Serrano Gotarredona, María TeresaLinares Barranco, Alejandro2025-01-142025-01-142024-10-25Ahmadi Farsani, J. (2024). Developing digital and analog spiking neural networks for interacting with biological tissues in the case study of temporal lobe epilepsy. (Tesis Doctoral Inédita). Universidad de Sevilla, Sevilla.https://hdl.handle.net/11441/166544The HERMES (Hybrid Enhanced Regenerative Medicine Systems) project aims to pioneer an innovative approach to treating temporal lobe epilepsy by leveraging the symbiotic integration of tissue engineering, neuromorphic engineering, and artificial intelligence (AI). Within this project, my role focused on the neuromorphic engineering aspect, specifically designing a spiking neural network (SNN) using CMOS integrated circuits (ICs) with memristors to demonstrate its potential for pattern recognition tasks. To facilitate the implementation of the SNN, memristors were chosen as synaptic components due to their emerging properties and potential benefits in memory calculation applications. A collaborative effort with researchers from Politecnico di Milano (POLIMI) provided access to memristor chips, enabling the incorporation of these devices into the SNN design. Additionally, collaboration with biologists from the Italian Institute of Technology (IIT) enriched the project by providing access to a microelectrode array (MEA) system capable of recording local field potentials from brain tissues, particularly, rodent brain slices. This real-time interaction with brain tissues through the MEA system presented opportunities to validate the functionality of the SNN in a physiological context. The implementation of the SNN began with simulation and modeling on a Digital Signal Processor (DSP) platform, specifically the DSK6455 board, which replicated the functionality of the DSP embedded within the MEA system. This initial validation step ensured the viability of the SNN design before proceeding to the costly and time-intensive process of ASIC chip design. Experimental results demonstrated the capability of the SNN to learn and adapt using Spike-Timing-Dependent Plasticity (STDP) algorithms, laying the foundation for further hybrid experiments conducted with the IIT team. Subsequent stages of the project involved the design and fabrication of ASIC chips and PCBs to create a fully custom hardware SNN, culminating in pattern recognition experiments conducted using FPGA boards controlling the ASICs and memristor chips. As a result of the thesis, the DSP was employed for modelling, evaluation, and testing of the SNN and its constituent building blocks, intended for eventual design on an ASIC. Additionally, using the DSP, a closed-loop real-time biohybrid in vitro system was developed to interact with an actual rodent brain slice for seizure detection. The designs initially implemented on the DSP were transitioned to ASIC utilizing a 180nm technology. In the pre-synaptic part, a compact field potential to spike converter was engineered, capable of extracting spikes from overshoots, undershoots, or both. Meanwhile, in the post-synaptic part, a current-attenuator was employed to diminish current prior to its input into the neuron circuit, resulting in a significantly compact neuron circuit design. Experimental findings indicated that the SNN shows notable power efficiency when compared with state of the arts.application/pdf140 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccess