150 Psychologie
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An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. convey information beyond what can be performed by isolated signals. Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in three common awake marmosets and investigated to what extent event-related-potentials (ERP) and broadband (BB) dynamics exhibit redundancy and synergy for auditory prediction error signals. We observed multiple patterns of redundancy and synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between lower and higher areas in the frontal cortex. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
SpikeShip: a method for fast, unsupervised discovery of high-dimensional neural spiking patterns
(2023)
Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multi-neuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-P urpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.
The hippocampal formation is linked to spatial navigation, but there is little corroboration from freely-moving primates with concurrent monitoring of three-dimensional head and gaze stances. We recorded neurons and local field potentials across hippocampal regions in rhesus macaques during free foraging in an open environment while tracking their head and eye. Theta band activity was intermittently present at movement onset and modulated by saccades. Many cells were phase-locked to theta, with few showing theta phase precession. Most hippocampal neurons encoded a mixture of spatial variables beyond place fields and a negligible number showed prominent grid tuning. Spatial representations were dominated by facing location and allocentric direction, mostly in head, rather than gaze, coordinates. Importantly, eye movements strongly modulated neural activity in all regions. These findings reveal that the macaque hippocampal formation represents three-dimensional space using a multiplexed code, with head orientation and eye movement properties dominating over simple place and grid coding during free exploration.
Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.
Neuroscience studies in non-human primates (NHP) often follow the rule of thumb that results observed in one animal must be replicated in at least one other. However, we lack a statistical justification for this rule of thumb, or an analysis of whether including three or more animals is better than including two. Yet, a formal statistical framework for experiments with few subjects would be crucial for experimental design, ethical justification, and data analysis. Also, including three or four animals in a study creates the possibility that the results observed in one animal will differ from those observed in the others: we need a statistically justified rule to resolve such situations. Here, I present a statistical framework to address these issues. This framework assumes that conducting an experiment will produce a similar result for a large proportion of the population (termed ‘representative’), but will produce spurious results for a substantial proportion of animals (termed ‘outliers’); the fractions of ‘representative’ and ‘outliers’ animals being defined by a prior distribution. I propose a procedure in which experimenters collect results from M animals and accept results that are observed in at least N of them (‘N-out-of-M’ procedure). I show how to compute the risks α (of reaching an incorrect conclusion) and β (of failing to reach a conclusion) for any prior distribution, and as a function of N and M. Strikingly, I find that the N-out-of-M model leads to a similar conclusion across a wide range of prior distributions: recordings from two animals lowers the risk α and therefore ensures reliable result, but leaves a large risk β; and recordings from three animals and accepting results observed in two of them strikes an efficient balance between acceptable risks α and β. This framework gives a formal justification for the rule of thumb of using at least two animals in NHP studies, suggests that recording from three animals when possible markedly improves statistical power, provides a statistical solution for situations where results are not consistent between all animals, and may apply to other types of studies involving few animals.
The neural mechanisms that unfold when humans form a large group defined by an overarching context, such as audiences in theater or sports, are largely unknown and unexplored. This is mainly due to the lack of availability of a scalable system that can record the brain activity from a significantly large portion of such an audience simultaneously. Although the technology for such a system has been readily available for a long time, the high cost as well as the large overhead in human resources and logistic planning have prohibited the development of such a system. However, during the recent years reduction in technology costs and size have led to the emergence of low-cost, consumer-oriented EEG systems, developed primarily for recreational use. Here by combining such a low-cost EEG system with other off-the-shelve hardware and tailor-made software, we develop in the lab and test in a cinema such a scalable EEG hyper-scanning system. The system has a robust and stable performance and achieves accurate unambiguous alignment of the recorded data of the different EEG headsets. These characteristics combined with small preparation time and low-cost make it an ideal candidate for recording large portions of audiences.
