47 citations · 147 across the 15 of their papers we have counts for
17 papers
EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks
Xi Chen, Siwei Mai, Konstantinos Michmizos
A vast majority of spiking neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and…
BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks
Guangzhi Tang, Neelesh Kumar, Ioannis Polykretis +1
Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic chips. To harn…
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity
Vladimir A. Ivanov, Konstantinos P. Michmizos
The liquid state machine (LSM) combines low training complexity and biological plausibility, which has made it an attractive machine learning framework for edge and neuromorphic co…
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Guangzhi Tang, Neelesh Kumar, Raymond Yoo +1
The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, wh…
An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi
Ioannis Polykretis, Konstantinos P. Michmizos
Locomotion is a crucial challenge for legged robots that is addressed "effortlessly" by biological networks abundant in nature, named central pattern generators (CPG). The multitud…
Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware
Guangzhi Tang, Neelesh Kumar, Konstantinos P. Michmizos
Energy-efficient mapless navigation is crucial for mobile robots as they explore unknown environments with limited on-board resources. Although the recent deep reinforcement learni…
Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots
Neelesh Kumar, Konstantinos P. Michmizos
Although cognitive engagement (CE) is crucial for motor learning, it remains underutilized in rehabilitation robots, partly because its assessment currently relies on subjective an…
Deep Learning of Movement Intent and Reaction Time for EEG-informed Adaptation of Rehabilitation Robots
Neelesh Kumar, Konstantinos P. Michmizos
Mounting evidence suggests that adaptation is a crucial mechanism for rehabilitation robots in promoting motor learning. Yet, it is commonly based on robot-derived movement kinemat…
A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head
Praveenram Balachandar, Konstantinos P. Michmizos
Robotic vision introduces requirements for real-time processing of fast-varying, noisy information in a continuously changing environment. In a real-world environment, convenient a…
Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
Guangzhi Tang, Ioannis E. Polykretis, Vladimir A. Ivanov +2
While there is still a lot to learn about astrocytes and their neuromodulatory role in the spatial and temporal integration of neuronal activity, their introduction to neuromorphic…
Axonal Conduction Velocity Impacts Neuronal Network Oscillations
Vladimir A. Ivanov, Ioannis E. Polykretis, Konstantinos P. Michmizos
Increasing experimental evidence suggests that axonal action potential conduction velocity is a highly adaptive parameter in the adult central nervous system. Yet, the effects of t…
Computational Astrocyence: Astrocytes encode inhibitory activity into the frequency and spatial extent of their calcium elevations
Ioannis E. Polykretis, Vladimir A. Ivanov, Konstantinos P. Michmizos
Deciphering the complex interactions between neurotransmission and astrocytic elevations is a target promising a comprehensive understanding of brain function. While the…
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM
Guangzhi Tang, Arpit Shah, Konstantinos P. Michmizos
Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of speciali…
A Neural-Astrocytic Network Architecture: Astrocytic calcium waves modulate synchronous neuronal activity
Ioannis Polykretis, Vladimir Ivanov, Konstantinos P. Michmizos
Understanding the role of astrocytes in brain computation is a nascent challenge, promising immense rewards, in terms of new neurobiological knowledge that can be translated into a…
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
Guangzhi Tang, Konstantinos P. Michmizos
It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for…
Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band
Sheraz Khan, Javeria Hashmi, Fahimeh Mamashli +14
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythm…
Sequence learning in Associative Neuronal-Astrocytic Network
Leo Kozachkov, Konstantinos P. Michmizos
The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how suc…