Neuro-Informatics ePosters

Population recording offer a window into the underlying dynamics of brain regions. Latent Variable Models (LVMs) infer these dynamics treating the observations as projections from the latents. Evaluating the quality of these models remains challenging, as we don't have access to the true underlying dynamics. A common…

Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons…

Electrophysiological recordings capture signals from hundreds of neurons simultaneously, but isolating single-cell activity often requires manual curation due to limitations in spike- sorting algorithms. As dataset sizes grow, the time and expertise required for accurate and consistent human curation pose a major…

Manual annotation of birdsong remains a significant bottleneck in systems neuroscience, especially for species like canaries with complex vocal repertoires. Existing unsupervised dimensionality reduction approaches, such as Uniform Manifold and Projection (UMAP), can be used to analyze stereotyped songs but require…

To understand the neural basis of many cognitive processes, such as learning and memory, we need to track the millisecond, single-spike activity of neurons across days. However, compared to calcium imaging methods, tracking neurons in chronic electrophysiological recordings remains challenging. Recent studies have…

Brain-computer interfaces (BCIs) have the potential to enable seamless interaction between humans and technology, with particular power for helping patients with paralysis, motor disabilities, or other neurodegenerative diseases move or communicate with the world. BCIs use ``decoders'‘ to relate neural activity to…

The relationship between morphology, connectivity, and function is a central challenge in neuroscience. The possibility of predicting neuronal function from morphology and connectivity has motivated the field to produce connectomes for increasingly complex organisms. With the recent completion of multiple connectomes…

Introduction: Spontaneous activity in visual cortex is high-dimensional and state-dependent, yet its organizing principles are unclear (Stringer et al. Science 2019; Berkes et al. Science 2011). We combine large-scale imaging, modelling, and theory to ask: 1. Does spontaneous activity share the structure of evoked…

Recent findings have shown that the brain’s internal representation of the external world shifts over days, a phenomenon termed representational drift [Driscoll et al., 2017, Rule et al., 2020]. Thus, it becomes crucial to account for such non-stationarities when investigating the roles of circuits in the brain. While…

Reservoir computing is a machine learning framework in which only the output layer is trained to perform a task; the rest – composed of an input layer and recurrent neural network – has fixed connections. The benefit of restricting training to the output layer is that it does not require the computationally expensive…

Recent advances in recording techniques and behavioral monitoring are revolutionizing systems neuroscience, enabling the study of behavior in naturalistic settings. Using fiber photometry data from dopamine axons (DA) in the striatum, we set out to characterize the homogeneity and heterogeneity across striatal areas…

Sensory, cognitive, and motor functions rely on transmission/integration of signals across multiple spatiotemporal scales which are fundamentally constrained by neuroanatomy. Uncovering connectivity patterns that shape signal propagation is thus of central importance to understanding brain computations. An…

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…

Temporal sequences are an important feature of neural information processing in biology. Neurons can fire a spike with millisecond precision, and, at the network level, repetitions of spatiotemporal spike patterns are observed in neurobiological data. However, methods for detecting precise temporal patterns in neural…

Connectomes provide critical constraints for neural circuit models. Advances in electron microscopy have dramatically increased the scale of connectome datasets, enabling more realistic modeling of neural circuits. However, challenges remain in using these datasets. Many models still rely on unconstrained…

We present, for the first time, insights into functional synchronization of individual neurons under a complete anatomical neural connectivity map at a massive scale. While functional synchrony has been extensively studied as a mechanism of information processing in the brain, previous work has been limited to small…

Recent technological advances have enabled the measurement of the ‘connectome,’ or synaptic wiring diagram, of neurons across large brain regions or entire brains. Theoretical work has long sought to link connectivity features of large neural networks to their emergent dynamics. Nevertheless, how to integrate…

Multimodal data fusion can facilitate a more complete understanding of neural dynamics. In neuroscience, modalities such as neuronal spiking activity, local field potentials (LFPs), and behavioral signals capture various aspects of brain processes. By leveraging the complementary strengths of different modalities,…

Spatiotemporal patterns of neuronal activity are hypothesized to be essential for information processing in the brain. They suggest the existence of cell assemblies, groups of co-active neurons that represent a distinct cognitive unit, potentially related to specific behaviors or representations. Conventionally, cell…

Recent studies have proposed that neural circuits generate a task manifold: i.e., a subset of the neural state-space to which neural activity is confined as an animal performs a task. Thus, discovering and characterising these manifolds from experimental data can shed light on the neural computations unfolding within…

Inferring the underlying connectivity of a network from observations of its activity is a long-standing challenge. In the brain, this challenge is exacerbated by dynamic reconfiguration of effective connectivity, mediated by both synaptic plasticity and neuromodulation. Traditional methods for extracting connectivity…

To date, most studies of neural representation have focused on how a single stimulus is encoded in the spiking activity of neurons. Consequently, little is known about how sensory neurons can preserve information from multiple stimuli – a problem given that many sensory neurons have relatively broad receptive fields.…

The mapping between neural activity and external behavioral variables can change across experimental conditions and behaviors. For example, in prefrontal cortex, the coding of a stimulus’s direction is different soon after stimulus presentation, versus during the delay period. Similarly, for a monkey performing a…

Predictions about sensory inputs (e.g. contextual signals from associative cortical areas to sensory areas) arrive primarily in the superficial layers of the cortical sheet, where they are received by the distal apical dendrites of pyramidal neurons. These distal apical dendrites are long tree-like extensions which…