Deep Learning ePosters

The brain needs to process high-dimensional sensory information in order to control the non-linear dynamics of body movements. One view posits that it does so modularly with different brain regions solving specific and distinct objectives that can be studied quasi-independently (e.g. visual classification in the…

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…

Alzheimer's disease (AD) is a progressive neurological disorder characterized by amyloid accumulation and behavioral changes across preclinical and clinical stages. Although AD is currently incurable, emerging machine-learning (ML) approaches are facilitating novel insights into disease etiology and treatment…

In surveying the rich reservoir of neuronal computations and brain functions, a natural question arises: what unified characteristics influence the diverse and heterogeneous dynamical states and computations in highly recurrent cortical networks? Seminal studies suggest that large-scale information processing results…

Noradrenaline (NA) is a ubiquitous neuromodulator largely produced by locus coeruleus (LC) neurons that impacts multiple cognitive processes. Non-invasive methods are used to track LC-NA activity. For instance, pupillometry is shown to be linked to arousal state, LC-NA, and other neuromodulator activity. However,…

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…

Animals rely on sensory inputs to navigate complex, dynamic environments. Understanding how the brain integrates these inputs to guide behavior remains a central question in neuroscience. Training AI and animals on the same task offers a unique opportunity to uncover both similarities and critical differences in the…

Uncertainty calibration, the process of aligning a model’s predictive confidence with the likelihood of correctness, is crucial for artificial intelligence systems. Despite recent advancements in deep learning, conventional models often struggle to calibrate confidence in a manner similar to human judgment (Guo et…

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…

Training deep neural networks typically relies on backpropagating high-dimensional error signals---a computationally intensive and biologically implausible process. However, since most tasks involve low-dimensional outputs, we propose that low-dimensional error signals may suffice for effective learning. To test this…

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 sound structures that convey meaning in speech such as phonemes and words vary widely in their duration. As a consequence, integrating across absolute time (e.g., 100 ms) and sound structure (e.g., phonemes) reflect fundamentally distinct neural computations. Auditory and cognitive models have often cast neural…

Associative learning relies on the contingency between stimuli and outcomes, yet the neural mechanisms instantiating contingency remain unclear. We investigated dopamine activity in the ventral striatum of mice performing a Pavlovian contingency degradation task. Both anticipatory licking behavior and dopamine…

Understanding how neural representations align between biological and artificial systems has emerged as a central challenge in computational neuroscience. While deep neural networks now reliably predict neural responses across multiple brain areas, their utility for understanding biological computation remains limited…

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…

Mental imagery refers to our remarkable capacity to generate percepts, emotions, and thoughts in the absence of external stimuli. This ability allows us to generate art, simulate actions and outcomes, remember previous experiences, and imagine new ones. Little is known about the mechanisms that enable mental imagery.…

Predicting sensory input responses to update an internal ``world model'' is a core principle in cognitive learning theories that try to explain mechanisms behind representation learning in the brain [1,2]. Recent machine learning methods have also adopted predictive world modeling for self-supervised representation…

Training recurrent neural networks (RNNs) to perform neuroscience tasks can be challenging. Unlike in machine learning where any architectural modification of an RNN (e.g. GRU or LSTM) is acceptable if it facilitates training, the RNN models trained as models of brain dynamics are subject to plausibility constraints…

Cortical neurons exhibit a high degree of trial-to-trial variability in response to repeated presentations of the same stimulus. We propose that this variability serves a useful computational purpose: allowing the brain to generalize from a small number of examples. To formalize this theory, we consider a Gaussian…

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…

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.…

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…

Our experiences from the recent past, even if no longer relevant, can powerfully shape our current choices. Such “serial dependence” has been observed ubiquitously in various cognitive phenomena [1]. Recent work suggests a causal role of the PPC in mediating serial dependence in rodents [2]; similar evidence in humans…