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Giacomo Indiveri

Has grant

FUll Professor

ETH Zürich

Switzerland

Research Interests

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Giacomo Indiveri is an Associate Professor at the University of Zurich and ETH Zurich in Switzerland. His research focuses on neuromorphic computing, exploring areas such as efficient learning in artificial agents, analog/digital architectures, and robust early-vision processing. Recent publications highlight his work on dynamic closed-loop models and hardware emulation in neuromorphic systems.

Recent Grants

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Neuromorphic processors: event-based VLSI models of cortical circuits for brain-inspired computation

Open Date: 2011-03-01

Close Date: 2017-02-28

Articles (27)

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Collaborators (34)

Huaqiang Wu

Professor

Tsinghua University

CHINA

Giacomo Indiveri

Research Interests

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About

Recent Grants

Articles (27)

A neuromodulable current-mode silicon neuron for robust and adaptive neuromorphic systems

How heterogeneity shapes dynamics and computation in the brain

Detecting high‐frequency oscillations in real time during epilepsy surgery with neuromorphic hardware validated to predict postoperative seizure outcome

Bioinspired spiking architecture enables energy constrained touch encoding

Real-time chirp-based seizure detection in human iEEG with neuromorphic hardware

Efficient training of neuromorphic electronics

Spiking neural network decoders of finger forces from high-density intramuscular microelectrode arrays

A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures

Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware

Neuromorphic dreaming as a pathway to efficient learning in artificial agents

Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity

Analog Implementation of a Spiking System for Performing Arithmetic Logic Operations on Mixed‐Signal Neuromorphic Processors

Distributed representations enable robust multi-timescale symbolic computation in neuromorphic hardware

Towards a Dynamic Closed-Loop Neuromorphic Trajectory Interpolation Model: Hardware Emulation and Software Simulation

Neuromorphic visual scene understanding with resonator networks

Visual odometry with neuromorphic resonator networks

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors

Neuromorphic computing and engineering’s coming of age

Author Correction: DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

A CMOS-compatible oscillation-based VO2 Ising machine solver

DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor

Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems

Spike-based computational primitives to reproduce neural dynamics in the parietal cortex during motor preparation

Focus on benchmarks for neuromorphic computing

Bottom-Up and Top-Down Approaches for the Design of Neuromorphic Processing Systems: Tradeoffs and Synergies Between Natural and Artificial Intelligence

Collaborators (34)

Huaqiang Wu

Angel Francisco Jiménez Fernández

Maeike Zijlmans

Pedro L. Galindo

Aida Todri-Sanial

Silvio P. Sabatini

Rishikesh Narayanan

Rodolphe Sepulchre

Fernando Pérez-Peña

Jérémie Lefebvre

Fernando Manuel Quintana Velazquez

Patrizia Fattori

Alberto Mazzoni

Jean-Michel Redoute

Johannes Sarnthein

Gabriel Jimenez-Moreno

Catherine Schuman

Alessio Franci

Ann Kennedy

Erik Aarnoutse

Luca Mazzucato

David Bol

Georgia Ramantani

Dario Farina

Bernabé Linares-Barranco

Ole Richter

Melika Payvand

Friedrich T. Sommer

Adilson E. Motter

Elisa Donati

Juan Pedro Dominguez-Morales

Chenxi Wen

Andreas Hierlemann

Daniel Casanueva Morato

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