Mehmet S. Ozdas

ORCID: 0000-0001-7769-0773
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About
Contact & Profiles
Research Areas
  • Ultrasound and Hyperthermia Applications
  • Neuroscience and Neural Engineering
  • Neural dynamics and brain function
  • Advanced Memory and Neural Computing
  • Lanthanide and Transition Metal Complexes
  • Electron Spin Resonance Studies
  • Ultrasound Imaging and Elastography
  • Pain Management and Treatment
  • Photoacoustic and Ultrasonic Imaging
  • Ultrasound and Cavitation Phenomena
  • Nanoparticle-Based Drug Delivery

University of Zurich
2015-2021

ETH Zurich
2020-2021

SIB Swiss Institute of Bioinformatics
2020

Abstract Non-invasive, molecularly-specific, focal modulation of brain circuits with low off-target effects can lead to breakthroughs in treatments disorders. We systemically inject engineered ultrasound-controllable drug carriers and subsequently apply a novel two-component Aggregation Uncaging Focused Ultrasound Sequence (AU-FUS) at the desired targets inside brain. The first sequence aggregates millimeter-precision by orders magnitude. second uncages carrier’s cargo locally achieve high...

10.1038/s41467-020-18059-7 article EN cc-by Nature Communications 2020-10-01

Hardware implementations of spiking neural networks offer promising solutions for computational tasks that require compact and low-power computing technologies. As these depend on both the specific network architecture type learning algorithm used, it is important to develop devices possibility reconfigure their topology implement different types mechanisms. Here we present a neuromorphic multi-neuron VLSI device with on-chip programmable event-based hybrid analog/digital circuits; nature...

10.1145/2658998 article EN ACM Journal on Emerging Technologies in Computing Systems 2015-09-02

Abstract On-line classification of neural recordings can be extremely useful in brain-machine interface, prosthetic applications or therapeutic intervention. In this work we present a feasibility study for developing compact low-power VLSI systems able to classify real-time, using spike-based neuromorphic circuits. We developed framework classifying extra-cellular made rat auditory cortex response different stimuli and porting the algorithm onto spiking multi-neuron chip with programmable...

10.1101/2021.07.18.452831 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2021-07-19
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