Vibration
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Spontaneous activity competes with externally evoked responses in sensory cortex
The interaction between spontaneously and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15-30 Hz beta-band represent activation of resting state networks and can mask perception of external cues. Yet demonstration of the effect of beta power modulation on perception in real-time is missing, and little is known about the underlying mechanism. In this talk I will present the methods we developed to fill this gap together with our recent results. We used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst-occupancy on perception can be counterbalanced in real-time by adjusting the vibration amplitude. Offline analysis of firing-rates and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of firing-rate. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.
Sensory and metasensory responses during sequence learning in the mouse somatosensory cortex
Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. Touch is one sensory modality where temporal patterning carries key information, and the rodent whisker system is a prominent model for understanding neuronal coding and plasticity underlying touch sensation. Neurons in this system are precise encoders of fluctuations in whisker dynamics down to a timescale of milliseconds, but it is not clear whether they can refine their encoding abilities as a result of learning patterned stimuli. For example, can they enhance temporal integration to become better at distinguishing sequences? To explore how cortical coding plasticity underpins sequence discrimination, we developed a task in which mice distinguished between tactile ‘word’ sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory “barrel” cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal’s action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal’s learnt action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were already found in naïve mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf sequence learning results in neurons whose activity reflects the learnt association between target sequence and licking, rather than a refined representation of sensory features. Taken together with results from other laboratories, our findings suggest that neurons in sensory cortex are involved in task-specific processing and that an animal does not sense the world independently of what it needs to feel in order to guide behaviour.
The active modulation of sound and vibration perception
The dominant view of perception right now is that information travels from the environment to the sensory system, then to the nervous systems which processes it to generate a percept and behaviour. Ongoing behaviour is thought to occur largely through simple iterations of this process. However, this linear view, where information flows only in one direction and the properties of the environment and the sensory system remain static and unaffected by behaviour, is slowly fading. Many of us are beginning to appreciate that perception is largely active, i.e. that information flows back and forth between the three systems modulating their respective properties. In other words, in the real world, the environment and sensorimotor loop is pretty much always closed. I study the loop; in particular I study how the reverse arm of the loop affects sound and vibration perception. I will present two examples of motor modulation of perception at two very different temporal and spatial scales. First, in crickets, I will present data on how high-speed molecular motor activity enhances hearing via the well-studied phenomenon of active amplification. Second, in spiders I will present data on how body posture, a slow macroscopic feature, which can barely be called ‘active’, can nonetheless modulate vibration perception. I hope these results will motivate a conversation about whether ‘active’ perception is an optional feature observed in some sensory systems, or something that is ultimately necessitated by both evolution and physics.
Neural mechanisms of proprioception and motor control in Drosophila
Animals rely on an internal sense of body position and movement to effectively control motor behaviour. This sense of proprioception is mediated by diverse populations of internal mechanosensory neurons distributed throughout the body. My lab is trying to understand how proprioceptive stimuli are detected by sensory neurons, integrated and transformed in central circuits, and used to guide motor output. We approach these questions using genetic tools, in vivo two-photon imaging, and patch-clamp electrophysiology in Drosophila. We recently found that the axons of fly leg proprioceptors are organized into distinct functional projections that contain topographic representations of specific kinematic features: one group of axons encodes tibia position, another encodes movement direction, and a third encodes bidirectional movement and vibration frequency. Whole-cell recordings from downstream neurons reveal that position, movement, and directional information remain segregated in central circuits. These feedback signals then converge upon motor neurons that control leg muscles during walking. Overall, our findings reveal how a low-dimensional stimulus – the angle of a single leg joint – is encoded by a diverse population of mechanosensory neurons. Specific proprioceptive parameters are initially processed by parallel pathways, but are ultimately integrated to influence motor output. This architecture may help to maximize information transmission, processing speed, and robustness, which are critical for feedback control of the limbs during adaptive locomotion.
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