ArticleFrontiers in computational neuroscience2009
Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.
Article in Frontiers in computational neuroscience, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- The Difference Neuron-a new spiking neuron model.Biological cybernetics · 2026Article
- Comparing effective and functional connectivity.Scientific reports · 2026Article
- Nondifferentiable activity in the brain.PNAS nexus · 2024Article
- The tuning of tuning: How adaptation influences single cell information transfer.PLoS computational biology · 2024Article
- Navigation and the efficiency of spatial coding: insights from closed-loop simulations.Brain structure & function · 2024Article
- Shared input and recurrency in neural networks for metabolically efficient information transmission.PLoS computational biology · 2024Article
- An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons.Bulletin of mathematical biology · 2023Article
- Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks.PLoS computational biology · 2023Article
- IDSNN: Towards High-Performance and Low-Latency SNN Training via Initialization and Distillation.Biomimetics (Basel, Switzerland) · 2023Article
- A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains.Biological cybernetics · 2022Article
- Large coefficient of variation of inter-spike intervals induced by noise current in the resonate-and-fire model neuron.Cognitive neurodynamics · 2022Article
- Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks.Scientific reports · 2022Article
- Cortical Representation of Touch in Silico.Neuroinformatics · 2022Article
- A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network.Frontiers in neuroscience · 2022Article
- A convolutional neural network for estimating synaptic connectivity from spike trains.Scientific reports · 2021Article
- Monosynaptic inference via finely-timed spikes.Journal of computational neuroscience · 2021Article
- Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks.Communications biology · 2021Article
- Nonlinear effects of intrinsic dynamics on temporal encoding in a model of avian auditory cortex.PLoS computational biology · 2021Article
- Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks.PloS one · 2020Article
- Identifiability of a Binomial Synapse.Frontiers in computational neuroscience · 2020Article
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3 authors.
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Abstract
Information is transmitted in the brain through various kinds of neurons that respond differently to the same signal. Full characteristics including cognitive functions of the brain should ultimately be comprehended by building simulators capable of precisely mirroring spike responses of a variety of neurons. Neuronal modeling that had remained on a qualitative level has recently advanced to a quantitative level, but is still incapable of accurately predicting biological data and requires high computational cost. In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. This model can express a continuous variety of the firing characteristics in a three-dimensional parameter space rather than just those identified in the conventional discrete categorization. Both high flexibility and low computational cost would help to model the real brain function faithfully and examine how network properties may be influenced by the distributed characteristics of component neurons.
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