Research on psychopathy has so far been largely limited to the investigation of high-level processes, such as emotion perception and regulation. In the present work, we investigate whether psychopathy has an effect on the estimation of fundamental physical parameters, which are computed in the brain during early stages of sensory processing. We employed a simple task in which participants had to estimate their interpersonal distance from a moving avatar and stop it at a given distance. The face expression of the avatars were positive, negative, or neutral. Participants carried out the task online on their home computers. We measured the psychopathy level via a self-report questionnaire. Regardless of the degree of psychopathy, the facial expression of the avatars showed no effect on distance estimation. Our results show that individuals with a high degree of psychopathy underestimate distance of approaching avatars significantly less (let the avatar approach them significantly closer) than did participants with a lesser degree of psychopathy. Moreover, participants who scored high in Self-Centered Impulsivity underestimate the distance to approaching avatars significantly less (let the avatar approach closer) than participants with a low score. Distance estimation is considered an automatic process performed at early stages of visual processing. Therefore, our results imply that psychopathy affects basic early sensory processes, such as feature extraction, in the visual cortex.
Moving in synchrony to external rhythmic stimuli is an elementary function that humans regularly engage in. It is termed “sensorimotor synchronization” and it is governed by two main parameters, the period and the phase of the movement with respect to the external rhythm. There has been an extensive body of research on the characteristics of these parameters, primarily once the movement synchronization has reached a steady-state level. Particular interest has been shown about how these parameters are corrected when there are deviations for the steady-state level. However, little is known about the initial “tuning-in” interval, when one aligns the movement to the external rhythm from rest. The current work investigates this “tuning-in” period for each of the four limbs and makes various novel contributions in the understanding of sensorimotor synchronization. The results suggest that phase and period alignment appear to be separate processes. Phase alignment involves limb-specific somatosensory memory in the order of minutes while period alignment has very limited memory usage. Phase alignment is the primary task but then the brain switches to period alignment where it spends most its resources. In overall this work suggests a central, cognitive role of period alignment and a peripheral, sensorimotor role of phase alignment.
Temporal anticipation is a fundamental process underlying complex neural functions such as associative learning, decision-making, and motor-preparation. Here we study event anticipation in its simplest form in human participants using magnetoencephalography. We distributed events in time according to different probability density functions and presented the stimuli separately in two different sensory modalities. We found that the temporal dynamics in right parietal cortex correlate with reaction times to anticipated events. Specifically, after an event occurred, event probability was represented in right parietal activity, hinting at a functional role of event-related potential component P300 in temporal expectancy. The results are consistent across both visual and auditory modalities. The right parietal cortex seems to play a central role in the processing of event probability density. Overall, this work contributes to the understanding of the neural processes involved in the anticipation of events in time.
Models of perceptual decision making have historically been designed to maximally explain behaviour and brain activity independently of their ability to actually perform tasks. More recently, performance-optimized models have been shown to correlate with brain responses to images and thus present a complementary approach to understand perceptual processes. In the present study, we compare how these approaches comparatively account for the spatio-temporal organization of neural responses elicited by ambiguous visual stimuli. Forty-six healthy human subjects performed perceptual decisions on briefly flashed stimuli constructed from ambiguous characters. The stimuli were designed to have 7 orthogonal properties, ranging from low-sensory levels (e.g. spatial location of the stimulus) to conceptual (whether stimulus is a letter or a digit) and task levels (i.e. required hand movement). Magneto-encephalography source and decoding analyses revealed that these 7 levels of representations are sequentially encoded by the cortical hierarchy, and actively maintained until the subject responds. This hierarchy appeared poorly correlated to normative, drift-diffusion, and 5-layer convolutional neural networks (CNN) optimized to accurately categorize alpha-numeric characters, but partially matched the sequence of activations of 3/6 state-of-the-art CNNs trained for natural image labeling (VGG-16, VGG-19, MobileNet). Additionally, we identify several systematic discrepancies between these CNNs and brain activity, revealing the importance of single-trial learning and recurrent processing. Overall, our results strengthen the notion that performance-optimized algorithms can converge towards the computational solution implemented by the human visual system, and open possible avenues to improve artificial perceptual decision making.