Neural responses

…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

Neural systems form high-dimensional representations that underpin their computational capabilities. Consequently, methods for quantifying representational similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that neural responses are deterministic and static. However, responses of biological systems are noisy and dynamically unfold over time and these characteristics can have substantial influence on a system’s computational capabilities. We first show that exi

Neural responses throughout the auditory pathway show tuning for modulations in a time-frequency representation of sound, but how these spectrotemporal modulations are encoded in the human auditory cortex remains poorly understood. Classical, linear spectrotemporal receptive field (STRF) models are simple to understand and fit but have limited predictive power, particularly for complex natural sounds in non-primary regions of the human auditory cortex. We measured responses to a diverse set of natural sounds using spatiotemporally precise intracranial recordings from human neurosurgical patien

…models that can both predict neural responses to natural stimuli and reveal interpretable functional organization in the brain. Statistical “component” models can learn interpretable, low-dimensional structure across different brain regions and subjects, but lack an explicit “encoding model” that links these components to the stimuli that drive them, and thus cannot generate predictions for new stimuli or generalize across different experiments. The predictive power of sensory encoding models has improved substantial

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

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

Normative neural network models provide a valuable tool for answering principled ‘why’ questions about neural structure, function, and organisation from an optimization perspective. However, typical approaches often compare models without controlling for differences in architecture, training dataset or model objective. As a result, it is often difficult to make strong conclusions relating variation in model performance to a given characteristic. Here we ask what factors tend to make models predict neural responses most effectively?To investigate this, we explored how hyperparameter choice impa

…in healthy participants in which the neural responses to heartb

…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

The proliferation of deep artificial neural network models has given rise to widespread interest in comparing such models to biological sensory systems. Model metamers – stimuli that produce the same responses in a model as a natural stimulus – have been proposed as a powerful test of the extent to which a model replicates the invariances of a biological sensory system. However, it has remained unclear whether discrepancies evident from metamers are the same as those identified by other model comparison benchmarks, and whether modifications that have been introduced to address other well-known

The cost of experimental neural recordings strongly limits the stimuli or conditions one can present. Ideally, we would therefore like to know how neural responses observed in specific experiments generalize beyond them. E.g., how does the brain’s response to isolated sounds presented in the lab compare to its response to those sounds within natural language in complex social settings? We addressed this problem in fly courtship–a complex, acoustically mediated social behavior. Starting with recorded responses from diverse female neurons to isolated elements of male courtship song, we found the

Linear regression and Pearson correlations are bedrock statistical analyses in neuroscience: correlations between neurons are a signature of collective information coding and computation, and linear regression quantifies the linear or nonlinear (e.g., polynomial) relationships among, e.g., neurons or brain regions. However, noise can bias the regression and correlation coefficient estimates towards zero, an effect known as regression dilution. While averaging over trials, time, or neurons--a widespread practice to mitigate noise in neuroscience--reduces the effects of noise, it does not genera

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

…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

According to the efficient coding hypothesis, neural responses represent information so as to enable the most accurate readout possible, constrained by neuronal resources and neuronal noise. To date, much of the theoretical work on efficient neural coding has focused on relatively simple models of neural activity, characterized by smooth, often unimodal tuning curves. Real neurons, however, often exhibit more complex tuning curves. For instance, in the enthorinal cortex, the periodicity of grid cell tuning curves, as well as their functional organization in modules, imparts the population code

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

…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

…and between-neuron coupling in neural responses to tastes. In fact, cortical taste responses, rather than simply reporting individual taste identities,

…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