Izhikevich neural model and STDP learning algorithm mapping on spiking neural network hardware emulator

From the 20th century, biological mechanisms of the brain behaviour have become more and more interesting for the research communities in information fields due to the computational power of the systems they inspire. In fact, despite the lack of consensus about the information processing actually involved in brain, biological processes have served as reference for recent computational models. The first Artificial Neural Networks (ANNs) were developed as simplified versions of biological neural networks in terms of structure and function. Today, after a long evolutionary history, the third generation of artificial network is the Spiking one (SNNs), which reach a more realistic modeling by utilizing true biological features like spikes to transmit information between neurons, incorporating the concepts of space and time through neural connectivity and plasticity rules for the evolution of the strength of the connections. The proposal of this thesis is to embed the izhichevic neuron model and a full custom "Spike timing dependent plasticity" (STDP) learning algorithm in an architecture called HEENS (Hardware Emulator of Evolved Neural System). HEENS is a multi-chip structure developed at the "Universitat Politècnica de Catalunya" (UPC) based on a ring link topology connecting several SIMD processors reproducing each one a group of neuron of a Spiking neural network. The Izhichevic neuron model is a worldwide adopted mathematical model for reproducing the neural membrane potential evolution, observed in some mammalian cortex, along time and according to external stimuli. STDP is a biological learning algorithm which shapes the strength of a synaptic connection according to the timing with which that connection takes part to the overall spiking activity of the post or pre-synaptic neurons. This master thesis project, in particular, acts at algorithm level and at instruction level as well at architectural level, analysing the mathematical models for the right data parallelism, writing the assembly code describing the routine common to all the neurons of the implemented Spiking neural network (SNN), modifying the instruction set and the existing hardware of the HEENS architecture in order to fulfil the biological model needs from a computational and performance point of view. HEENS architecture is described in VHDL code, its set-up operations ( assembler for code translation, generation of memories, Network configuration) are performed by Python scripts, the comparison between the actual behavior of HEENS to that of the mathematical models is instead performed via MatLAB scripts. The latter allow: to imitate the performances of a special purpose hardware; to generate source files in order to synchronize and align the model and the architecture even with the randomization of several neural parameters; to make some design choices; to verify and to show the results. In the following chapters: first, the basic concepts of the neural networks are reviewed and the HEENS architecture is briefly detailed for what is of close interest to this text; Chapter 2 and Chapter 3 deal with the development of the two algorithms in HEENS. Finally, in Chapter 4 conclusions and future work are presented.