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DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays
(2023)arXivAn increasing number of neuroscience studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the brain for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection, using coincidence detection (CD) mechanisms, made possible by the presence of synaptic delays that align temporally ...Working Paper -
A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors
(2023)Research SquareBio-signal sensing represents a pivotal domain in the medical applications of bioelectronics. Traditional methods have, so far, focused on capturing these signals as accurately as possible, leading to high sampling rates in clocked synchronous architectures. Given the sparse activity of bio-signals, this approach often results in large amounts of digitized data with no relevant information and in a significant amount of energy consumed ...Working Paper -
Orchestrated excitatory and inhibitory plasticity produces stable dynamics in heterogeneous neuromorphic computing systems
(2023)Research SquareMany neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brain's computational primitives, including self-sustained neural activity. However, achieving the same robustness of biological networks in ...Working Paper -
Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware
(2023)Research SquareMixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing the dynamics of biological neural systems in real-time with bio-physically realistic dynamics. However, similar to their biological counterparts, these circuits have limited resolution and are affected by a high degree of variability. Considering this, we developed a recurrent spiking neural network that implements a faithful model of the retinocortical ...Working Paper -
Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework
(2023)Research SquareInterictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and de-correlated temporal signal encoding. ...Working Paper -
A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG
(2021)Research SquareBackground: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal ...Working Paper -
Modelling novelty detection in the thalamocortical loop
(2021)bioRxivIn complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch ...Working Paper -
MEMSORN: Self-organization of an inhomogeneous memristive hardware for sequence learning
(2021)Research SquareLearning is a fundamental component for creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time-scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in ...Working Paper -
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
(2021)arXivSpiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network ...Working Paper -
The neuromorphic Mosaic: re-configurable in-memory small-world graphs
(2021)Research SquareThanks to their non-volatile and multi-bit properties, memristors have been extensively used as synaptic weight elements in neuromorphic architectures. However, their use to define and re-program the network connectivity has been overlooked. Here, we propose, implement and experimentally demonstrate Mosaic, a neuromorphic architecture based on a systolic array of memristor crossbars. For the first time, we use distributed non-volatile ...Working Paper