Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals

The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a Pavlovian-conditioned spiking neural network model that may help elucidate these mechanisms. Using "three-factor learning rule," we conditioned an initially random spiking neural network to discriminate a specific spatiotemporal stimulus - a sequence of two or three pulses delivered within <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo> <mml:mn>10</mml:mn></mml:mrow> </mml:math> ms to two or three distinct neuronal subpopulations - from other pulse sequences differing by only a few milliseconds. Through conditioning, a feedforward structure emerges that encodes the target pattern's temporal information into specific topographic arrangements of stimulated subpopulations. In the readout phase, discrimination of different inputs is achieved by evaluating the shape and peak-shift characteristics of the spike density functions (SDFs) of input-triggered population bursts. The network's dynamic range - defined by the duration over which pulse sequences are processed accurately - is limited to around 10 ms, as determined by the duration of the input-triggered population burst. However, by introducing axonal conduction delays, we show that the network can generate "superbursts," producing a more complex and extended SDF lasting up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∼</mml:mo></mml:math> 30 ms, and potentially much longer. This extension effectively broadens the network's dynamic range for processing temporal sequences. We propose that such conditioning mechanisms may provide insight into the brain's ability to perceive and interpret complex spatiotemporal sensory information encountered in real-world contexts.