# Parallel Simulations of the Sharp Wave-Ripples of the Hippocamp on Multicore CPUs and GPUs
## 1 Introduction  
The simulation of complex biological systems plays a pivotal role in research, as it can substitute for costly, prolonged, and intricate experiments. This markedly enhances efficiency and quantification in research, facilitating the investigation of the impact on biological system operations under artificially imposed conditions. Of particular interest is the study and simulation of the human brain, given its inherent complexity and significance. Within this domain, the hippocampus, a critical region for memory and learning, represents a focal point of research, where simulations can provide invaluable insights into its functional dynamics and responses. The process of memory consolidation occurs during sleep, facilitated by a bidirectional exchange of information between the hippocampus and the cortex [1, 2]. This intricate communication enables the transfer and integration of newly acquired memories, thereby solidifying them for long-term storage and retrieval. During sleep, neurons in the hippocampus and cortex that were active during daytime behavioural tasks are reactivated. This process is of great consequence in linking and modifying memories, transferring them from short-term storage in the hippocampus to long-term storage in the cortex. A pivotal element of this process is the occurrence of brief electrical events in the hippocampus, designated as sharp wave-ripples, which are high-frequency oscillations that facilitate the replay of neural activity patterns, effectively coordinating the communication between the hippocampus and cortex to consolidate and stabilise memories for long-term retention [3, 4]. A recent work [5] described a computational model capable of reproducing the ripple generation mechanism consistent with a wide range of experimental data. It considers a network composed of two different types of neurons, namely excitatory and inhibitory cells. The release of the neurotransmitter glutamate by excitatory neurons increases the likelihood of other neurons firing, thereby stimulating brain activity and propagating signals. In contrast, the release of Gamma-aminobutyricacid (GABA) by inhibitory neurons decreases the likelihood of neuron firing, regulating and balancing neural activity to prevent excessive excitation. In the hippocampal cornu ammonia region 1 (CA1), the ratio of excitatory to inhibitory cells is approximately 5:1 [6], which is maintained in the simulated network where 800 excitatory and 160 inhibitory neurons are modeled [5]. Nevertheless, the computational complexity of such models increases in proportion to the number of neurons involved. The typical approach when facing problems where computational power is needed is the adoption of high-performance computing methods, as has already been conducted in other scientific fields. Thus, to address this issue, several methods have been developed with the objective of accelerating the simulation of biologically realistic neuronal networks [7]. These methods primarily leverage high-performance computing technologies, including supercomputers [8], multicore processors [9], Graphical Processing Units (GPUs) [10; 11], Field Programmable Gate Arrays (FPGAs) [12], and custom processors [13]. However, when focusing on the simulation of the hippocampus, only some of these technologies have been adopted. A study [14] on the Parallel Brain Simulator (PBS) demonstrates its capacity to simulate expansive hippocampal networks through the utilisation of high-performance computing, encompassing clusters and supercomputers. The model demonstrates effective scalability and supports diverse synaptic types. However, the primary limitations include considerable computational complexity and the challenges in balancing accuracy with performance, particularly as the network size increases. Furthermore, the platform's dependency on specialised hardware may restrict its accessibility for some researchers.