A5011585098- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Memory and Neural Mechanisms
- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Neuroscience and Neuropharmacology Research
- Ferroelectric and Negative Capacitance Devices
- Neural and Behavioral Psychology Studies
- Modular Robots and Swarm Intelligence
- Neural Networks and Reservoir Computing
- Sleep and Wakefulness Research
- Reinforcement Learning in Robotics
- Neurotransmitter Receptor Influence on Behavior
- Neural Networks and Applications
- CCD and CMOS Imaging Sensors
- Photoreceptor and optogenetics research
- Functional Brain Connectivity Studies
- Robotics and Automated Systems
- Cell Image Analysis Techniques
- Visual perception and processing mechanisms
- Robotic Path Planning Algorithms
- Receptor Mechanisms and Signaling
- Zebrafish Biomedical Research Applications
- Robotics and Sensor-Based Localization
- Action Observation and Synchronization
University of California, Irvine
2015-2024
Institute for Cognitive Science Studies
2022
University of California System
2021
Neurosciences Institute
2001-2008
John Jay College of Criminal Justice
2002-2008
Saarland University
2007
Carnegie Mellon University
2007
Indiana University Bloomington
2007
University of Coimbra
2007
University of Sussex
2007
Neuromorphic hardware implements biological neurons and synapses to execute a spiking neural network (SNN)-based machine learning. We present SpiNeMap, design methodology map SNNs crossbar-based neuromorphic hardware, minimizing spike latency energy consumption. SpiNeMap operates in two steps: SpiNeCluster SpiNePlacer. is heuristic-based clustering technique partition an SNN into clusters of synapses, where intracluster local are mapped within crossbars the intercluster global shared...
Large-scale spiking neural network (SNN) simulations are challenging to implement, due the memory and computation required iteratively process large set of state dynamics updates. To meet these challenges, we have developed CARLsim 4, a user-friendly SNN library written in C++ that can simulate biologically detailed networks. Improving on efficiency scalability earlier releases, present release allows for simulation using multiple GPUs CPU cores concurrently heterogeneous computing cluster....
Biological organisms have the ability to respond quickly an ever-changing world. Because this adaptability is so critical for survival, all vertebrates sub-cortical structures, which comprise neuromodulatory systems, regulate fundamental behavior and drive decision making in response environmental events. In vertebrate, there are separate neuromodulators that threats, reward anticipation, novelty, attentional effort. However, each of these systems has a similar effect, is, cause organism be...
The vertebrate neuromodulatory systems are critical for appropriate value-laden responses to environmental challenges. Whereas changes in the overall level of dopamine have an effect on organism's reward or curiosity seeking behavior, serotonin can affect its anxiety harm aversion. Moreover, top-down signals from frontal cortex exert cognitive control these systems. cholinergic and noradrenergic ability filter out noise irrelevant events. We introduce a neural network action selection that...
As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex circuits that explore various phenomena such as plasticity, vision systems, auditory oscillations, and many other important topics function. Additionally, are particularly well-adapted run on neuromorphic hardware will support biological brain-scale architectures. Although...
We present PyCARL, a PyNN-based common Python programming interface for hardware-software cosimulation of spiking neural network (SNN). Through we make the following two key contributions. First, provide an PyNN to CARLsim, computationally- efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development machine learning models code sharing between CARLsim users, promoting integrated larger neuromorphic community. Second, integrate cycle-accurate...
Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These energy efficient executing Spiking Neural Networks (SNNs). We observe that long bitlines wordlines a memristive major source of parasitic voltage drops, which create current asymmetry. Through circuit simulations, we show the significant endurance variation results from this Therefore, if critical (ones with lower endurance) overutilized, they...
Mature simulation systems for Spiking Neural Networks (SNNs) become more relevant than ever understanding the brain and supporting neuromorphic computing. The CARL-sim SNN platform is one of first Open Source that utilized CUDA GPUs to address tremendous parallel processing demands natural brains. It has evolved over almost a decade in numerous scientific research projects requiring efficient biologically plausible modeling at scale. With its sixth major release, CARLsim 6 respects this...
In studying brain activity during the behavior of living animals, it is not possible simultaneously to analyze all levels control from molecular events motor responses. To provide insights into how interact, we have carried out synthetic neural modeling using a brain-based real-world device. We describe here design and performance such device, designated Darwin VII, which guided by computer-simulated analogues cortical subcortical structures. All VII's architecture can be examined as device...
Analyzing neural dynamics underlying complex behavior is a major challenge in systems neurobiology. To meet this through computational neuroscience, we have constructed brain-based device (Darwin X) that interacts with real environment, and whose guided by simulated nervous system incorporating detailed aspects of the anatomy physiology hippocampus its surrounding regions. Darwin X integrates cues from environment to solve spatial memory task. Place-specific units, similar place cells...
Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and engineering applications. Spiking neural (SNN) have been traditionally simulated on large-scale clusters, super-computers, or dedicated hardware architectures. Alternatively, graphics processing units (GPUs) can provide a low-cost, programmable, high-performance computing platform simulation SNNs. In this paper we demonstrate an efficient, Izhikevich neuron based SNN...