000916175 001__ 916175
000916175 005__ 20240313095006.0
000916175 037__ $$aFZJ-2022-05995
000916175 1001_ $$0P:(DE-Juel1)176593$$aMorales-Gregorio, Aitor$$b0$$eCorresponding author$$ufzj
000916175 1112_ $$aINM-IBI Retreat$$cJuelich$$d2022-10-18 - 2022-10-19$$wGermany
000916175 245__ $$aOscillating neural activity saves energy via reduced Na+ ion flux
000916175 260__ $$c2022
000916175 3367_ $$033$$2EndNote$$aConference Paper
000916175 3367_ $$2BibTeX$$aINPROCEEDINGS
000916175 3367_ $$2DRIVER$$aconferenceObject
000916175 3367_ $$2ORCID$$aCONFERENCE_POSTER
000916175 3367_ $$2DataCite$$aOutput Types/Conference Poster
000916175 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1673265756_16301$$xAfter Call
000916175 520__ $$aNeural oscillations are ubiquitous in the nervous system and have been shown to enable efficient communi-cation between cortical areas [1]. However, some brain regions tend to show oscillatory activity when theputative amount of information to be transmitted is low, such as the electroencephalography (EEG) of thevisual cortex oscillating at ~10 Hz when the eyes are closed and no visual input is received [2]. To the bestof our knowledge, it is unclear whether there are any practical benefits from such low-frequency oscillationsassociated with reduced information transmission.Here, we hypothesize that synchronous oscillations at low frequencies can reduce the energy consumptionof neurons when compared to asynchronous activity, assuming the same total amount of spikes. We proposethat energy expenditure is decreased through the coordinated flux of ions (especially Na + ) between the intra-and extracellular space, leading to ion concentration gradients that are favorable for reducing the energyconsumed by ion pumps. The saved energy may be considerable because neuronal Na-K pumps consume upto 40% of the total energy in the brain [3, 4].To test our hypothesis, we set up an in silico experiment using a modified Hodgkin-Huxley model whichaccounts for both intra- and extracellular ion concentrations with biologically realistic parameters [5]. First,we extended the model to account for a small population of neurons (N=10) with a shared extracellular space.Then, we simulated our model in two different conditions (with approximately the same amount of spikes)using different types of input to the neurons: 1) an inhomogeneous Poisson process with an oscillating rate(oscillatory condition); 2) a Poisson process with a constant rate (non-oscillatory condition). We observed thatthe total Na + flux from the intracellular to the extracellular space was around 20% lower for the oscillatorycondition, i.e. less Na + had to be pumped out of the neurons and thus less energy was needed. We consideredspike waveforms as potential indirect evidence of altered Na + currents between the two conditions. Wemeasured the waveform height and width in silico from our model, and in vivo from extracellular recordingsof macaque visual cortex neurons [6]. However, both the computational model and the in vivo data showeda strong robustness of spike waveforms to the presence of oscillations, despite the large ion flux distortionsrevealed by the model.In conclusion, our computational model suggests that neural oscillations save energy due to favorable Na +gradients, but we could not test this hypothesis using the available in vivo data since the action potentialwaveforms appear unchanged by the altered Na + flux. Future work may consider more direct measurementsof Na + concentrations in vitro or in vivo to test whether the proposed mechanism is at work.References:[1] Sengupta et al. (2013). PLoS Comput. Biol. doi.org/10.1371/journal.pcbi.1003263[2] Lewine and Orrison (1995). doi.org/10.1016/B978-0-8151-6509-5.50012-6[3] Attwell and Laughlin (2001). J. Cereb. Blood Flow Metab. doi.org/10.1097/00004647-200110000-00001[4] Harris et al. (2012). Neuron. doi.org/10.1016/j.neuron.2012.08.019[5] Hübel and Dahlem (2014). PLoS Comput. Biol. doi.org/10.1371/journal.pcbi.1003941[6] Chen et al. (2022). Scientific Data. doi.org/10.1038/s41597-022-01180-1
000916175 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000916175 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000916175 7001_ $$0P:(DE-Juel1)176920$$aKleinjohann, Alexander$$b1$$ufzj
000916175 7001_ $$0P:(DE-Juel1)144576$$aIto, Junji$$b2$$ufzj
000916175 7001_ $$0P:(DE-Juel1)180539$$aAlbers, Jasper$$b3$$ufzj
000916175 7001_ $$0P:(DE-Juel1)180150$$aFischer, Kirsten$$b4$$ufzj
000916175 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b5$$ufzj
000916175 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b6$$ufzj
000916175 909CO $$ooai:juser.fz-juelich.de:916175$$pec_fundedresources$$pVDB$$popenaire
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176593$$aForschungszentrum Jülich$$b0$$kFZJ
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176920$$aForschungszentrum Jülich$$b1$$kFZJ
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144576$$aForschungszentrum Jülich$$b2$$kFZJ
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180539$$aForschungszentrum Jülich$$b3$$kFZJ
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180150$$aForschungszentrum Jülich$$b4$$kFZJ
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b5$$kFZJ
000916175 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138512$$aForschungszentrum Jülich$$b6$$kFZJ
000916175 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000916175 9141_ $$y2022
000916175 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000916175 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000916175 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000916175 980__ $$aposter
000916175 980__ $$aVDB
000916175 980__ $$aI:(DE-Juel1)INM-6-20090406
000916175 980__ $$aI:(DE-Juel1)IAS-6-20130828
000916175 980__ $$aI:(DE-Juel1)INM-10-20170113
000916175 980__ $$aUNRESTRICTED
000916175 981__ $$aI:(DE-Juel1)IAS-6-20130828