Determinants of input amplitude and slope detection in bursting neurons
Creators: name: Miko, Rebecca
name: Steuber, Volker
name: Schmuker, Michael
Publisher: name: Springer Nature
Year: 2023

description: Turbulence in natural environments causes a rich temporal structure in gas stimuli, which contains information that may aid in gas-based navigation [1, 2]. Research has suggested that animals exploit this structure to locate odour sources [3] and that some of this information lies in the temporal dynamics of the signals [2]. Furthermore, a study found that particular spatial and temporal features in neuronal inputs trigger bursts [4], which would suggest that these firing patterns represent a particular form of neural code. We investigated if and to what extent a bursting neuron that implements the Izhikevich differential equations [5] can detect amplitudes and slopes of a naturalistic input signal.

The input signal was injected directly into each neuron as Gaussian white noise (sampling rate fs = 400 Hz, μ = 0.003, and σ = 0.005) with a 25 Hz Butterworth low-pass filter. We recorded bursts which we defined as having <10ms intervals between spikes.

We then recorded the lengths of the bursts, for example, 2- or 3-spike bursts, to understand the distribution of burst density over the amplitudes and the slopes in the signal. From there, we created receiver operator characteristic (ROC) curves for the 2- and 3-spike burst distributions. Our fitness function was the area under the curve (AUC), where higher AUC values tell us that the neuron is more successful at distinguishing between different slopes or amplitudes. We found that, depending on the parameters, the neurons can detect either the slope of the signal, or the amplitude, or both.

These neurons were successful when presented with a generated naturalistic input signal. We plan to extend this research by investigating if and to what extent these neurons can detect amplitudes and slopes of an input signal which has been recorded directly from a neuromorphic sensor called an electronic nose (e-nose) within a real-world environment. Wind conditions, temperature and sensor time lags are some of the biggest challenges we will face. It would be interesting to see how our neurons perform in these conditions and what adjustments need to be made in order to be successful at detecting signal slopes and amplitudes with real-world data.

 

References

[1] Celani, A., Villermaux, E. and Vergassola, M.: Odor landscapes in turbulent environments. Physical Review X, 4(4), p.041015, 2014.

[2] Schmuker, M., Bahr, V. and Huerta, R.: Exploiting plume structure to decode gas source distance using metal-oxide gas sensors. Sensors and Actuators B: Chemical, 235, pp.636-646, 2016.

[3] Jacob, V., Monsempès, C., Rospars, J.P., Masson, J.B. and Lucas, P.: Olfactory coding in the turbulent realm. PLoS Computational Biology, 13(12), p.e1005870, 2017.

[4] Kepecs, Adam, Xiao Jing Wang, and John Lisman (2002). “Bursting neurons signal input slope”. In: Journal of Neuroscience 22.20, pp. 9053–9062. issn: 02706474. doi: 10.1523/jneurosci.22-20-09053.2002.

[5] Izhikevich, Eugene M. (2003). “Simple model of spiking neurons”. In: IEEE Transactions on Neural Networks 14.6, pp. 1569–1572. issn: 10459227. doi: 10.1109/TNN.2003.820440.