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Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding. Sensory systems must adapt to changing perceptual demands. Here, the authors show that changes in sub-second timescale neural population dynamics enable flexible sensory encoding during different behavioural states, in mouse visual cortex. [ABSTRACT FROM AUTHOR]

Alzheimer's disease and other dementias are thought to underlie a progressive impairment of neural plasticity. Previous work in mouse models of Alzheimer's disease shows pronounced changes in artificially-induced plasticity in hippocampus, perirhinal and prefrontal cortex. However, it is not known how degeneration disrupts intrinsic forms of brain plasticity. Here we characterised the impact of tauopathy on a simple form of intrinsic plasticity in the visual system, which allowed us to track plasticity at both long (days) and short (minutes) timescales. We studied rTg4510 transgenic mice at early stages of tauopathy (5 months) and a more advanced stage (8 months). We recorded local field potentials in the primary visual cortex while animals were repeatedly exposed to a stimulus over 9 days. We found that both short- and long-term visual plasticity were already disrupted at early stages of tauopathy, and further reduced in older animals, such that it was abolished in mice expressing mutant tau. Additionally, visually evoked behaviours were disrupted in both younger and older mice expressing mutant tau. Our results show that visual cortical plasticity and visually evoked behaviours are disrupted in the rTg4510 model of tauopathy. This simple measure of plasticity may help understand how tauopathy disrupts neural circuits, and offers a translatable platform for detection and tracking of the disease. Amalia Papanikolaou and Fabio Rodrigues et al. examine visual cortical function and intrinsic plasticity using the rTg4510 tauopathy mouse model. They demonstrate that, while basal visual cortical responses seem largely unaffected, intrinsic plasticity and the behavioural response to a visual stimulus is compromised, even in young rTg4510 mice. [ABSTRACT FROM AUTHOR]

A major role of vision is to guide navigation, and navigation is strongly driven by vision1-4. Indeed, the brain’s visual and navigational systems are known to interact5,6, and signals related to position in the environment have been suggested to appear as early as in the visual cortex6,7. Here, to establish the nature of these signals, we recorded in the primary visual cortex (V1) and hippocampal area CA1 while mice traversed a corridor in virtual reality. The corridor contained identical visual landmarks in two positions, so that a purely visual neuron would respond similarly at those positions. Most V1 neurons, however, responded solely or more strongly to the landmarks in one position rather than the other. This modulation of visual responses by spatial location was not explained by factors such as running speed. To assess whether the modulation is related to navigational signals and to the animal’s subjective estimate of position, we trained the mice to lick for a water reward upon reaching a reward zone in the corridor. Neuronal populations in both CA1 and V1 encoded the animal’s position along the corridor, and the errors in their representations were correlated. Moreover, both representations reflected the animal’s subjective estimate of position, inferred from the animal’s licks, better than its actual position. When animals licked in a given location—whether correctly or incorrectly—neural populations in both V1 and CA1 placed the animal in the reward zone. We conclude that visual responses in V1 are controlled by navigational signals, which are coherent with those encoded in hippocampus and reflect the animal’s subjective position. The presence of such navigational signals as early as a primary sensory area suggests that they permeate sensory processing in the cortex. When running through a virtual reality corridor, a mouse’s position is represented in both the hippocampus (as expected) and the primary visual cortex, for places that are visually identical. [ABSTRACT FROM AUTHOR]

Successful navigation through the world requires accurate estimation of one's own speed. To derive this estimate, animals integrate visual speed gauged from optic flow and run speed gauged from proprioceptive and locomotor systems. The primary visual cortex (V1) carries signals related to visual speed, and its responses are also affected by run speed. To study how V1 combines these signals during navigation, we recorded from mice that traversed a virtual environment. Nearly half of the V1 neurons were reliably driven by combinations of visual speed and run speed. These neurons performed a weighted sum of the two speeds. The weights were diverse across neurons, and typically positive. As a population, V1 neurons predicted a linear combination of visual and run speeds better than either visual or run speeds alone. These data indicate that V1 in the mouse participates in a multimodal processing system that integrates visual motion and locomotion during navigation. [ABSTRACT FROM AUTHOR]

Sensory systems exhibit mechanisms of neural adaptation, which adjust neuronal activity on the basis of recent stimulus history. In primary visual cortex (V1) in particular, adaptation controls the responsiveness of individual neurons and shifts their visual selectivity. What benefits does adaptation confer on a neuronal population? We measured adaptation in the responses of populations of cat V1 neurons to stimulus ensembles with markedly different statistics of stimulus orientation. We found that adaptation served two homeostatic goals. First, it maintained equality in the time-averaged responses across the population. Second, it maintained independence in selectivity across the population. Adaptation scaled and distorted population activity according to a simple multiplicative rule that depended on neuronal orientation preference and on stimulus orientation. We conclude that adaptation in V1 acts as a mechanism of homeostasis, enforcing a tendency toward equality and independence in neural activity across the population. [ABSTRACT FROM AUTHOR]

During anesthesia, slow-wave sleep and quiet wakefulness, neuronal membrane potentials collectively switch between de- and hyperpolarized levels, the cortical UP and DOWN states. Previous studies have shown that these cortical UP/DOWN states affect the excitability of individual neurons in response to sensory stimuli, indicating that a significant amount of the trial-to-trial variability in neuronal responses can be attributed to ongoing fluctuations in network activity. However, as intracellular recordings are frequently not available, it is important to be able to estimate their occurrence purely from extracellular data. Here, we combine in vivo whole cell recordings from single neurons with multi-site extracellular microelectrode recordings, to quantify the performance of various approaches to predicting UP/DOWN states from the deep-layer local field potential (LFP). We find that UP/DOWN states in deep cortical layers of rat primary auditory cortex (A1) are predictable from the phase of LFP at low frequencies (< 4 Hz), and that the likelihood of a given state varies sinusoidally with the phase of LFP at these frequencies. We introduce a novel method of detecting cortical state by combining information concerning the phase of the LFP and ongoing multi-unit activity. [ABSTRACT FROM AUTHOR]

During navigation, rodents continually sample the environment with their whiskers. How locomotion modulates neuronal activity in somatosensory cortex, and how it is integrated with whisker-touch remains unclear. Here, we compared neuronal activity in layer 2/3 (L2/3) and L5 of barrel cortex using calcium imaging in mice running in a tactile virtual reality. Both layers increase their activity during running and concomitant whisking, in the absence of touch. Fewer neurons are modulated by whisking alone. Whereas L5 neurons respond transiently to wall-touch during running, L2/3 neurons show sustained activity. Consistently, neurons encoding running-with-touch are more abundant in L2/3 and they encode the run-speed better during touch. Few neurons across layers were also sensitive to abrupt perturbations of tactile flow during running. In summary, locomotion significantly enhances barrel cortex activity across layers with L5 neurons mainly reporting changes in touch conditions and L2/3 neurons continually integrating tactile stimuli with running. The influence of locomotion on somatosensory processing in barrel cortex is not well understood. Here the authors report distinct layer-specific responses, with L5 primarily reporting changes in touch condition while L2/3 neurons integrating touch and locomotion continuously. [ABSTRACT FROM AUTHOR]