References
- Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717–727.
- Acharya UR, Fujita H, Lih OS, et al. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl Based Syst. 2017;132(supp C):62–71.
- Boostani R, Rismanchi M, Khosravani A, et al. Presenting a hybrid method in order to predict the 2009 pandemic influenza A (H1N1). Adv Comput. 2012;3(1):31–43.
- Srinivasan V, Eswaran C, Sriraam N. Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst. 2005;29(6):647–660.
- Basheer I, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. 2000;43(1):3–31.
- Xin H, Wang F, Li Y, et al. Secondary release of exosomes from astrocytes contributes to the increase in neural plasticity and improvement of functional recovery after stroke in rats treated with exosomes harvested from MicroRNA 133b-overexpressing multipotentmesenchymal stromal cells. Cell Transplant. 2017;26(2):243–257.
- Nudo RJ, Plautz EJ, Frost SB. Role of adaptive plasticity in recovery of function after damage to motor cortex. Muscle Nerve. 2001;24(8):1000–1019.
- Ward NS. Restoring brain function after stroke — bridging the gap between animals and humans. Nat Rev Neurol. 2017;13(4):244–255.
- Ievins A, Moritz CT. Therapeutic stimulation for restoration of function after spinal cord injury. Physiology. 2017;32(5):391–398.
- Rismanchi M. Toward restorative neurosurgery at cortical level: the role of injured primitive networks in upsetting perilesional reorganization. Neurosurg Rev. 2013;36(3):503–504.
- Samii M, Gerganov VM, Freund H-J. Restorative neurosurgery of the cortex: resections of pathologies of the central area can improve preexisting motor deficits. Neurosurg Rev. 2012;35(2):277–286.
- Benítez JM, Castro JL, Requena I. Are artificial neural networks black boxes? IEEE Trans Neural Netw. 1997;8(5):1156–1164.
- Harrington PB, Wan C. Sensitivity analysis applied to artificial neural networks: what has my neural network actually learned? Anal Chem. 1998;70:2983–2990.
- Olden JD, Jackson DA. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol Modell. 2002;154(1):135–150.
- Olden JD, Joy MK, Death RG. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Modell. 2004;178(3):389–397.
- Baek HJ. Regional cortical hyperperfusion on perfusion CT during postictal motor deficit: a case report. J Korean Soc Radiol. 2013;69(2):89–92.
- Pearl PL, Carrazana EJ, Holmes GL. The Landau–Kleffner syndrome. Epilepsy Curr. 2001;1(2):39–45.
- Iliuta L, Rac-Albu M. Ivabradine versus beta-blockers in patients with conduction abnormalities or left ventricular dysfunction undergoing cardiac surgery. Cardiol Ther. 2014;3(1-2):13–26.
- Cardona F, Seide H, Cox RA, et al. Effect of right atrial pacing, intravenous amiodarone and beta blockers for suppression of atrial fibrillation after coronary artery bypass surgery: a pilot study. P R Health Sci J. 2003;22(2):119–123.
- Chiken S, Nambu A. Mechanism of deep brain stimulation: inhibition, excitation, or disruption? Neuroscientist. 2016;22(3):313–322.