The Cochlea, a spiral-shaped structure in the inner ear, plays a crucial role in the process of hearing by converting sound waves into electrical signals that the brain can interpret. This study introduces a cost-effective adaptation of 2D artificial Cochlea mathematical modeling using a planar approximation technique. The main novelty and contribution of our work is a method employs surface-based functions and is known as the Surface-Based Approximation Model of Cochlea (SBAMoC). By simplifying complex multiplication processes in nonlinear components, the SBAMoC reduces costs and enhances efficiency, making it suitable for FPGA implementation with minimal hardware requirements. The proposed model is evaluated in scenarios involving two-coupled oscillations and grid-based cochlear networks to better understand its performance. Through hardware synthesis on a Virtex-II board, the SBAMoC demonstrates improved efficiency and reduced computational expenses compared to the original model, achieving faster speeds and greater cost-effectiveness. In practical tests, the SBAMoC exhibits higher operational speeds and increased scalability, outperforming the original model by replicating accurate cochlear behaviors with minimal deviations. Specifically, the single SBAMoC implementation in our model achieves a speed boost of approximately 1.333 times compared to the original model (381.292 MHz vs. 286.029 MHz) and supports a greater number of fitted SBAMoCs (75 vs. 35), showcasing its superior efficiency and performance enhancements. In case of real-world applications, it can be considered for the development of more efficient and cost-effective cochlear implants, leading to improved hearing restoration solutions for individuals with hearing impairments. Also, the findings from this study could also be leveraged to enhance the design and implementation of signal processing systems in various audio and communication devices, paving the way for advanced audio processing technologies with increased efficiency and reduced hardware costs.

The Central Nervous System (CNS) acts as the main element of the biological system, regulating and commanding numerous organs in the human body. Neurons play a crucial role in the central nervous system, and it is necessary to thoroughly examine, replicate, simulate, and integrate various aspects of the CNS to develop a comprehensive neuronal system that can mimic the actual nervous system. In this research, a neuron model called the Quartic Neuron Model is employed to imitate the fundamental nervous functions of the human brain. The proposed method, known as Digital-QNM (D-QNM) is accomplished by employing power-2 based approximation and linear approaches to modify the fourth-degree function. These power-2 based functions are digital-friendly terms (high-accurate, low-cost and leads to high-frequency implementation). By eliminating the high-cost function, the presented model offers advantages such as low error, high speed, and efficient resource utilization compared to the basic main state. In order to validate the final hardware design, a digital FPGA board (specifically, the Xilinx Virtex-5 FPGA board) is employed. The process of digitally synthesizing the hardware demonstrates that our proposed approach can replicate the QNM with improved frequency, performance, and reduced hardware costs. The implementation outcomes show a significant reduction of 98% in FPGA resources cost and a higher operating frequency of the suggested model, reaching 190 MHz. This frequency is considerably higher than the original model’s 105 MHz.

Biological systems in case of real-time state and also large-scale simulation approach are interesting and challenge-based due to different aspects of nonlinear mathematical modeling that can describe the interactions of biological blocks. Thus, hardware circuit designing of these basic blocks in the Central Nervous System (CNS) can be an important field in case of achieving high performance neuromorphic system emulator. This paper presents a high-speed, low-cost, and efficient digital circuit for emulating the plausible calcium-dynamic-based model of astrocyte which has spontaneous oscillations. The nonlinear high-cost functions of the complex astrocyte model are reformulated using the power-2 based low-cost terms using optimized exhaustive search algorithm. Subsequently, the proposed model is simulated in case of validating the presented model and new optimized functions. Finally, the proposed model is physically realized in hardware case using Virtex 4 FPGA platform to test and validate final circuits. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. The proposed hardware consumes maximum 2% of the all resources of a Virtex 4 board. Additionally, timing analysis and synthesize report represent that the proposed model works in a high frequency of 371.56 MHz. Moreover, to validate the results of implementation, the proposed model is compared with the original model and other similar works in terms of accuracy, speed-up, and maximum number of implemented astrocyte.