Neural mechanisms for the localization of unexpected external motion

The extraordinary enigma of ordinary tickle behavior: Why gargalesis still puzzles neuroscience

Gargalesis, or tickle, is one of the most trivial yet enigmatic human behaviors. We do not know how a touch becomes ticklish or why we respond to other people's tickles but not our own. No theory satisfactorily explains why touch on some body areas feels more ticklish than on others or why some people are highly sensitive while others remain unresponsive. Gargalesis is likely the earliest trigger for laughter in life, but it is unclear whether we laugh because we enjoy it. Socrates, Aristotle, Bacon, Galileo, Descartes, and Darwin theorized about tickling, but after two millennia of intense philosophical interest, experimentation remains scarce. This review argues that gargalesis is an exhilarating scientific puzzle with far-reaching implications for developmental, sensorimotor, social, affective, clinical, and evolutionary neuroscience. We reflect on the challenges in defining and eliciting ticklish sensations in the lab and unraveling their neural mechanism, discuss five classic yet unanswered questions about tickle, and suggest directions for future research.

Whisking and locomotion are jointly represented in superior colliculus neurons

Active sensation requires the brain to interpret external stimuli against an ongoing estimate of body position. While internal estimates of body position are often ascribed to the cerebral cortex, we examined the midbrain superior colliculus (SC), due to its close relationship with the sensory periphery as well as higher, motor-related brain regions. Using high-density electrophysiology and movement tracking, we discovered that the on-going kinematics of whisker motion and locomotion speed accurately predict the firing rate of mouse SC neurons. Neural activity was best predicted by movements occurring either in the past, present, or future, indicating that the SC population continuously estimates a trajectory of self-motion. A combined representation of slow and fast whisking features predicted absolute whisker angle at high temporal resolution. Sensory reafference played at least a partial role in shaping this feature tuning. Taken together, these data indicate that the SC contains a joint representation of whisking and locomotor features that is potentially useful in guiding complex orienting movements involving the face and limbs.

Stimulus selection enhances value-modulated somatosensory processing in the superior colliculus

A fundamental trait of intelligent behavior is the ability to respond selectively to stimuli with higher value. Where along the neural hierarchy does somatosensory processing transition from a map of stimulus location to a map of stimulus value? To address this question, we recorded single-unit activity from populations of neurons in somatosensory cortex (S1) and midbrain superior colliculus (SC) in mice conditioned to respond to a positive-valued stimulus and withhold responses to an adjacent, negative-valued stimulus. The stimulus preference of the S1 population was equally weighted towards either stimulus, in line with a somatotopic map. Surprisingly, we discovered a large population of SC neurons that were disproportionately biased towards the positive stimulus. This disproportionate bias was largely driven by enhanced spike suppression for the negative stimulus. Removing the opportunity for mice to behaviorally select the positive stimulus reduced positive stimulus bias and spontaneous firing rates in SC but not S1, suggesting that neural selectivity was augmented by task readiness. Similarly, the spontaneous firing rates of SC but not S1 neurons predicted reaction times, suggesting that SC neurons played a persistent role in perceptual decision-making. Taken together, these data indicate that the somatotopic map in S1 is transformed into a value-based map in SC that encodes stimulus priority.

Predictions of bimanual self-touch determine the temporal tuning of somatosensory perception

We easily distinguish self-touch from the touch of others. This distinction is suggested to arise because the brain predicts the somatosensory consequences of voluntary movements using an efference copy and attenuates the predicted self-touch. However, it remains unclear how these predictions impact somatosensory perception before or after the self-touch occurs. Here, participants discriminated forces applied to their left index finger at different phases of the right hand's reaching movement toward the left hand. We observed that forces felt progressively weaker during the reaching, reached their minimum perceived intensity at the time of self-touch, and recovered after the movement ended. We further demonstrated that this gradual attenuation vanished during similar reaching movements that did not produce expectations of self-touch between the two hands. Our results indicate a temporal tuning of somatosensory perception during movements to self-touch and underscore the role of sensorimotor context in forming predictions that attenuate the self-touch intensity.

Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.

The locus coeruleus as a global model failure system

Predictive processing models posit that brains constantly attempt to predict their sensory inputs. Prediction errors signal when these predictions are incorrect and are thought to be instructive signals that drive corrective plasticity. Recent findings support the idea that the locus coeruleus (LC) - a brain-wide neuromodulatory system - signals several types of prediction error. I discuss how these findings support models proposing that the LC signals global model failures: instances where predictions about the world are strongly violated. Focusing on the cortex, I explore the utility of this signal in learning rate control, how the LC circuit may compute the signal, and how this view may aid our understanding of neurodivergence.

Assessing cross-contamination in spike-sorted electrophysiology data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner.