A ten-layer feed forward network is constructed in the presence of an exogenous alternating magnetic field. Specifically, our results indicate that for rate coding, the firing rate is significantly increased in the presence of an exogenous alternating magnetic field and particularly with increasing enhancement of the alternating magnetic field amplitude. For temporal coding, the interspike intervals of the spiking sequence are decreased and the distribution of the interspike intervals of the spiking sequence tends to be uniform in the presence of alternating magnetic field.
WangH. and ChenY., Spatiotemporal activities of neural network exposed to external electric fields, Nonlinear Dynamics85 (2016), 881–891. doi:10.1007/s11071-016-2730-4.
2.
YuK., WangJ., DengB. and XieX., Effects of induced electric field on network synchrony under magnetic stimulation, Journal of Tianjin University46 (2013), 726–736(in Chinese).
3.
EbrahimianH., FiroozabadiM. and JanahmadiM., Parametric modeling of nerve cell under the sinusoidal environmental 50 Hz extremely low frequency magnetic field, Journal of Ardabil University of Medical Sciences10 (2013), 820–827.
4.
RuohonenJ., RavazzaniP., TognolaG. and GrandoriF., Modeling peripheral nerve stimulation using magnetic fields, Journal of the Peripheral Nervous System Jpns2 (1997), 17–29.
5.
ModoloJ., LegrosA., PratoF.S. and ThomasA.W., Modulation of neuronal activity with extremely low-frequency magnetic fields: Insights from biophysical modeling, in: 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications, 2010, pp. 1356–1364.
6.
YiG., WangJ., WeiX., DengB., TsangK.M., ChanW.L. and HanC., Effects of extremely low-frequency magnetic fields on the response of a conductance-based neuron model, International Journal of Neural Systems24 (2014), 1450007. doi:10.1142/S0129065714500075.
7.
CameraF., ThomasA.W., PaffiA., D'InzeoG. and ApollonioF., Effects of pulsed magnetic field on neurons: Cnp signal silences a feed-forward network model, in: 6th Annual International IEEE/EMBS Conference on Neural Engineering, San Diego, CA, 2013, pp. 223–226.
8.
JiangX.Y., WangJ., YiG.S., DengB., WeiX.L. and HanC.X., Effects of extremely low-frequency magnetic fields on neuron activity, in: Proceedings of the 31st Chinese Control Conference, IEEE, 2012, pp. 7355–7359.
9.
IzhikevichE.M., Which model to use for cortical spiking neurons?, IEEE Transactions on Neural Networks15 (2004), 1063–1070. doi:10.1109/TNN.2004.832719.
10.
IzhikevichE.M., Simple model of spiking neurons, IEEE Transactions on Neural Networks14 (2003), 1569–1572. doi:10.1109/TNN.2003.820440.
11.
DaveyK.R., EpsteinC.M., GeorgeM.S. and BohningD.E., Modeling the effects of electrical conductivity of the head on the induced electrical field in the brain during magnetic stimulation, Clin. Neurophysiol.114 (2003), 2204–2209. doi:10.1016/S1388-2457(03)00240-2.
12.
BédardC., KrögerH. and DestexheA., Model of low-pass filtering of local field potentials in brain tissue, Physical Review E73 (2006), 051911. doi:10.1103/PhysRevE.73.051911.
13.
RadmanT., RamosR.L., BrumbergJ.C. and BiksonM., Role of cortical cell type and morphology in sub- and suprathreshold uniform electric field stimulation, Brain Stimulation2 (2009), 215–228.e3. doi:10.1016/j.brs.2009.03.007.
14.
GiannìM., LibertiM., ApollonioF. and D'InzeoG., Modeling electromagnetic fields detectability in a HH-like neuronal system: Stochastic resonance and window behavior, Biological Cybernetics94 (2006), 118–127. doi:10.1007/s00422-005-0029-5.
15.
DjurfeldtM., LundqvistM., JohanssonC., RehnM., EkebergÖ. and LansnerA., Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer, IBM Journal of Research and Development52 (2008), 31–41. doi:10.1147/rd.521.0031.
16.
beim GabenP., ZhouC., ThielM. and KurthsJ. (eds), Lectures in Supercomputational Neuroscience: Dynamics in Complex Brain Networks, Springer, Berlin, 2008.
17.
LeCunY., BengioY. and HintonG., Deep learning, Nature521 (2015), 436–444. doi:10.1038/nature14539.
18.
PaoliM., WeiszN., AntoliniR. and HaaseA., Spatially resolved time-frequency analysis of odour coding in the insect antennal lobe, European Journal of Neuroscience44 (2016), 2387–2395. doi:10.1111/ejn.13344.
19.
WonJ.H., TremblayK., ClinardC.G., WrightR.A., SagiE. and SvirskyM., The neural encoding of formant frequencies contributing to vowel identification in normal-hearing listeners, Journal of the Acoustical Society of America139 (2016), 1–11. doi:10.1121/1.4931909.
20.
JonesH.G., BrownA.D., KokaK., ThorntonJ.L. and TolinD.J., Sound frequency-invariant neural coding of a frequency-dependent cue to sound source location, Journal of Neurophysiology114 (2015), 531–539. doi:10.1152/jn.00062.2015.
21.
RosenbaumR., ZimnikA., ZhengF., TurnerR.S., AlzheinerC., DoironB. and RubinJ.E., Axonal and synaptic failure suppress the transfer of firing rate oscillations, synchrony and information during high frequency deep brain stimulation, Neurobiology of Disease62 (2014), 86–99. doi:10.1016/j.nbd.2013.09.006.
22.
AlvespintoA., PalmerA.R. and LopezpovedaE.A., Perception and coding of high-frequency spectral notches: Potential implications for sound localization, Frontiers in Neuroscience8 (2014), 112.
23.
TsuboY., IsomuraY. and FukaiT., Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons, PLoS Computational Biology8 (2012), e1002461. doi:10.1371/journal.pcbi.1002461.
24.
YangY., RamamurthyB., NeefA. and Xu-FriedmanM.A., Low somatic sodium conductance enhances action potential precision in time-coding auditory neurons, Journal of Neuroscience36 (2016), 11999–12009. doi:10.1523/JNEUROSCI.1475-16.2016.
