Abstract
The response of a neuron when receiving a periodic input current signal is a periodic spike firing rate signal. The frequency of an input sinusoidal current and the surrounding environment such as background noises are two important factors that affect the firing rate output signal of a neuron model. This study focuses on the phase shift between input and output signals, and here we present a new concept: the agility of a neuron, to describe how fast a neuron can respond to a periodic input signal. By applying the score of agility, we are capable of characterizing the surrounding environment; once the frequency of periodic input signal is given, the actual angle of phase shift can then be determined, and therefore different neuron models can be normalized and compared to others.
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References
Amit, D.J., Tsodyks, M.: Quantitative study of attractor neural network retrieving at low spike rates: I Substrate-spikes, rates and neuronal gain. Netw. Comput. Neural Syst. 2(3), 259–273 (1991)
Amit, D.J., Tsodyks, M.: Effective neurons and attractor neural networks in cortical environment. Netw.: Computat. Neural Syst. 3(2), 121–137 (1992)
Brunel, N., Chance, F.S., Fourcaud, N., Abbott, L.: Effects of synaptic noise and filtering on the frequency response of spiking neurons. Phys. Rev. Lett. 86(10), 2186 (2001)
Burkitt, A.N.: A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern. 95(1), 1–19 (2006). https://doi.org/10.1007/s00422-006-0068-6
Burkitt, A.N., Meffin, H., Grayden, D.B.: Study of neuronal gain in a conductance-based leaky integrate-and-fire neuron model with balanced excitatory and inhibitory synaptic input. Biol. Cybern. 89(2), 119–125 (2003)
Carandini, M., Mechler, F., Leonard, C.S., Movshon, J.A.: Spike train encoding by regular-spiking cells of the visual cortex. J. Neurophysiol. 76(5), 3425–3441 (1996)
Chance, F.S.: Modeling cortical dynamics and the response of neurons in the primary visual cortex: Frances S. Chance. Ph.D. thesis, Brandeis University (2000)
Dayan, P., Abbott, L.F., Abbott, L.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge (2001)
Gerstner, W.: Population dynamics of spiking neurons: fast transients, asynchronous states, and locking. Neural Comput. 12(1), 43–89 (2000)
Izhikevich, E.M.: Dynamical Systems in Neuroscience. MIT Press, Cambridge (2007)
Knight, B.W.: Dynamics of encoding in a population of neurons. J. Gen. Physiol. 59(6), 734–766 (1972)
McCormick, D.A., Connors, B.W., Lighthall, J.W., Prince, D.A.: Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol. 54(4), 782–806 (1985)
Rinzel, J.: Models in neurobiology. In: Enns, R.H., Jones, B.L., Miura, R.M., Rangnekar, S.S. (eds.) Nonlinear Phenomena in Physics and Biology. NATO Advanced Study Institutes Series (Series B: Physics), vol. 75, pp. 345–367. Springer, Boston (1981). https://doi.org/10.1007/978-1-4684-4106-2_9
Spekreijse, H., Oosting, H.: Linearizing: a method for analysing and synthesizing nonlinear systems. Biol. Cybern. 7(1), 22–31 (1970)
Tranquillo, J.V.: Quantitative Neurophysiology. Synth. Lect. Biomed. Eng. 3(1), 1–142 (2008)
Troyer, T.W., Miller, K.D.: Physiological gain leads to high isi variability in a simple model of a cortical regular spiking cell. Neural Comput. 9(5), 971–983 (1997)
Van Vreeswijk, C., Sompolinsky, H.: Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274(5293), 1724–1726 (1996)
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Cheng, CY., Lu, CC. (2020). The Agility of a Neuron: Phase Shift Between Sinusoidal Current Input and Firing Rate Curve. In: Măndoiu, I., Murali, T., Narasimhan, G., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2019. Lecture Notes in Computer Science(), vol 12029. Springer, Cham. https://doi.org/10.1007/978-3-030-46165-2_2
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DOI: https://doi.org/10.1007/978-3-030-46165-2_2
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