Topic: neural responses

We developed a novel paradigm measuring implicit identity recognition using Fast Periodic Visual Stimulation (FPVS) with EEG among 16 students and 12 police officers with normal face processing abilities. Participants' neural responses to a 1-Hz tagged oddball identity embedded within a 6-Hz image stream revealed implicit recognition with high-quality mugshots but not CCTV-like images, suggesting optimal resolution requirements. Our findings extend previous research by demonstrating that even unfamiliar identities can elicit robust neural recognition signatures through brief, repeated passive

Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to dete

Various clinical cases have provided evidence linking cardiovascular, neurological, and psychiatric disorders to changes in the brain-heart interaction. Our recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness and sum to the existing evidence in healthy participants in which the neural responses to heartb

Higher cortical areas carry a wide range of sensory, cognitive, and motor signals supporting complex goal-directed behavior. These signals mix in heterogeneous responses of single neurons, making it difficult to untangle underlying mechanisms. I will present two approaches for revealing interpretable circuit mechanisms from heterogeneous neural responses during cognitive tasks. First, I will show a flexible nonparametric framework for simultaneously inferring population dynamics on single trials and tuning functions of individual neurons to the latent population state. When applied to recordin

A signature feature of cortical circuits is the irregularity of neuronal firing, which manifests itself in the high temporal variability of spiking and the broad distribution of rates. Theoretical works have shown that this feature emerges dynamically in network models if coupling between cells is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order 1/\sqrt{K}. However, the degree to which these models capture the mechanisms underlying neuronal firing in cortical circuits is not fully understood. Results have been derived using neuron models with

Quantifying the timing (duration and frequency) of brief visual events is vital to human perception, multisensory integration and action planning. For example, this allows us to follow and interact with the precise timing of speech and sports. Here we investigate how visual event timing is represented and transformed across the brain’s hierarchy: from sensory processing areas, through multisensory integration areas, to frontal action planning areas. We hypothesized that the dynamics of neural responses to sensory events in sensory processing areas allows derivation of event timing representati

Neural activity in response to sensory stimuli leads to a reorganization of neuronal ensemble dynamics, which can be captured by lower-dimensional latent dynamics. Recurrent Neural Networks (RNN) have been proven successful at extracting such latent representations of neural dynamics. Here, we applied an RNN to the data available in the Steinmetz Neuropixels dataset, containing electrophysiological and behavioral data from mice engaged in a visual go/no go contrast detection task. First, we chose as our input (‘seed’) region a subset of spike rates recorded from mouse primary visual cortex (VI

In the social context, animal species depend on vocal communication throughout their life, as they are the primary source of communication, yet the neurophysiological underpinnings of the perception of intra-specific communication signals are still poorly understood. The brain processes underpinning the capacity to discern between familiar and unfamiliar vocalizations is still in infancy among avian species. Previous studies have demonstrated that zebra finch neurons in the secondary auditory cortex (caudomedial nidopallium or NCM) respond differently to distant calls from familiar conspecific

Classic studies show that in many species – from leech and cricket to primate – responses of neural populations can be quite successfully read out using a measure neural population activity termed the population vector. However, despite its successes, detailed analyses have shown that the standard population vector discards substantial amounts of information contained in the responses of a neural population, and so is unlikely to accurately describe how signal communication between parts of the nervous system. I will describe recent theoretical results showing how to modify the population vect

In a few months, human infants develop complex capacities in numerous cognitive domains. They learn their native language, recognize their parents, refine their numerical capacities and their perception of the world around them but are they conscious and how can we study consciousness when no verbal report is possible? One way to approach this question is to rely on the neural responses correlated with conscious perception in adults (i.e. a global increase of activity in notably frontal regions with top-down amplification of the sensory levels). We can thus study at what age the developing ana

Neural and behavioral responses to sensory stimuli are notoriously variable from trial to trial. Does this mean the brain is inherently noisy or that we don’t completely understand the nature of the brain and behavior? Here we monitor the state of activity of the animal through videography of the face, including pupil and whisker movements, as well as walking, while also monitoring the ability of the animal to perform a difficult auditory or visual task. We find that the state of the animal is continuously changing and is never stable. The animal is constantly becoming more or less activat

The moment that a tasty substance enters an animal’s mouth, the clock starts ticking. Taste information transduced on the tongue signals whether a potential food will nourish or poison, and the animal must therefore use this information quickly if it is to decide whether the food should be swallowed or expelled. The system tasked with computing this important decision is rife with cross-talk and feedback—circuitry that all but ensures dynamics and between-neuron coupling in neural responses to tastes. In fact, cortical taste responses, rather than simply reporting individual taste identities,

Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment jointly models shared information and idiosyncratic topographies. Pattern vectors for neural responses and connectivities are projected into a common, high-dimensional information space, rather than being aligned in a canonical anatomical space. Hyperalignment calculates individual transformation matrices that preserve the geometry of pairwise dissimilarities between pattern vectors. Individual cortical topographies are modeled as mixtures of overlapping, individual-specific

A vast variety of sensory cues are represented as distinct patterns of neural responses in the brain. To retrieve memories associated with sensory cues, one would assume that the representations of sensory signals should be stable over time. However, recent development of population-level recording techniques has revealed that these representations can drift over time $^{1}$. To investigate the mechanisms and consequences of representational drift, we formulate a random feedforward network model $^{2,3}$ that incorporates stochastic fluctuations in membrane potential and synaptic strength $^{4