P199 Efficient slope detection with regular spiking and bursting point neuron models
Rebecca Miko*1, Marcus M. Scheunemann2,3, Volker Steuber1, Michael Schmuker1
1Biocomputation Research group, University of Hertfordshire, Hatfield, UK
2Adaptive Systems Research Group, University of Hertfordshire, Hatfield, UK
3Autonomy Department, Dexory, London, United Kingdom
*Email: rebeccamiko@outlook.com
Introduction
In real-world environments, odour stimuli exhibit a complex temporal structure due to turbulent gas dispersion, resulting in intermittent and sparse signals. These turbulence-induced fluctuations can be rapid yet contain valuable information crucial for locating odour sources. This ability is essential for both biological agents in foraging and mate-seeking behaviours, as well as robotic gas sensing in environmental and industrial monitoring. However, omnipresent turbulence destroys concentration gradients. Research suggests that the temporal dynamics of odour signals encode key information about the olfactory scene [1, 2].
Methods
Using the Izhikevich model [3], we develop neurons that spike at the rising edges (Fig. 1a) of naturalistic input signals across varying frequencies. We then compare these neurons to a two-compartmental model [4], which predominantly fires bursts at positive slopes in naturalistic inputs. By analysing the spiking behaviour of both models, we assessed whether bursting mechanisms are necessary for detecting odour signal dynamics
Results
Our findings indicate that a regular spiking neuron can effectively encode the slopes of input signals through discrete spike events, and that these detectors do not need to have the bursting mechanism (Fig. 1b). In contrast, the two-compartmental model [4] predominantly fires bursts in response to rising signal slopes, while the Izhikevich model generates single spikes at these transitions while maintaining computational efficiency. This demonstrates that a simple spiking neuron can capture key temporal features of odour signals without complex bursting dynamics.
Discussion
These results suggest that detecting odour signal slopes does not require burst firing. Instead, regular spiking neurons can efficiently encode temporal features of turbulent odour signals. Given the computational efficiency of the Izhikevich point neuron model, our findings offer potential applications in robotic gas navigation, where rapid and accurate data processing is crucial. By leveraging simple neural mechanisms, future research can explore bio-inspired gas-sensing systems for environmental and industrial monitoring.
Figure 1. Top trace: Gaussian white noise input nA (5 Hz; µ = .006; σ = .015). Bottom trace: membrane potential mV response. Top panel: neuron has parameters {a:0.01,b:0.2,c:- 35,d:5.0}. Asterisks mark burst onsets (grey dotted lines added for clarity). Bursts are defined by ISI ≤ 10 ms. No single spikes were produced. Bottom panel: neuron has parameters {a:0.01,b:0.2,c:-50,d:8.0}. Asterisks mark spikes.
Acknowledgements
Funding received from the NSF/MRC NeuroNex Odor2Action programme 274 (NSF #2014217, MRC #MR/T046759/1).
References
[1] Schmuker, M., Bahr, V., & Huerta, R. (2016). Exploiting plume structure to decode gas source distance using metal-oxide gas sensors. Sensors and Actuators B: Chemical, 235, 636–646
[2] Ackels, T., Erskine, A., Dasgupta, D., et al. (2021). Fast odour dynamics are encoded in the olfactory system and guide behaviour. Nature, 593(7859), 558–563
[3] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569–1572
[4] Kepecs, A., Wang, X. J., & Lisman, J. (2002). Bursting neurons signal input slope. Journal of Neuroscience, 22(20), 9053–9062
