Abstract
Dendrites, the most conspicuous elements of neurons, extensively determine a cell’s capacity to recognise synaptic inputs. Investigating its structure and morphological properties unravels the functioning mechanism of neurons that cooperates the process of learning and memory. This research systematically generates a varying topology of dendrites in a multi-compartmental model of a neuron with passive properties and it further explores a cell’s integration ability of complex synaptic potentials. The neurons receive an equal number of binary input patterns of synaptic activity and the performance of a cell is gauged by calculating the signal to noise ratio between amplitudes of somatic voltage. The objective is to analyse the types of input pattern in combination with morphological properties that may strengthen or weaken the somatic response. Finally, an evolutionary algorithm produces a fine variety of branching structures calculating the weighted sum of synaptic inputs, further identifying the impact of membrane and morphological properties on neuronal performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cooke, S.F., Bliss, T.V.P.: Plasticity in the human central nervous system. Brain 129(7), 1659–1673 (2006)
Cuntz, H., Borst, A., Segev, I.: Optimization principles of dendritic structure. Theoret. Biol. Med. Model. 4(1), 1 (2007)
De Sousa, G., et al.: Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances. J. Comput. Neurosci. 38(2), 221–234 (2015)
Graham, B.P.: Pattern recognition in a compartmental model of a CA1 pyramidal neuron. Network Comput. Neural Syst. 12(4), 473–492 (2001)
Gulledge, A.T., Kampa, B.M., Stuart, G.J.: Synaptic integration in dendritic trees. J. Neurobiol. 64(1), 75–90 (2005)
Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Psychology Press, New York (2005)
Hines, M.L., Nicholas, T.: The NEURON simulation environment. Neural Comput. 9(6), 1179–1209 (1997)
Ho, V.M., Lee, J.-A., Martin, K.C.: The cell biology of synaptic plasticity. Science 334(6056), 623–628 (2011)
Langton, C.G.: Artificial Life: An Overview. MIT Press, Cambridge (1997)
Martínez-Cerdeño, V.: Dendrite and spine modifications in autism and related neurodevelopmental disorders in patients and animal models. Developmental Neurobiology (2016)
Takeuchi, T., Duszkiewicz, A.J., Morris, R.G.: The synaptic plasticity and memory hypothesis: encoding, storage and persistence. Phil. Trans. R. Soc. B 369(1633), 20130288 (2014)
Van Pelt, J., Verwer, R.W.H.: Growth models (including terminal and segmental branching) for topological binary trees. Bull. Math. Biol. 47(3), 323–336 (1985)
Wen, Q., Chklovskii, D.B.: A cost-benefit analysis of neuronal morphology. J. Neurophysiol. 99(5), 2320–2328 (2008)
Williams, R.W., Herrup, K.: The control of neuron number. Annu. Rev. Neurosci. 11(1), 423–453 (1988)
Acknowledgements
I would like to express my sincere gratitude to Dr. Rene te Boekhorst for his valued support and guidance extended to me.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kagdi, M.Z. (2017). Evolving Dendritic Morphologies Highlight the Impact of Structured Synaptic Inputs on Neuronal Performance. In: Bracciali, A., Caravagna, G., Gilbert, D., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016. Lecture Notes in Computer Science(), vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-319-67834-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-67834-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67833-7
Online ISBN: 978-3-319-67834-4
eBook Packages: Computer ScienceComputer Science (R0)