregulation. These findings shape our understanding of neuronal stress responses and suggest new therapeutic strategies to enhance neuronal resilience.

Population Representation of the Confidence in a Decision in the Lateral Intraparietal Area of the Macaque.

Confidence in a decision is the belief, prior to feedback, that one's choice is correct. In the brain, many decisions are implemented as a race between competing evidence-accumulation processes. We ask whether the neurons that represent evidence accumulation also carry information about whether the choice is correct (i.e., confidence). Monkeys performed a reaction time version of the random dot motion task. Neuropixels probes were used to record from neurons in the lateral intraparietal (LIP) area. LIP neurons with response fields that overlap the choice-target contralateral to the recording site (Tin neurons) represent the accumulation of evidence in favor of contralateral target selection. We demonstrate that shortly before a contralateral choice is reported, the population of Tin neurons contains information about the accuracy of the choice (i.e., whether the choice is correct or incorrect). This finding is unexpected because, on average, Tin neurons exhibit a level of activity before the report that is independent of reaction time and evidence strength-both strong predictors of accuracy. This apparent contradiction is resolved by examining the variability in neuronal responses across the population of Tin neurons. While on average, Tin neurons exhibit a stereotyped level of activity before a contralateral choice, many neurons depart from this average in a consistent manner. From these neurons, the accuracy of the choice can be predicted using a simple logistic decoder. The accuracy of the choice predicted from neural activity reproduces the hallmarks of confidence identified in human behavioral experiments. Therefore, neurons that represent evidence accumulation can also inform the monkey's confidence.

Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex.

Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.

Single neuron responses to perceptual difficulty in the mouse auditory cortex.

Perceptual learning leads to improvement in behavioral performance, yet how the brain supports challenging perceptual demands is unknown. We used two photon imaging in the mouse primary auditory cortex during behavior in a Go-NoGo task designed to test perceptual difficulty. Using general linear model analysis, we found a subset of neurons that increased their responses during high perceptual demands. Single neurons increased their responses to both Go and NoGo sounds when mice were engaged in the more difficult perceptual discrimination. This increased responsiveness contributes to enhanced cortical network discriminability for the learned sounds. Under passive listening conditions, the same neurons responded weaker to the more similar sound pairs of the difficult task, and the training protocol by itself induced specific suppression to the learned sounds. Our findings identify how neuronal activity in auditory cortex is modulated during high perceptual demands, which is a fundamental feature associated with perceptual improvement.