We consider a heterogeneous, globally coupled population of excitatory quadratic integrate-and-fire neurons with excitability adaptation due to a metabolic feedback associated with ketogenic diet, a form of therapy for epilepsy. Bifurcation analysis of a three-dimensional mean-field system derived in the framework of next-generation neural mass models allows us to explain the scenarios and suggest control strategies for the transitions between the neurophysiologically desired asynchronous states and the synchronous, seizure-like states featuring collective oscillations. We reveal two qualitatively different scenarios for the onset of synchrony. For weaker couplings, a bistability region between the lower- and the higher-activity asynchronous states unfolds from the cusp point, and the collective oscillations emerge via a supercritical Hopf bifurcation. For stronger couplings, one finds seven co-dimension two bifurcation points, including pairs of Bogdanov–Takens and generalized Hopf points, such that both lower- and higher-activity asynchronous states undergo transitions to collective oscillations, with hysteresis and jump-like behavior observed in vicinity of subcritical Hopf bifurcations. We demonstrate three control mechanisms for switching between asynchronous and synchronous states, involving parametric perturbation of the adenosine triphosphate (ATP) production rate, external stimulation currents, or pulse-like ATP shocks, and indicate a potential therapeutic advantage of hysteretic scenarios.

1.
E.
Montbrió
,
D.
Pazó
, and
A.
Roxin
, “
Macroscopic description for networks of spiking neurons
,”
Phys. Rev. X
5
,
021028
(
2015
).
2.
C.
Bick
,
M.
Goodfellow
,
C. R.
Laing
, and
E. A.
Martens
, “
Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: A review
,”
J. Math. Neurosci.
10
,
9
(
2020
).
3.
S.
Coombes
and
Á.
Byrne
, “Next generation neural mass models,” in Nonlinear Dynamics in Computational Neuroscience, edited by F. Corinto and A. Torcini (Springer International Publishing, Cham, 2019), pp. 1–16.
4.
A.
Byrne
,
R. D.
O’Dea
,
M.
Forrester
,
J.
Ross
, and
S.
Coombes
, “
Next-generation neural mass and field modeling
,”
J. Neurophysiol.
123
,
726
(
2020
).
5.
S.
Coombes
, “
Next generation neural population models
,”
Front. Appl. Math. Stat.
9
,
1128224
(
2023
).
6.
S.
Coombes
,
P.
beim Graben
,
R.
Potthast
, and
J.
Wright
,
Neural Fields: Theory and Applications
(
Springer Berlin
,
Heidelberg
,
2014
).
7.
G.
Deco
,
V. K.
Jirsa
,
P. A.
Robinson
,
M.
Breakspear
, and
K.
Friston
, “
The dynamic brain: From spiking neurons to neural masses and cortical fields
,”
PLoS Comput. Biol.
4
,
e1000092
(
2008
).
8.
N.
Deschle
,
J.
Ignacio Gossn
,
P.
Tewarie
,
B.
Schelter
, and
A.
Daffertshofer
, “
On the validity of neural mass models
,”
Front. Comput. Neurosci.
14
,
581040
(
2021
).
9.
E.
Ott
and
T. M.
Antonsen
, “
Low dimensional behavior of large systems of globally coupled oscillators
,”
Chaos
18
,
037113
(
2008
).
10.
E.
Ott
and
T. M.
Antonsen
, “
Long time evolution of phase oscillator systems
,”
Chaos
19
,
023117
(
2009
).
11.
T. B.
Luke
,
E.
Barreto
, and
P.
So
, “
Complete classification of the macroscopic behavior of a heterogeneous network of theta neurons
,”
Neural Comput.
25
,
3207
(
2013
).
12.
F.
Devalle
,
A.
Roxin
, and
E.
Montbrió
, “
Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks
,”
PLoS Comput. Biol.
13
,
e1005881
(
2017
).
13.
I.
Ratas
and
K.
Pyragas
, “
Macroscopic self-oscillations and aging transition in a network of synaptically coupled quadratic integrate-and-fire neurons
,”
Phys. Rev. E
94
,
032215
(
2016
).
14.
B.
Pietras
,
F.
Devalle
,
A.
Roxin
,
A.
Daffertshofer
, and
E.
Montbrió
, “
Exact firing rate model reveals the differential effects of chemical versus electrical synapses in spiking networks
,”
Phys. Rev. E
100
,
042412
(
2019
).
