In this paper, we study the influence of the coupling strength on the synchronization behavior of a population of leaky integrate-and-fire neurons that is self-excitatory with a population density approach. Each neuron of the population is assumed to be stochastically driven by an independent Poisson spike train and the synaptic interaction between neurons is modeled by a potential jump at the reception of an action potential. Neglecting the synaptic delay, we will establish that for a strong enough connectivity between neurons, the solution of the partial differential equation which describes the population density function must blow up in finite time. Furthermore, we will give a mathematical estimate on the average connection per neuron to ensure the occurrence of a burst. Interpreting the blow up of the solution as the presence of a Dirac mass in the firing rate of the population, we will relate the blow up of the solution to the occurrence of the synchronization of neurons. Fully stochastic simulations of a finite size network of leaky integrate-and-fire neurons are performed to illustrate our theoretical results.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s11538-013-9823-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing.
ACS Nano
January 2025
Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes.
View Article and Find Full Text PDFSg-snn: a self-organizing spiking neural network based on temporal information.
Cogn Neurodyn
December 2025
Shanghai University, Shanghai, China.
Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network.
View Article and Find Full Text PDFFrom light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing.
Light Sci Appl
January 2025
Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior, dynamic responses, and energy efficiency characteristics. Although charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity, replicating the key functionality of neurons-integrating diverse presynaptic inputs to fire electrical impulses-has remained challenging. In this study, we developed reconfigurable metal-oxide-semiconductor capacitors (MOSCaps) based on hafnium diselenide (HfSe).
View Article and Find Full Text PDFA robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.
BMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
View Article and Find Full Text PDFArtificial Photothermal Nociceptor Using Mott Oscillators.
Adv Sci (Weinh)
December 2024
Department of Materials Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
Bioinspired sensory systems based on spike neural networks have received considerable attention in resolving high energy consumption and limited bandwidth in current sensory systems. To efficiently produce spike signals upon exposure to external stimuli, compact neuron devices are required for signal detection and their encoding into spikes in a single device. Herein, it is demonstrated that Mott oscillative spike neurons can integrate sensing and ceaseless spike generation in a compact form, which emulates the process of evoking photothermal sensing in the features of biological photothermal nociceptors.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!