Giacomo Indiveri

Last Name

Indiveri

First Name

Giacomo

ORCID

0000-0002-7109-1689

Organisational unit

09699 - Indiveri, Giacomo / Indiveri, Giacomo

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Publications1 - 10 of 225

Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection
Bartels, Jim; Gallou, Olympia; Ito, Hiroyuki; et al. (2025)
Scientific Reports
Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-lasting operation times using current technologies is still an open challenge. Brain-inspired spiking neural networks (SNNs) represent a promising signal processing and computing framework as they can be deployed on ultra-low power neuromorphic computing systems, for this purpose. Here, we introduce a novel SNN architecture, co-designed and validated on a mixed-signal neuromorphic chip, that shows potential for always-on monitoring of epileptic activity. We demonstrate how the hardware implementation of this SNN captures the phenomenon of partial synchronization within neural activity during seizure periods. We assess the network using a full-custom asynchronous mixed-signal neuromorphic platform, processing analog signals in real-time from an Electroencephalographic (EEG) seizure dataset. The neuromorphic chip comprises an analog front-end (AFE) signal conditioning stage and an asynchronous delta modulation (ADM) circuit directly integrated on the same die, which can produce the stream of spikes as input to the SNN, directly from the analog EEG signals. We show a linear classifier in a post processing stage that is sufficient to reliably classify and detect seizures, from the local features extracted by the SNN, indicating the feasibility of full on-chip seizure monitoring in the future. This research marks a significant advancement toward developing embedded intelligent “wear and forget” units for resource-constrained environments. These units could autonomously detect and log relevant EEG events of interest in out-of-hospital environments, offering new possibilities for patient care and management of neurological disorders.
Electrically Tunable Graphene Organic Hybrid Ring Resonators
Ma, Ping; Zhang, X.Z.; Heni, Wolfgang; et al. (2021)
2021 IEEE Photonics Conference (IPC)
ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales
Frenkel, Charlotte; Indiveri, Giacomo (2022)
Digest of Technical Papers / IEEE International Solid State Circuits Conference ~ 2022 IEEE International Solid- State Circuits Conference (ISSCC)
The robustness of autonomous inference-only devices deployed in the real world is limited by data distribution changes induced by different users, environments, and task requirements. This challenge calls for the development of edge devices with an always-on adaptation to their target ecosystems. However, the memory requirements of conventional neural-network training algorithms scale with the temporal depth of the data being processed, which is not compatible with the constrained power and area budgets at the edge. For this reason, previous works demonstrating end-to-end on-chip learning without external memory were restricted to the processing of static data such as images [1]–[4], or to instantaneous decisions involving no memory of the past, e.g. obstacle avoidance in mobile robots [5]. The ability to learn short-to-long-term temporal dependencies on-chip is a missing enabler for robust autonomous edge devices in applications such as gesture recognition, speech processing, and cognitive robotics.
Embedded neuromorphic vision for humanoid robots
Bartolozzi, Chiara; Rea, Francesco; Clercq, Charles; et al. (2011)
Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011
Focus on benchmarks for neuromorphic computing
Stewart, Terrence C.; Schuman, Catherine; Sandamirskaya, Yulia; et al. (2024)
Neuromorphic Computing and Engineering
Silicon neuron with programmable ion channel kinematics for bioelectronic applications
Donati, Elisa; Indiveri, Giacomo (2021)
2021 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Bioelectronic medicine is driving the need to design low-power circuits for interfacing biological neurons to electronic neural processing systems, and for implementing real-time close-loop interactions with the biological tissue. This interaction would benefit from congruent features between the biological and artificial systems, such as their working frequency and temporal dynamics. Neuromorphic engineering provides design solutions for building circuits capable of emulating biological neural processing systems faithfully. However, very few, albeit notable, attempts have been made so far to provide accurate models of action potential generation mechanisms with time-constants and dynamics that resemble those of real neurons. This paper presents a design of a silicon neuron, based on a generalized Hodgkin-Huxley model with programmable slopes for each ion channel model, that provide a robust method for matching accurately the silicon neural dynamics to those of target neuron types in biological systems. The parameters of the ion channel dynamics are controlled by a circuit comprising multiple Differential Pairs. This can be used to shape the membrane voltage profile of the silicon neuron. We use this feature to emulate biological neurons involved in respiratory Central Pattern Generator responsible for the stimulation of the vagus nerve for the activation of the heart chamber pacing. The novelty introduced in our approach is to provide a step further toward the development of a silicon neuron able to reproduce the response of biological cells and to interact with them in real-time, with the aim to design low power Brain-Machine-Interface.
How heterogeneity shapes dynamics and computation in the brain
Dahmen, David; Hutt, Axel; Indiveri, Giacomo; et al. (2026)
Neuron
Much effort has been spent clustering neurons into transcriptomic or functional cell types and characterizing the differences between them. Beyond subdividing neurons into categories, we must recognize that no two neurons are identical and that graded physiological or transcriptomic properties exist within cells of a given type. This often overlooked “within-type” heterogeneity is a specific neuronal implementation of what statistical physics refers to as “disorder” and exhibits rich computational properties, the identification of which may shed crucial insights into theories of brain function. In this perspective article, we address this gap by highlighting theoretical frameworks for the study of neural tissue heterogeneity and discussing the benefits and implications of within-type heterogeneity for neural network dynamics, computation, and self-organization.
Analog Weight Updates with Compliance Current Modulation of Binary ReRAMs for On-Chip Learning
Payvand, Melika; Demirag, Yigit; Dalgaty, Thomas; et al. (2020)
2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Many edge computing and IoT applications require adaptive and on-line learning architectures for fast and low-power processing of locally sensed signals. A promising class of architectures to solve this problem is that of in-memory computing ones, based on event-based hybrid memristive-CMOS devices. In this work, we present an example of such systems that supports always-on on-line learning. To overcome the problems of variability and limited resolution of ReRAM memristive devices used to store synaptic weights, we propose to use only their High Conductive State (HCS) and control their desired conductance by modulating their programming Compliance Current (I CC ). We describe the spike-based learning CMOS circuits that are used to modulate the synaptic weights and demonstrate the relationship between the synaptic weight, the device conductance, and the I CC used to set its weight, with experimental measurements from a 4kb array of HfO 2 -based devices. To validate the approach and the circuits presented, we present circuit simulation results for a standard CMOS 180nm process and system-level behavioral simulations for classifying hand-written digits from the MNIST data-set with classification accuracy of 92.68% on the test set.
A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems
Chicca, Elisabetta; Indiveri, Giacomo (2020)
Applied Physics Letters
Neuromorphic visual scene understanding with resonator networks
Renner, Alpha; Supic, Lazar; Danielescu, Andreea; et al. (2024)
Nature Machine Intelligence
Analysing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one major problem is the computational challenge of the inference procedure, involving a combinatorial search across object identities and poses. Here we propose a neuromorphic solution exploiting three key concepts: (1) a computational framework based on vector symbolic architectures (VSAs) with complex-valued vectors, (2) the design of hierarchical resonator networks to factorize the non-commutative transforms translation and rotation in visual scenes and (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued resonator networks on neuromorphic hardware. The VSA framework uses vector binding operations to form a generative image model in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which can then be efficiently factorized by a resonator network to infer objects and their poses. The hierarchical resonator network features a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. Our approach is demonstrated on synthetic scenes composed of simple two-dimensional shapes undergoing rigid geometric transformations and colour changes. A companion paper demonstrates the same approach in real-world application scenarios for machine vision and robotics.

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Giacomo Indiveri

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