Aug 2026· Frontiers in Systems Neuroscience· Vol 20· 0 citations· 35 references
TL;DR
A high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics and paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
Abstract
Understanding the neural mechanisms underlying general anesthesia remains a significant challenge in neuroscience and clinical practice. Traditional software-based simulations of large-scale brain networks are often constrained by high computational costs and fail to achieve real-time performance.
In this paper, we propose a high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics. The system successfully models a cortical network comprising 10,000 spiking neurons (8,000 excitatory and 2,000 inhibitory) utilizing the biologically plausible Izhikevich neuron model. Deployed on a field-programmable gate array (FPGA), the proposed architecture exploits high parallelism to achieve real-time simulation speeds. By adjusting synaptic weights and network parameters to mimic the pharmacological eects of anesthetic agents, our system can continuously monitor and evaluate state transitions in neural synchronization and firing patterns.
The results demonstrate that the hardware-accelerated neuromorphic approach provides an efficient, scalable, and real-time platform for investigating large-scale neural dynamics.
Pending future validation against empirical clinical EEG data, this foundational framework paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
It is demonstrated that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy, establishing a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.
P. Félix, M. Jurado-Parras, J. Freitas et al.· bioRxiv· 0 citations
It is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems is proved.
Mesala Sravani, K. Kumari, S. M. Reddy· International Journal of Unc...· 0 citations
This work introduces their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents, and derives and validate a mean-field neural mass model that remains stable while retaining working-memory functionality.
D. Depannemaecker, Adrien D’hollande, G. Casagrande et al.· Nature Communications· 0 citations
This paper presents a low-power analog implementation of astrocyte dynamics using a simplified Postnov model. The proposed circuit employs floating-gate MOS transistors, which are well-suited for low-voltage, low-power neuromorphic VLSI systems. The architecture consists of two interconnected subcircuits that emulate the internal state and output behavior of the astrocyte model. The proposed design reproduces astrocytic dynamics in real time, closely matching the temporal behavior observed in biological systems. Simulations are performed using HSPICE in a $0.18~\mu $ m CMOS process. Results demonstrate that the circuit effectively mimics key astrocytic dynamics, including response timing and output patterns. These findings highlight the circuit’s potential for integration into large-scale neuromorphic architectures, providing a scalable and energy-efficient solution for modeling glial contributions to neural computation. The work establishes a foundation for future hardware implementations of astrocyte-inspired neuromorphic systems and provides a platform for exploring astrocyte function and their potential roles in neurological and biomedical applications.
Neuromorphic computing is closely associated with spiking neuronal networks. However, an alternative class of so-called"rate-based"models arising from computational neuroscience and machine learning forgoes spiking interactions and instead relies on continuous coupling between neurons. Existing neuromorphic implementations designed around spike-based interactions are not well-suited for emulating such models. Here view the distributed simulation of these models as message-passing algorithms on parallel hardware. Leveraging prior art in numerical algorithms and distributed simulation, we outline steps that enable the design of efficient digital neuromorphic accelerators for non-spiking neuronal models. In particular, we show that multi-bit packets, rather than spikes, are the most efficient communication strategy in packet-switched networks and that compared to basic numerical integration methods, higher-order differential equation solvers decrease both computation and communication costs while achieving lower numerical error, but that these benefits are ultimately limited by arithmetic precision. Using our proposed design principles, we convert an existing neuromorphic architecture into a distributed numerical solver - a spikeless neuromorphic system - for continuously-coupled neuronal models. We thereby demonstrate that our theoretical considerations indeed translate into practical advantages, namely reduced energy consumption and delay.
Jakob Jordan, Ole Richter, Cong Li et al.· 0 citations
Findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
Yamini Devi Ykuntam, M. V. Nageswara Rao, Leela Kumari. B.· International Journal of Com...· 0 citations
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