15.
E.
Montbrió
and
D.
Pazó
, “
Exact mean-field theory explains the dual role of electrical synapses in collective synchronization
,”
Phys. Rev. Lett.
125
,
248101
(
2020
).
16.
D. S.
Goldobin
, “
Mean-field models of populations of quadratic integrate-and-fire neurons with noise on the basis of the circular cumulant approach
,”
Chaos
31
,
083112
(
2021
).
17.
M.
di Volo
,
M.
Segneri
,
D. S.
Goldobin
,
A.
Politi
, and
A.
Torcini
, “
Coherent oscillations in balanced neural networks driven by endogenous fluctuations
,”
Chaos
32
,
023120
(
2022
).
18.
P.
Clusella
and
E.
Montbrió
, “Exact low-dimensional description for fast neural oscillations with low firing rates,”
arXiv.2208.05515
(
2022
).
19.
B.
Pietras
,
R.
Cestnik
, and
A.
Pikovsky
, “
Exact finite-dimensional description for networks of globally coupled spiking neurons
,”
Phys. Rev. E
107
,
024315
(
2023
).
20.
V.
Pyragas
and
K.
Pyragas
, “
Effect of cauchy noise on a network of quadratic integrate-and-fire neurons with non-cauchy heterogeneities
,”
Phys. Lett. A
480
,
128972
(
2023
).
21.
D. S.
Goldobin
,
M.
di Volo
, and
A.
Torcini
, “
Reduction methodology for fluctuation driven population dynamics
,”
Phys. Rev. Lett.
127
,
038301
(
2021
).
22.
V. V.
Klinshov
and
S. Y.
Kirillov
, “
Shot noise in next-generation neural mass models for finite-size networks
,”
Phys. Rev. E
106
,
L062302
(
2022
).
23.
V. V.
Klinshov
,
P. S.
Smelov
, and
S. Y.
Kirillov
, “
Constructive role of shot noise in the collective dynamics of neural networks
,”
Chaos
33
,
061101
(
2023
).
24.
I.
Ratas
and
K.
Pyragas
, “
Symmetry breaking in two interacting populations of quadratic integrate-and-fire neurons
,”
Phys. Rev. E
96
,
042212
(
2017
).
25.
H.
Schmidt
,
D.
Avitabile
,
E.
Montbrió
, and
A.
Roxin
, “
Network mechanisms underlying the role of oscillations in cognitive tasks
,”
PLoS Comput. Biol.
14
,
e1006430
(
2018
).
26.
K.
Pyragas
,
A. P.
Fedaravičius
, and
T.
Pyragienė
, “
Suppression of synchronous spiking in two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons
,”
Phys. Rev. E
104
,
014203
(
2021
).
27.
A.
Byrne
,
D.
Avitabile
, and
S.
Coombes
, “
Next-generation neural field model: The evolution of synchrony within patterns and waves
,”
Phys. Rev. E
99
,
012313
(
2019
).
28.
H.
Schmidt
and
D.
Avitabile
, “
Bumps and oscillons in networks of spiking neurons
,”
Chaos
30
,
033133
(
2020
).
29.
A.
Byrne
,
J.
Ross
,
R.
Nicks
, and
S.
Coombes
, “
Mean-field models for EEG/MEG: From oscillations to waves
,”
Brain Topogr.
35
,
36
(
2022
).
30.
G.
Dumont
and
B.
Gutkin
, “
Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits
,”
PLoS Comput. Biol.
15
,
e1007019
(
2019
).
31.
H.
Bi
,
M.
Segneri
,
M.
di Volo
, and
A.
Torcini
, “
Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons
,”
Phys. Rev. Res.
2
,
013042
(
2020
).
32.
M.
Segneri
,
H.
Bi
,
S.
Olmi
, and
A.
Torcini
, “
Theta-nested gamma oscillations in next generation neural mass models
,”
Front. Comput. Neurosci.
14
,
47
(
2020
).
33.
S.
Keeley
,
A.
Byrne
,
A.
Fenton
, and
J.
Rinzel
, “
Firing rate models for gamma oscillations
,”
J. Neurophysiol.
121
,
2181
(
2019
).
34.
A.
Ceni
,
S.
Olmi
,
A.
Torcini
, and
D.
