There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with the original model. Accordingly, a compact structure of the models which can be trained with supervised and unsupervised learning algorithms has been developed. Using this structure and based on a spike rate coding, a character recognition case study has been implemented and tested.

This paper proposes a neuromorphic platform for on-FPGA online spike timing dependant plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components. First, the Izhikevich neuron model is modified for implementation using the CORDIC algorithm, simulated to ensure the model accuracy, described as hardware, and implemented on FPGA. Second, the STDP learning algorithm is adapted and optimized using the CORDIC method, synthesized for hardware, and implemented to perform on-FPGA online learning on a network of CORDIC Izhikevich neurons to demonstrate competitive Hebbian learning. The implementation results are compared with the original model and state-of-the-art to verify accuracy, effectiveness, and higher speed of the system. These comparisons confirm that the proposed neuromorphic system offers better performance and higher accuracy while being straightforward to implement and suitable to scale.

High-accuracy implementation of biological neural networks is a computationally expensive task, specially, for large-scale simulations of neuromorphic algorithms. This paper proposes a set of models for biological spiking neurons, which are efficiently implementable on digital platforms. Proposed models can reproduce different biological behaviors with a high precision. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting low-cost hardware implementation. Hardware synthesis and physical implementations on a field-programmable gate array show that the proposed models can produce biological behavior of different types of neurons with higher performance and considerably lower implementation costs compared with the original model.

This paper presents a COordinate Rotation DIgital Computer (CORDIC) based Adaptive Exponential Integrate and Fire (AdEx) neuron for efficient large scale biological neural network implementation. The accuracy of the modified model is investigated by both calculating various errors and bifurcation analysis; both show that the proposed model follows the same signaling, dynamical behavior, and bifurcation pattern as the original model. Network behavior of the original and proposed models are also observed to be very much alike in following the same activity patterns. The model is hardware synthesized and implemented on FPGA as a proof of concept. Measurement results show that the proposed model can reproduce neuronal behaviors similar to the original model. Hardware device utilization and speed also confirm the efficiency of the realized hardware compared with previous works.