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When less is more: non-monotonic spike processing in neurons

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Most neurons in the nervous system communicate by sending and receiving stereotyped electrical pulses called action potentials or spikes. The computational capabilities of neural circuits centrally rely on the input-output relations of single neurons. This relation is commonly characterized by its output spike rate in response to a temporally continuous constant input current, sometimes in the presence of additional current fluctuations [1]. If the spikes each neuron receives are irregular in time and individually only weakly affect the neuron's membrane potential, this continuous-input picture serves as an appropriate approximation [2] to the actual spike sequence input. As a consequence, a neuron's response curve in terms of its output spike rate, increasing monotonically as a function of input current, is considered one of its most important standard characteristics. Yet, a broad range of neural systems exhibit more regular, patterned spike sequences. Some experimental and numerical studies [35] suggest that certain neurons receiving correlated spiking inputs may exhibit non-monotonic input-output relations. Here we systematically analyze the stationary spiking response of neurons to regular spiking inputs and reveal that it is generically non-monotonic. Our theoretical analysis shows that the underlying mechanism relies solely on a combination of the discrete nature of the communication by spikes and limited resources required for spike processing. Numerical simulations of mathematically idealized and biophysically detailed models, as well as neurophysiological experiments confirm our theoretical predictions (Figure 1). Non-monotonic response to regular spiking inputs thus is a generic feature common across spiking neurons.

Figure 1
figure1

Non-monotonic response to regular spiking inputs as observed in neurons from the medial nucleus of the trapezoid body (MNTB) of rats. Shown is the measured output rate in dependence of the input rate of the neuron. Dashed lines indicate theoretical predictions (parameter-free), colored data results from experiment. Different colors code for different input strength of the spiking input.

References

  1. 1.

    Dayan P, Abbott LF: Theoretical Neuroscience. 2001, Cambridge: MIT Press

  2. 2.

    Brunel N: Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci. 2000, 8: 183-10.1023/A:1008925309027.

  3. 3.

    Perkel DH, Schulmann JH, Bullock TH, Moore GP, Segundo JP: Pacemaker neurons: Effects of regularly spaced synaptic input. Science. 1964, 145: 61-10.1126/science.145.3627.61.

  4. 4.

    de la Rocha J, Parga N: Short-term synaptic depression causes a non-monotonic response to correlated stimuli. J Neurosci. 2005, 25: 8416-8431. 10.1523/JNEUROSCI.0631-05.2005.

  5. 5.

    Giugliano M, la Camera G, Rauch A, Lüscher H-R, Fusi S: Non-monotonic current-to-rate response function in a novel integrate-and-fire model neuron. LNCS. 2002, 2415: 141-146.

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Acknowledgements

We acknowledge partial support by the BMBF under grant number 01GQ1005B.

Author information

Correspondence to Hinrich Arnoldt.

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Arnoldt, H., Jahnke, S., Happ, L. et al. When less is more: non-monotonic spike processing in neurons. BMC Neurosci 14, P389 (2013) doi:10.1186/1471-2202-14-S1-P389

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Keywords

  • Sequence Input
  • Electrical Pulse
  • Neural Circuit
  • Input Current
  • Computational Capability