Neuronal morphology enhances robustness to perturbations of channel densities

Zang, Yunliang; Marder, Eve

doi:10.1073/pnas.2219049120

Cited by 24 publications

(13 citation statements)

References 60 publications

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Section: Discussionmentioning

confidence: 99%

“…There have been other studies taking various approaches that aim to shed light on the general question about the influence of K-channels on membrane excitability (Santini and Porter, 2010; Shipston, 2001), including studies that make spatial considerations (Zang and Marder, 2023). For instance, Drion et al (2015) investigate how changes in the amplitude of A-type K-channels and CaL channels modify the qualitative features of I-F curves.…”

Section: Discussionmentioning

confidence: 99%

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Association between electrophysiological phenotypes and Kv2.1 potassium channel expression explained by geometrical analysis

Reyes-Garibaldi,

Herrera-Valdez

2023

Preprint

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Excitable cells exhibit different electrophysiological profiles while responding to current stimulation in current-clamp experiments. In theory, the differences could be explained by changes in the expression of proteins mediating transmembrane ion transport. Experimental verification by performing systematic, controlled variations in the expression of proteins of the same type (e.g. voltage-dependent, noninactivating Kv2.1 channels) is difficult to achieve in the absence of other changes. However, biophysical models enable this possibility and allows us to assess and characterise the electrophysiological phenotypes associated to different levels of expression of non-inactivating voltage-dependent K-channels of type Kv2.1. To do so, we use a 2-dimensional biophysical model of neuronal membrane potential and study the phase plane geometry and bifurcation structures associated with different levels of Kv2.1 expression with the input current as bifurcation parameter. We find that increasing the expression of Kv2.1 channels reduces the size of the region of the phase plane from which action potentials can be initiated. The changes in expression can also be related to different transitions between rest and repetitive firing in current clamp experiments. For instance, increasing the number of Kv2.1 channels shifts the rheobase current to higher levels, but also expands the dynamic range in which excitatory external current produces repetitive spiking. Our analysis shows that changes in the responses to increasing input currents can be associated to different sequences of fixed point bifurcations. In general, the fixed points are attracting, then repulsive, and later become attracting again as the input current increases, but the bifurcation sequences also include changes in fixed point type, and change qualitatively with the expression of Kv2.1 channels. In the non-repetitive spiking regime with low current stimulation, low expression of Kv2.1 channels yields bifurcation sequences that include transitions between 3 and 1 fixed points, and repetitive firing starts with delays that decrease with increasing current (aggregation). For higher expression of Kv2.1 channels there is only one fixed point that changes in type and attractivity as the input current increases, convergence to rest tends to be oscillatory (resonance), and repetitive spiking starts without noticeable delays. Our models explain how the same neuron is theoretically be capable of including both aggregating and resonant modes of integration for synaptic input, as shown in current clamp experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Association between electrophysiological phenotypes and Kv2.1 potassium channel expression explained by geometrical analysis

Reyes-Garibaldi,

Herrera-Valdez

2023

Preprint

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“…Neuronal morphology is critical for the functioning of neuronal spikes, including the reliability of spiking and its propagation. In fact, the diversity of neuronal morphology and working modes is an important basis for their functional diversity, allowing neurons to be classified into different types, according to their firing patterns [6,7]. Neurons can be classified into different types according to their firing patterns.…”

Section: Verification Of Dynamic Computational Characteristicsmentioning

confidence: 99%

Hardware Implementation of an Approximate Simplified Piecewise Linear Spiking Neuron

et al. 2023

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Artificial intelligence has revolutionized image and speech recognition, but the neural network fitting method has limitations. Neuromorphic chips that mimic biological neurons can better simulate the brain’s information processing mechanism. As the basic computing component of the new neuromorphic network, the new neural computing unit’s design and implementation have important significance; however, complex dynamical features come with a high computational cost: approximate computing has unique advantages, in terms of optimizing the computational cost of neural networks, which can solve this problem. This paper proposes a hardware implementation of an approximate spiking neuron structure, based on a simplified piecewise linear model (SPWL), to optimize power consumption and area. The proposed structure can achieve five major neuron spiking generation patterns. The proposed design was synthesized and compared to similar designs, to evaluate its potential advantages and limitations. The results showed that the approximate spiking neuron had the lowest computational cost and the fastest computation speed. A typical spiking neural network was constructed, to test the usability of the SPWL model. The results showed that the proposed approximate spiking neuron could work normally in the spiking neural network, and achieved an accuracy of 94% on the MNIST dataset.

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“…Neurons can generally be divided into two components: cell bodies and neurites. While some neurons, such as Purkinje neurons, have dendrites and axons separated within the neurites [1], others, such as LP neurons in the crab stomach, only have axons and neurites [2]. Neurites that only receive signals are called dendrites, while those that both receive and send signals are simply referred to as neurites.…”

Section: Introductionmentioning

confidence: 99%

Yet Another Effective Dendritic Neuron Model Based on the Activity of Excitation and Inhibition

Yang

Li²,

et al. 2023

Mathematics

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Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Each of the three improved models are designed to mimic the excitation and inhibition activity of neurons. The improved model proposed in this paper is shown to be effective in the experimental part. All three models and original DNM have their own strengths, so it can be considered that the new model proposed in this paper well enriches the diversity of neuronal models and contributes to future research on networks models.

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Neuronal morphology enhances robustness to perturbations of channel densities

Cited by 24 publications

References 60 publications

Association between electrophysiological phenotypes and Kv2.1 potassium channel expression explained by geometrical analysis

Association between electrophysiological phenotypes and Kv2.1 potassium channel expression explained by geometrical analysis

Hardware Implementation of an Approximate Simplified Piecewise Linear Spiking Neuron

Yet Another Effective Dendritic Neuron Model Based on the Activity of Excitation and Inhibition

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