Angulo-Garcia
, “
Cross frequency coupling in next generation inhibitory neural mass models
,”
Chaos
30
,
053121
(
2020
).
35.
Á.
Byrne
,
S.
Coombes
, and
P. F.
Liddle
, “A neural mass model for abnormal beta-rebound in schizophrenia,” in Handbook of Multi-Scale Models of Brain Disorders, edited by V. Cutsuridis (Springer, Cham, 2019), pp. 21–27.
36.
G.
Weerasinghe
,
B.
Duchet
,
H.
Cagnan
,
P.
Brown
,
C.
Bick
, and
R.
Bogacz
, “
Predicting the effects of deep brain stimulation using a reduced coupled oscillator model
,”
PLoS Comput. Biol.
15
,
e1006575
(
2019
).
37.
H.
Taher
,
A.
Torcini
, and
S.
Olmi
, “
Exact neural mass model for synaptic-based working memory
,”
PLoS Comput. Biol.
16
,
e1008533
(
2020
).
38.
M.
Gerster
,
H.
Taher
,
A.
Škoch
,
J.
Hlinka
,
M.
Guye
,
F.
Bartolomei
,
V.
Jirsa
,
A.
Zakharova
, and
S.
Olmi
, “
Patient-specific network connectivity combined with a next generation neural mass model to test clinical hypothesis of seizure propagation
,”
Front. Syst. Neurosci.
15
,
675272
(
2021
).
39.
R.
Gast
,
H.
Schmidt
, and
T. R.
Knösche
, “
A mean-field description of bursting dynamics in spiking neural networks with short-term adaptation
,”
Neural Comput.
32
,
1615
(
2020
).
40.
R.
Gast
,
T. R.
Knösche
, and
H.
Schmidt
, “
Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity
,”
Phys. Rev. E
104
,
044310
(
2021
).
41.
A.
Ferrara
,
D.
Angulo-Garcia
,
A.
Torcini
, and
S.
Olmi
, “
Population spiking and bursting in next-generation neural masses with spike-frequency adaptation
,”
Phys. Rev. E
107
,
024311
(
2023
).
42.
J.
Sawicki
,
R.
Berner
,
S. A. M.
Loos
,
M.
Anvari
,
R.
Bader
,
W.
Barfuss
,
N.
Botta
,
N.
Brede
,
I.
Franović
,
D. J.
Gauthier
,
S.
Goldt
,
A.
Hajizadeh
,
P.
Hövel
,
O.
Karin
,
P.
Lorenz-Spreen
,
C.
Miehl
,
J.
Mölter
,
S.
Olmi
,
E.
Schöll
,
A.
Seif
,
P. A.
Tass
,
G.
Volpe
,
S.
Yanchuk
, and
J.
Kurths
, “
Perspectives on adaptive dynamical systems
,”
Chaos
33
,
071501
(
2023
).
43.
L.
Abbott
and
S.
Nelson
, “
Synaptic plasticity: Taming the beast
,”
Nat. Neurosci.
3
,
1178
(
2000
).
44.
N.
Caporale
and
Y.
Dan
, “
Spike timing-dependent plasticity: A Hebbian learning rule
,”
Ann. Rev. Neurosci.
31
,
25
(
2008
).
45.
G. E.
Ha
and
E.
Cheong
, “
Spike frequency adaptation in neurons of the central nervous system
,”
Exp. Neurobiol.
26
,
179
(
2017
).
46.
G.
Fuhrmann
,
H.
Markram
, and
M.
Tsodyks
, “
Spike frequency adaptation and neocortical rhythms
,”
J. Neurophysiol.
88
,
761
(
2002
).
47.
Y.
Katsu-Jiménez
,
R. M. P.
Alves
, and
A.
Giménez-Cassina
, “
Food for thought: Impact of metabolism on neuronal excitability
,”
Exp. Cell Res.
360
,
41
46
(
2017
).
48.
T.
Fardet
and
A.
Levina
, “
Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions
,”
PLoS Comput. Biol.
16
,
e1008503
(
2020
).
49.
I.
Franović
,
S.
Eydam
,
S.
Yanchuk
, and
R.
Berner
, “
Collective activity bursting in a population of excitable units adaptively coupled to a pool of resources
,”
Front. Netw. Physiol.
2
,
841829
(
2022
).
