Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems
D Zendrikov, S Solinas, G Indiveri - … Computing and Engineering, 2023 - iopscience.iop.org
Neuromorphic processing systems implementing spiking neural networks with mixed signal
analog/digital electronic circuits and/or memristive devices represent a promising …
analog/digital electronic circuits and/or memristive devices represent a promising …
DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
An increasing number of studies are highlighting the importance of spatial dendritic
branching in pyramidal neurons in the neocortex for supporting non-linear computation …
branching in pyramidal neurons in the neocortex for supporting non-linear computation …
Fault-tolerant spiking neural network mapping algorithm and architecture to 3D-NoC-based neuromorphic systems
Neuromorphic computing uses spiking neuron network models to solve machine learning
problems in a more energy-efficient way when compared to conventional artificial neural …
problems in a more energy-efficient way when compared to conventional artificial neural …
R-MaS3N: Robust Mapping of Spiking Neural Networks to 3D-NoC-Based Neuromorphic Systems for Enhanced Reliability
Neuromorphic computing utilizes spiking neural networks (SNNs) to offer power/energy-
efficient solutions for complex machine-learning problems in hardware. However, neural …
efficient solutions for complex machine-learning problems in hardware. However, neural …
Establishing brain states in neuroimaging data
The definition of a brain state remains elusive, with varying interpretations across different
sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of …
sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of …
Predictive Coding with Spiking Neural Networks: a Survey
In this article, we review a class of neuro-mimetic computational models that we place under
the label of spiking predictive coding. Specifically, we review the general framework of …
the label of spiking predictive coding. Specifically, we review the general framework of …
Efficient coding with chaotic neural networks: A journey from neuroscience to physics and back
J Kadmon - arXiv preprint arXiv:2408.01949, 2024 - arxiv.org
This essay, derived from a lecture at" The Physics Modeling of Thought" workshop in Berlin
in winter 2023, explores the mutually beneficial relationship between theoretical …
in winter 2023, explores the mutually beneficial relationship between theoretical …
Signatures of criticality in efficient coding networks
The critical brain hypothesis states that the brain can benefit from operating close to a
second-order phase transition. While it has been shown that several computational aspects …
second-order phase transition. While it has been shown that several computational aspects …
Fault Recovery in Spiking Neural Networks Through Target and Selection of Faulty Neurons for 3D Spiking Neuromorphic Processors
Neuromorphic systems integrate features inspired by the neurobiological system which
makes them suitable for many cognitive applications like facial recognition, autonomous …
makes them suitable for many cognitive applications like facial recognition, autonomous …
Principles of Robust Neural Computation Through the Lens of Analog Neuromorphic Hardware
D Zendrikov - 2024 - zora.uzh.ch
Neuromorphic electronics provide a computational substrate that natively supports spiking
neural networks through device physics, making them strong competitors for low-power …
neural networks through device physics, making them strong competitors for low-power …