Giovanni Orani
Implementation of a Hippocampal-Cortical spiking neural network for memory semantization on Loihi 2 neuromorphic hardware.
Rel. Gianvito Urgese, Walter Gallego Gomez, Vittorio Fra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2026
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Abstract
Neuromorphic computing is emerging as an important frontier of Artificial Intelligence (AI), helping overcome the energy costs and latency limitations of traditional Deep Neural Networks (DNNs) by drawing inspiration from biological neural circuits. In this context, Spiking Neural Networks (SNNs) employ biologically inspired neuron models that span different levels of abstraction, ranging from highly detailed but computationally demanding formulations such as the Hodgkin–Huxley model to simplified and more efficient approximations such as the Leaky Integrate-and-Fire (LIF) model. SNNs encode information through sparse spike events and temporal dynamics, enabling efficient processing of sparse data and naturally supporting temporal coding. This thesis addresses the challenge of bridging the gap between computational neuroscience and specialized neuromorphic hardware.
Specifically, the work focuses on adapting and refactoring a complex SNN model, named Hippocampal-Cortico Spiking Neural Network (HiCo SNN) to make it compatible with Intel’s neuromorphic hardware Loihi 2
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