50.
A.
Lutas
and
G.
Yellen
, “
The ketogenic diet: Metabolic influences on brain excitability and epilepsy
,”
Trends Neurosci.
36
,
32
(
2013
).
51.
J. W.
Wheless
, “
History of the ketogenic diet
,”
Epilepsia
49
,
3
(
2008
).
52.
Y.
Fei
,
R.
Shi
,
Z.
Song
, and
J.
Wu
, “
Metabolic control of epilepsy: A promising therapeutic target for epilepsy
,”
Front. Neurol.
11
,
592514
(
2020
).
53.
J. M.
Rho
, “
How does the ketogenic diet induce anti-seizure effects?
,”
Neurosci. Lett.
637
,
4
(
2017
).
54.
I.
D’Andrea Meira
,
T. T.
Romão
,
H. J. P.
do Prado
,
L. T.
Krüger
,
M. E. P.
Pires
, and
P. O.
da Conceição
, “
Ketogenic diet and epilepsy: What we know so far
,”
Front. Neurosci.
13
,
5
(
2019
).
55.
O. F.
El-Rashidy
,
M. F.
Nassar
,
W. A.
Shokair
, and
Y. G. A.
El Gendy
, “
Ketogenic diet for epilepsy control and enhancement in adaptive behavior
,”
Sci. Rep.
13
,
2102
(
2023
).
56.
J. A.
Roberts
,
K. K.
Iyer
,
S.
Vanhatalo
, and
M.
Breakspear
, “
Critical role for resource constraints in neural models
,”
Front. Syst. Neurosci
8
,
154
(
2014
).
57.
Y. S.
Virkar
,
W. L.
Shew
,
J. G.
Restrepo
, and
E.
Ott
, “
Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain
,”
Phys. Rev. E
94
,
042310
(
2016
).
58.
K. A.
Kroma-Wiley
,
P. J.
Mucha
, and
D. S.
Bassett
, “
Synchronization of coupled kuramoto oscillators under resource constraints
,”
Phys. Rev. E
104
,
014211
(
2021
).
59.
I. M.
Glynn
, “
A hundred years of sodium pumping
,”
Annu. Rev. Physiol.
64
,
1
(
2002
).
60.
M. D.
Forrest
, “
The sodium-potassium pump is an information processing element in brain computation
,”
Front. Physiol.
5
,
472
(
2014
).
61.
P.
Bazzigaluppi
,
A. E.
Amini
,
I.
Weisspapir
,
B.
Stefanovic
, and
P. L.
Carlen
, “
Hungry neurons: Metabolic insights on seizure dynamics
,”
Int. J. Mol. Sci.
18
,
2269
(
2017
).
62.
M.
Patel
, “
A metabolic paradigm for epilepsy
,”
Epilepsy Curr.
18
,
318
(
2018
).
63.
R.
Kovács
,
Z.
Gerevich
,
A.
Friedman
,
J.
Otáhal
,
O.
Prager
,
S.
Gabriel
, and
N.
Berndt
, “
Bioenergetic mechanisms of seizure control
,”
Front. Cell. Neurosci.
12
,
335
(
2018
).
64.
H.
Büeler
, “
Impaired mitochondrial dynamics and function in the pathogenesis of Parkinson’s disease
,”
Exp. Neurol.
218
,
235
(
2009
).
65.
D.
Haddad
and
K.
Nakamura
, “
Understanding the susceptibility of dopamine neurons to mitochondrial stressors in Parkinson’s disease
,”
FEBS Lett.
589
,
3702
(
2015
).
66.
E.
Beghi
, “
Global, regional, and national burden of epilepsy, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016
,”
Lancet Neurol.
18
,
357
(
2019
).
67.
M.-C.
Picot
,
M.
Baldy-Moulinier
,
J.-P.
Daurs
,
P.
Dujols
, and
A.
Crespel
, “
The prevalence of epilepsy and pharmacoresistant epilepsy in adults: A population-based study in a western European country
,”
Epilepsia
49
,
1230
(
2008
).
68.
E. G.
Neal
and
J. H.
Cross
, “
Efficacy of dietary treatments for epilepsy
,”
J. Hum. Nutr. Diet.
23
,
113
119
(
2010
).
69.
P.
Joo
,
H.
Lee
,
S.
Wang
,
S.
