Engineering ePosters

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 neural control of movement is a fundamental aspect of understanding how the nervous system coordinate and regulate motor behavior. Procedural safety is a critical concern for movement control, especially for online tasks in high-dimensional control spaces. Current safe exploration algorithms exhibit inefficiency…

Developing a technology able to measure in-vivo and non-invasively the electrical activity of the human spinal cord would fulfill an important and still unmet need in both basic and clinical research. In this work, we describe a novel recording and analysis pipeline to obtain non-invasive, high-resolution images of…

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing…

Computational models have been seminal in expanding our understanding of how neural, biomechanical, or physical elements can give rise to behavior. With the forthcoming emergence of nearly complete connectomes of the central nervous system, musculature, and biomechanics of Drosophila, it is only now possible to…

The study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to…

Complex brain functions are realized through the coordinated activity of populations of neurons. To manipulate brain function, it is crucial for brain stimulation techniques to achieve targeted control at the level of neural populations. Electrical microstimulation (uStim) has the potential to create targeted activity…

Synchronous movements of the entire face, from grimacing to chewing, offer significant insights into internal states. Mice, with discernible facial responses and evolutionarily conserved mammalian facial movement control circuits, provide an ideal model to unravel the link between facial movement and internal…

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers…

Sensory prosthesis research has advanced at a rapid pace, yet standard stimulation pulse shapes have remained largely unchanged. Most prostheses typically utilize symmetric pulses to deliver electrical stimulation to peripheral nerves. Recent neurophysiological studies, however, have shown that these symmetric pulses…

The onset of a stimulus, whether sensory or non-sensory, quenches trial-to-trial neural variability. The universality of this phenomenon across brain areas and species implies that it is a fundamental strategy employed by the brain to boost the fidelity of stimulus representation. Multiple network models have…

Deep Brain Stimulation (DBS) involves implanting electrodes in the brain to modulate neural activity, treating conditions like Epilepsy [1]. Using synthetic micro-electrode array (MEA) signals as stimuli in a DBS system enables electrical impulses that mimic natural brain activity, improving precision and…

Transcranial direct current stimulation (tDCS) has proven to be effective in reducing the seizure frequency in patients with epilepsy. This therapy greatly benefits from designing personalized electrode montages (electrode positions and currents) that target the appropriate areas of the brain. In the state-of-the-art…

Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively…

The use of electromyography (EMG) for translating muscle activity into precise movements has become a pivotal technique to control myoelectric prosthetic devices, significantly improving the quality of life for amputees. Traditional EMG control of prosthetics relies on techniques that decode forearm muscle activity…

Since decades ago, biologists have recognized the existence of tradeoffs in biological systems (e.g., [1]). Yet, the first study that borrowed the idea of Pareto-optimality to analyze tradeoffs in biological systems was published in 1995 [2]. In 2008, following [2], it was hypothesized in [3] that Pareto-optimality,…

Expanding use of large-scale neural recordings has led to paradigm shifts in how neural computation is understood [1]. Technologies used for collecting these datasets face fundamental limitations ranging from power dissipation, penetration depth, and resolution [2] while facing additional practical challenges related…

The rise of spiking neural networks (SNNs) offers a promising alternative to traditional activation-based deep neural networks, advancing the pursuit of more sustainable and ethical computing architectures. Despite its potential, neuromorphic event-based computing, which promises to fully harness the benefits of…

Sharp-wave ripples (SWRs) are thought to play a crucial role in memory consolidation, learning, and the formation of long-term memories by facilitating the transfer of information from the hippocampus to neocortical networks. To understand the mechanisms governing SWRs, we have developed a closed-loop stimulation…

The Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories [1] is a compilation of three-dimensional task-space motion data simultaneously measured by two motion tracking systems. The first one, an optical motion capture system, provided robust reference data. The second recording system consisted of a…

Modeling non-invasive brain stimulation, particularly transcranial magnetic stimulation (TMS) of the primary motor cortex (M1), has been previously explored through simulation methods at different scales. However, the coupling of TMS induced electric fields to neural mass models is still largely unexplored, while…

Recent advances in neuronal recording technology have facilitated the development of large-scale, high-density microelectrode arrays resulting in a substantial increase (>10x) in the number of neurons that can be studied simultaneously within a localized area of neural tissue. A prime example is the recent development…

In autonomous navigation, an agent's knowledge of its environment is represented as sensory inputs (states) linked together by actions. A popular approach for acquiring this knowledge is reinforcement learning (RL), where learning is stereotyped, i.e. an agent learns to react to a state with the action associated with…

Eliciting percepts akin to natural vision using brain computer interfaces is the holy grail of vision prosthetics. However, progress has been slowed by our lack of understanding of how the human visual cortex processes and encodes information as well as how external perturbations, such as electrical stimulation via…