Giacomo Indiveri

This paper demonstrates a learning rule for neuromorphic systems that balances excitation and inhibition, enabling robust self-sustained dynamics despite device variability.

This paper introduces a spiking neural network designed for real-time epileptic seizure detection using a neuromorphic chip, demonstrating its feasibility for on-chip monitoring.

This paper presents a neuromorphic electronic circuit architecture inspired by cortical microcircuits, demonstrating robust learning and classification with low power consumption and resilience to variability.

The paper presents brain-inspired strategies to improve reliability and robustness in neuromorphic systems by leveraging biological constraints and neural coding strategies.

This paper identifies low-dimensional 'operative dimensions' in RNNs that significantly influence dynamics, showing that much of the high-dimensional connectivity is functionally irrelevant.

This paper develops a neuromorphic model of olfactory processing in Drosophila larvae, showing how sparse coding and efficient encoding can be achieved through feedback inhibition and spike frequency adaptation in a small neural network.

The paper develops a thalamocortical network model to explain novelty detection through synaptic depression in cortical layer 6 and thalamic oscillations, showing similarity to EEG-based mismatch negativity.

This paper presents a feasibility study for real-time classification of extra-cellular neural recordings using low-power VLSI systems and spike-based neuromorphic circuits.

The paper presents a computational model of columnar visual cortex that explains surround suppression and response properties through competitive dynamics involving local and long-range connections.