Kim
, and
A. G.
Hudetz
, “
Network model with reduced metabolic rate predicts spatial synchrony of neuronal activity
,”
Front. Comput. Neurosci.
15
,
738362
(
2021
).
70.
M. O.
Cunningham
,
D. D.
Pervouchine
,
C.
Racca
,
N. J.
Kopell
,
C. H.
Davies
,
R. S. G.
Jones
,
R. D.
Traub
, and
M. A.
Whittington
, “
Neuronal metabolism governs cortical network response state
,”
Proc. Natl. Acad. Sci. U. S. A.
103
,
5597
(
2006
).
71.
S.
Ching
,
P. L.
Purdon
,
S.
Vijayan
,
N. J.
Kopell
, and
E. N.
Brown
, “
A neurophysiological–metabolic model for burst suppression
,”
Proc. Natl. Acad. Sci. U. S. A.
109
,
3095
(
2012
).
72.
G. B.
Ermentrout
and
D. H.
Terman
,
Mathematical Foundations of Neuroscience
(
Springer
,
New York
,
2010
).
73.
E. M.
Izhikevich
,
Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting
(
The MIT Press
,
Cambridge
,
2007
).
74.
C.-W.
Huang
,
C.-C.
Huang
,
J.-T.
Cheng
,
J.-J.
Tsai
, and
S.-N.
Wu
, “
Glucose and hippocampal neuronal excitability: Role of atp-sensitive potassium channels
,”
J. Neurosci. Res.
85
,
1468
(
2007
).
75.
L. E.
Martinet
,
G.
Fiddyment
,
J. R.
Madsen
,
E. N.
Eskandar
,
W.
Truccolo
,
U. T.
Eden
,
S. S.
Cash
, and
M. A.
Kramer
, “
Human seizures couple across spatial scales through travelling wave dynamics
,”
Nat. Commun.
8
,
14896
(
2017
).
76.
G.
Yellen
, “
Ketone bodies, glycolysis, and KATP channels in the mechanism of the ketogenic diet
,”
Epilepsia
49
,
80
(
2008
).
77.
G. B.
Ermentrout
and
N.
Kopell
, “
Parabolic bursting in an excitable system coupled with a slow oscillation
,”
SIAM J. Appl. Math.
46
,
233
(
1986
).
78.
B.
Ermentrout
, “
Type I membranes, phase resetting curves, and synchrony
,”
Neural Comput.
8
,
979
(
1996
).
79.
V. J.
Miller
,
R. A.
LaFountain
,
E.
Barnhart
,
T. S.
Sapper
,
J.
Short
,
W. D.
Arnold
,
P. N.
Hyde
,
C. D.
Crabtree
,
M. L.
Kackley
,
W. J.
Kraemer
,
F. A.
Villamena
, and
J. S.
Volek
, “
A ketogenic diet combined with exercise alters mitochondrial function in human skeletal muscle while improving metabolic health
,”
Am. J. Physiol. Endocrinol. Metab.
319
,
E995
(
2020
).
80.
S. A.
Masino
and
J. M.
Rho
, “Mechanisms of ketogenic diet action,” in Jasper’s Basic Mechanisms of the Epilepsies (Oxford University Press, 2012).
81.
K. J.
Bough
,
J.
Wetherington
,
B.
Hassel
,
J. F.
Pare
,
J. W.
Gawryluk
,
J. G.
Greene
,
R.
Shaw
,
Y.
Smith
,
J. D.
Geiger
, and
R. J.
Dingledine
, “
Mitochondrial biogenesis in the anticonvulsant mechanism of the ketogenic diet
,”
Ann. Neurol.
60
,
223
(
2006
).
82.
L. B.
Gano
,
M.
Patel
, and
J. M.
Rho
, “
Ketogenic diets, mitochondria, and neurological diseases
,”
J. Lipid Res.
55
,
2211
(
2014
).
83.
C. G. J.
Saris
and
S.
Timmers
, “
Ketogenic diets and ketone suplementation: A strategy for therapeutic intervention
,”
Frontiers Nutr.
9
,
947567
(
2022
).
84.
C.
Rackauckas
and
Q.
Nie
, “
DifferentialEquations.jl—A performant and feature-rich ecosystem for solving differential equations in Julia
,”
J. Open Res. Softw.
5
,
15
(
2017
).
85.
