Neural activity

Investigate how neural circuits generate and regulate behaviour in model organisms. The search emphasizes large-scale measurements and perturbations of neural activity, molecular tools, functional recording and complex behaviours in naturalistic environments. The successful scholar joins Biology or Psychological and Brain Sciences, develops independent research and teaches and mentors students. Priority review is 15 November 2026.

…devices allow researchers to examine neural activity associated with symptom changes, responses to stimulation and recovery. The talk connects these measurements with the longer-term goal of developing more precise, personalised treatments, using real-world recordings to understand how neuromodulation affects brain activity and behaviour.

…synchronise those measurements with neural activity. The BRAIN Initiative call supports methods capable of capturing richer interactions among brain activity, behaviour and the surrounding environment, with emphasis on practical research utility. U01 projects may last up to five years, and individual budgets should reflect project needs. The next application deadline is 15 June 2027 at 5 p.m. in the applicant organisation’s local time. The programme-wide funding allocation is not an individual award limit.

…for Brain Research at MIT, studies the neural basis of cognition in humans and animal models across systems, computational and cognitive neuroscience, recording and perturbing neural activity as animals perform complex tasks. The Research Technician I divides time roughly equally between non-human primate care and wet-lab work: daily husbandry and welfare monitoring of monkeys, behavioral observation, training and data collection, assisting with surgeries and peri-operative care, supporting MRI procedures, preparing and steril

Investigate how neural circuits support motor learning and skilled movement. The work combines electrophysiology, genetic targeting, circuit manipulation and analysis of behaviour and neural activity.

…The conversation connects neural activity and drift-diffusion models with unconscious and conscious thought, aiming to identify tractable directions for experimental consciousness research.

John Beggs reviews the critical-brain hypothesis and the proposal that neural activity operates near a transition between ordered and chaotic regimes. He discusses information processing, dynamic range, measurement methods, experimental evidence, criticisms and the question of whether criticality is a distinctive computational property of brains or part of a broader natural phenomenon.

…requires simultaneous recording of neural activity from multiple brain regions and behaviour as they unfold. This is challenging due to complexities in a) mimicking natural settings-including la

Neural activity and behavior in awake animals reflect the interaction between multiple fast processes, such as movements, and slow processes, including arousal, neuromodulation, and learning. A variety of experimental and modeling approaches have successfully tackled single aspects along this spectrum, but a comprehensive multi-scale framework is lacking. To address this challenge, we developed two-photon (2p) mesoscope assays to record neural activity at single-cell resolution from the dorsal and lateral cortex of freely running mice during behavior. We found that whole-brain single-cell dyna

Convolutional neural networks (CNNs) provide a generalizable architecture for encoding models that predict neural activity evoked by natural sounds with much greater accuracy than other established models. However, the complexity of CNNs makes it difficult to discern the computational properties that support their improved predictive power. We used a combination of local linear approximation and dimensionality reduction to measure the tuning subspace captured by a CNN. Single-unit data was recorded using microelectrode arrays from primary auditory cortex (A1) of awake, passively listening ferr

Locomotor behaviors are composed of ordered transitions between motor states required to produce a functional outcome. However, the circuit mechanisms by which the brain generates a sequential ordering of motor actions for constructive behavioral output, particularly in freely-behaving animals, is largely unknown. With 302 neurons within a well-defined connectome and a library of genetic tools, complex yet discernible behaviors, and recent advances in brain-wide imaging, the nematode Caenorhabditis elegans offers a unique opportunity to study how the brain generates behavior. To better underst

…decision-making models predict that neural activity will exhibit persistent ramping with evidence. By contrast, recent studies in rodents and primates have observed that the representation of accumulated evidence shifts between different neural populations as decision-making demands change within a trial. Prior models only accumulate and maintain evidence, but fail to explain the transfer of graded evidence between populations. This transfer supports the continuous representation of a decision when an anim

The goal of behavioral neuroscience is to understand the relationship between neural activity and behavior. One traditional approach for studying this relationship has been to build models that either translate from the neural activity to behavior (decoding) or from behavior to neural activity (encoding). Recent advances in self-supervised learning now allow for modeling both modalities jointly. In this work, we introduce a multimodal masked modeling approach that masks portions of both behavior and neural activity, using the unmasked data to make predictions across both modalities. We evaluat

In neuroscience, we record neural activity over time. We can try to make sense of this data by studying the (low-dimensional) space the activity is constrained to. To identify these spaces, one typically either treats the rows (neurons / spike trains) or the columns (timepoints / population vectors) of the activity matrix as points. In general, these two perspectives will not give the same space, obscuring the underlying structure of the data. Here, we provide a unified framework for thinking about this challenge in neural data. We show that if rows and columns are convex on the space(s),

…unclear whether they can also explain neural activity. Testing them using neural data is complicated by the fact that linking them to neural activity requires two key moving parts: (1) a hypothesis about which latent variable is represented by a neuron, or neural population (``generative model'') and (2) a hypothesis about how beliefs about a latent variable (``posterior'') are represented by neural responses. Here, we present two new methods that in conjunction allow us to, first, fa

Observation of neural firing patterns can constrain theories for the types of activity patterns that the brain uses to guide behavior. However, directly perturbing neural activity, ideally with great specificity, is required to causally test any particular theory. We combined two-photon (2p) imaging and cellular resolution optogenetic photostimulation1 to causally test how neural activity in the mouse primary visual cortex (V1) is read out to detect visual stimuli. Both an unweighted mean readout or a weighted read out of visually sensitive neurons could support detection, but the weighted rea

…Yet, the neural mechanisms generating these representations remain unclear. Here, we hypothesize that the claustrum, a subcortical nucleus highly interconnected with the neocortex, encodes decision variables and aids in sustaining their representations in cerebral cor

…states align with distinct epochs of activity in medial prefrontal cortex (mPFC) – a coincidence that

Integrating task-relevant information into neural representations is essential for both biological and artificial intelligence. However, current experimental limitations in tracking synaptic weight changes make it challenging to understand learning mechanisms in animals. To address this, we propose a framework based on neural manifold geometry to study representation learning directly from neural activity. A neural manifold represents the collection of population responses to the same task condition (e.g., a cat manifold for responses to images of cats) and can be estimated from recorded activ

Traditionally, the processing of object motion and form has been attributed to the dorsal and ventral visual pathways, respectively. However, recent studies challenge this strict dichotomy. For example, Hong et al. (2016) demonstrated that the ventral stream represents spatial information, while Ramezanpour et al. (2024) showed that object motion could be decoded from the inferior temporal (IT) cortex. These findings prompt further exploration of how the ventral pathway supports the integration of motion and form, particularly in naturalistic environments. In our study, we aimed to investigate

High-dimensional neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related latent variables. A fundamental goal of neural data analysis consists of identifying such variables and their mapping to neural activity. The canonical approach is to assume the latent variables are behaviour (e.g. the measured location of the animal) and visualise the subsequent tuning curves. However, significant mismatches between behaviour and the encoded variables may still exist---the agent may be thinking of another location, or be uncertain of its own---distorting the tuni

Cortical neuronal networks exhibit a rich dynamical repertoire and are capable of performing many different computations, for instance the generation of multiple distinct dynamical patterns in motor cortex. However, we do not know what connectivity structures support this capability, nor the mechanism for selecting among the dynamics required for different tasks. We develop an analytical theory describing a network model capable of multi-task computation, where the connectivity is generated as a sum over a large number of low-rank matrices, each of which is specialized to perform one low-dimen

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new t

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a k