Engineering ePosters

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…

Low-intensity ultrasound neuromodulation is a rapidly emerging technology due to its non-invasiveness and millimeter-scale precision. Despite numerous experimental settings demonstrating that ultrasonic waves can modulate the neural activity in different brain areas, the interaction between the ultrasonic source and…

Despite being inspired by biological neuronal networks (BNNs), artificial neural networks (ANNs) have fundamental differences from their biological counterparts. While ANNs excel at solving complex problems across various domains, BNNs outperform them in terms of energy efficiency and processing highly complex…

By causally mapping neural activity to behavior [1], Brain-Computer Interfaces (BCI) offer a means to study the dynamics of sensorimotor learning. Here, we combine computational modeling and data analysis to study the neural learning algorithm [2] monkeys use to adapt to a changed output mapping in a center-out…

For motor control, the activities of local neural nets need to be coordinated, which can be attained via direct synaptic connections. Phylogenetically, the main purpose of the brain is the active interaction with the environment which suggests the presence of an alternative neural coordination mechanism, namely one…

The success of Artificial Neural Networks (ANNs) trained with error-backpropagation algorithms relies on the combination of offline training, vast amounts of data, and energy-intensive simulations on dedicated hardware accelerators. In contrast, biological neurons can learn quickly in a self-supervised manner by…

Spiking neural networks (SNNs) are characterized by low power consumption, highly parallel computation and event-driven information processing [1]. Many of the concepts that promise to improve the efficiency of SNNs compared to conventional deep neural networks, such as spikes, sparsity, and static suppression, are…

Invasive cortical brain-machine interfaces (BMIs) can significantly improve life quality for motor-impaired patients. However, externally mounted BMIs carry the risk of wound infection, necessitating fully implanted systems with integrated signal processing. Such systems must ensure reliable and accurate data…

Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work [1, 2] has demonstrated the substantial advantages of learning these delays along with synaptic weights,…

Deep Brain Stimulation (DBS) in the Globus Pallidus internus (GPi) is a therapeutic approach for various neurological disorders. Its effects on decision-making remain complex and not fully understood. The presented study uses a neuro-computational model of the basal ganglia to investigate how DBS influences…

Analog neuromorphic circuits emulate the dynamic properties of biological neural systems through their physics [1], sharing similarities and constraints with them [2,3]. This alignment can be leveraged for fundamental research to validate or challenge theories of neural computation as well as for applied research to…

Neuronal cultures and human stem-cell-derived organoids are fundamental building blocks of neuroscientific research and future personalized medicine. However, in-vitro networks show considerably more synchronous bursting activity than networks in-vivo [1]. One leading theory proposes that under a lack of external…