F.
Devalle
,
E.
Montbrió
, and
D.
Pazó
, “
Dynamics of a large system of spiking neurons with synaptic delay
,”
Phys. Rev. E
98
,
042214
(
2018
).
86.
H.
Taher
,
D.
Avitabile
, and
M.
Desroches
, “
Bursting in a next generation neural mass model with synaptic dynamics: A slow–fast approach
,”
Nonlinear Dyn.
108
,
4261
(
2022
).
87.
D.
Avitabile
,
M.
Desroches
, and
G. B.
Ermentrout
, “
Cross-scale excitability in networks of quadratic integrate-and-fire neurons
,”
PLoS Comput. Biol.
18
,
e1010569
(
2022
).
88.
R.
Veltz
, see https://hal.archives-ouvertes.fr/hal-02902346 for “Bifurcationkit.jl” (2020).
89.
V.
Klinshov
and
I.
Franović
, “
Two scenarios for the onset and suppression of collective oscillations in heterogeneous populations of active rotators
,”
Phys. Rev. E
100
,
062211
(
2019
).
90.
Dynamic Bifurcations, Lecture Notes in Mathematics Vol. 1493, edited by E. Benoît (Springer, Berlin, 1991).
91.
C.
Kuehn
, “
A mathematical framework for critical transitions: Bifurcations, fast–slow systems and stochastic dynamics
,”
Phys. D
240
,
1020
(
2011
).
92.
R. D.
Fields
, “
A new mechanism of nervous system plasticity: Activity-dependent myelination
,”
Nat. Rev. Neurosci.
16
,
756
(
2015
).
93.
D.
Attwell
and
S. B.
Laughlin
, “
An energy budget for signaling in the grey matter of the brain
,”
J. Cereb. Blood Flow Metab.
21
,
1133
(
2001
).
94.
L. F.
Lafuerza
,
P.
Colet
, and
R.
Toral
, “
Nonuniversal results induced by diversity distribution in coupled excitable systems
,”
Phys. Rev. Lett.
105
,
084101
(
2010
).
95.
S.
Ostojic
,
N.
Brunel
, and
V.
Hakim
, “
Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities
,”
J. Comput. Neurosci.
26
,
369
(
2009
).
96.
C.
Kuehn
, “
A mathematical framework for critical transitions: Normal forms, variance and applications
,”
J. Nonlinear Sci.
23
,
457
(
2013
).
97.
C.
Meisel
,
A.
Klaus
,
C.
Kuehn
, and
D.
Plenz
, “
Critical slowing down governs the transition to neuron spiking
,”
PLoS Comput. Biol.
11
,
e1004097
(
2015
).
98.
M. I.
Maturana
,
C.
Meisel
,
K.
Dell
,
P. J.
Karoly
,
W.
D’Souza
,
D. B.
Grayden
,
A. N.
Burkitt
,
P.
Jiruska
,
J.
Kudlacek
,
J.
Hlinka
,
M. J.
Cook
,
L.
Kuhlmann
, and
D. R.
Freestone
, “
Critical slowing down as a biomarker for seizure susceptibility
,”
Nat. Commun.
11
,
2172
(
2020
).
99.
T.
Wilkat
,
T.
Rings
, and
K.
Lehnertz
, “
No evidence for critical slowing down prior to human epileptic seizures
,”
Chaos
29
,
091104
(
2019
).
100.
J. R.
Cressman
,
G.
Ullah
,
J.
Ziburkus
,
S. J.
Schiff
, and
E.
Barreto
, “
The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: I. Single neuron dynamics
,”
J. Comput. Neurosci.
26
,
159
(
2009
).
101.
M.
Du
,
J.
Li
,
R.
Wang
, and
Y.
Wu
, “
The influence of potassium concentration on epileptic seizures in a coupled neuronal model in the hippocampus
,”
Cogn. Neurodyn.
10
,
405
(
2016
).
102.
G.
Ullah
and
S. J.
Schiff
, “
Assimilating seizure dynamics
,”
PLoS Comput. Biol.
6
,
e1000776
(
2010
).
103.
J.
Zierenberg
,
J.
Wilting
, and
V.
Priesemann
, “
Homeostatic plasticity and external input shape neural network dynamics
,”
Phys. Rev. X
8
,
031018
(
2018
).
You do not currently have access to this content.