Back

Phase resetting of in-phase synchronized Hodgkin-Huxleydynamics under voltage perturbation reveals reduced null space

Gupta, R.; Karmeshu, ; Singh, R. K. B.

2026-03-24 neuroscience
10.64898/2026.03.21.713085 bioRxiv
Show abstract

Voltage perturbations to a repetitively firing Hodgkin-Huxley (HH) model of neuronal spiking in the bistable regime with coexisting limit cycle and stable steady node can either lead to the spikes phase resetting or collapse to the stable steady state. The latter describes a non-firing hyperpolarized quiescent state of the neuron despite the presence of constant external current. Using asymptotic phase response curve (PRC), the impact of voltage perturbations on a repetitively firing HH model is studied here while it is diffusively coupled to another HH model under identical external stimulation. It is observed that the pre-perturbation state of synchronization and the coupling strength critically determine the PRC response of the perturbed HH dynamics. Higher coupling strengths of perfectly in-phase (anti-phase) synchronized HH models shrink (expand) the combinatorial space of perturbation strengths and the oscillation phases causing collapse to the quiescent state. This indicates reduced (enlarged) basin of attraction, viz. the null space, associated with the steady state in the HH phase space. The findings bear important implications to the spiking dynamics of diverse interneurons, as well as special cases of pyramidal neurons, coupled through electrical synapses via. gap junctions, and suggest the role of gap junction plasticity in tuning vulnerability to quiescent state in the presence of biological noise and spikelets.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Chaos, Solitons & Fractals
32 papers in training set
Top 0.1%
18.1%
2
Journal of Computational Neuroscience
23 papers in training set
Top 0.1%
17.1%
3
Frontiers in Computational Neuroscience
53 papers in training set
Top 0.2%
9.8%
4
Physical Review E
95 papers in training set
Top 0.1%
7.0%
50% of probability mass above
5
PLOS Computational Biology
1633 papers in training set
Top 5%
6.6%
6
Scientific Reports
3102 papers in training set
Top 39%
3.5%
7
Cognitive Neurodynamics
15 papers in training set
Top 0.1%
2.7%
8
Frontiers in Systems Neuroscience
19 papers in training set
Top 0.1%
2.3%
9
Biological Cybernetics
12 papers in training set
Top 0.1%
2.0%
10
Neuroscience
88 papers in training set
Top 0.8%
2.0%
11
Nonlinear Dynamics
10 papers in training set
Top 0.2%
1.7%
12
Brain Topography
23 papers in training set
Top 0.2%
1.4%
13
PLOS ONE
4510 papers in training set
Top 57%
1.4%
14
Neural Computation
36 papers in training set
Top 0.5%
1.3%
15
Frontiers in Neural Circuits
36 papers in training set
Top 0.4%
1.2%
16
Neural Networks
32 papers in training set
Top 0.6%
1.2%
17
Chaos: An Interdisciplinary Journal of Nonlinear Science
16 papers in training set
Top 0.2%
0.9%
18
Bulletin of Mathematical Biology
84 papers in training set
Top 2%
0.9%
19
Brain Sciences
52 papers in training set
Top 2%
0.8%
20
Mathematical Biosciences
42 papers in training set
Top 1%
0.7%
21
Brain Structure and Function
83 papers in training set
Top 0.6%
0.7%
22
eneuro
389 papers in training set
Top 9%
0.7%
23
Frontiers in Neuroscience
223 papers in training set
Top 8%
0.7%
24
Biology
43 papers in training set
Top 3%
0.7%
25
Journal of Theoretical Biology
144 papers in training set
Top 2%
0.7%
26
Biophysical Journal
545 papers in training set
Top 6%
0.6%
27
Neurocomputing
13 papers in training set
Top 0.7%
0.6%
28
PRX Life
34 papers in training set
Top 1%
0.6%
29
Cerebral Cortex
357 papers in training set
Top 3%
0.6%
30
Network Neuroscience
116 papers in training set
Top 1%
0.6%