P162 Pavlovian Conditioning of a Superburst Generating Neural Network for High-precision Perception of Spatiotemporal Sensory Information
Kyoung J. Lee*¹, Jongmu Kim², Woojun Park¹, Inhoi Jeong¹
¹ Department of Physics, Korea University, Seoul, Korea
² Department of Mechanical Engineering, Korea University, Seoul, Korea
*Email:kyoung@korea.ac.kr
Introduction
How the brain perceives, learns, and distinguishes different spatiotemporal sensory information remains a fundamental yet largely unresolved question in neuroscience [1]. This study demonstrates how an initially random network of Izhikevich neurons can learn, encode, and differentiate time intervals ranging from milliseconds to tens of milliseconds with high temporal precision using a Pavlovian conditioning framework [2]. Notably, our findings highlight the potential role of superbursts in sensory perception, offering new insights into how neural circuits process temporal information.
Methods
Our network model comprises excitatory and inhibitory neurons with synaptic weights evolving through dopamine-modulated spike-timing-dependent plasticity. The conditioning protocol involves sequential electrical stimulation of, for example, three neuron subpopulations (S0, S1, S2) with specific time intervals (Dt1cond.,Dt2cond.), referred to as “target triplet stimulation.” Despite the presence of various distracting stimuli with different time intervals, the network successfully encodes the target stimulation pattern and later responds to it by generating a distinctive population burst—a neuronal spiking avalanche—which acts as a test gauge for perception.
Results
During conditioning, the initially random network evolves into a feedforward structure [3] (Fig. 1A), where three subpopulations (S0, red; S1, blue; S2, green) self-organize according to the imposed time intervals (Dt1cond.,Dt2cond.), effectively encoding temporal information into its network morphology. With axonal conduction delays, the network generates superbursts, featuring multiple sub-burst humps, lasting tens of milliseconds (Fig. 1B). In a perception test, stimuli with varying time intervals and subpopulations produce distinct neuronal avalanches: For example, a network conditioned forDt1cond.=Dt2cond.= 11 ms exhibits systematically varying burst patterns upon receiving different stimulli (Fig. 1B and 1C).
Discussion
These findings provide insight into how seemingly simple neural circuits can encode and process temporal information through structured population spiking activity. Perception in this system can utilize the shape of stimulus-triggered population bursts, allowing for superb temporal resolution (< 1 ms). Furthermore, incorporating axonal conduction delays enables the network to generate superbursts lasting tens of milliseconds, with intricate internal temporal structures, significantly enhancing its perceptual dynamic range. This learning framework can be extended to distinguish much more complex spatiotemporal sequences beyond the simple triplet examples explored in this study.
Figure 1. Fig. 1 Encoding different sets of (Dt_1^cond., Dt_2^cond.) into network morphology (A) and perceptual testing with various (Dt_1^test, Dt_2^test) combinations (B) and subpopulations (C) for the case of (Dt_1^cond.= Dt_2^cond. = 11 ms). In (A), the colored crossbars mark the centroids of S0, S1, and S2 , reflecting the topographic encoding of temporal information (six different cases are shown).
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2024-00335928).
References
1.https://doi.org/10.1016/j.neuron.2020.08.020
2.https://doi.org/10.1093/cercor/bhl152
3.https://doi.org/10.1371/journal.pcsy.0000035
