  Nonlinear photonic sources including semiconductor lasers have recently been
utilized as ideal computation elements for information processing. They supply
energy-efficient way and rich dynamics for classification and recognition
tasks. In this work, we propose and numerically study the dynamics of complex
photonic systems including high-$\beta$ laser element with delayed feedback and
functional current modulation, and employ nonlinear laser dynamics of
near-threshold region for the application in time-delayed reservoir computing.
The results indicate a perfect (100$\%$) recognition accuracy for the pattern
recognition task, and an accuracy of about 98$\%$ for the Mackey-Glass chaotic
sequences prediction. Therefore, the system shows an improvement of performance
with low-power consumption, in particular, the error rate is an order of
magnitude smaller in comparison with previous works. Furthermore, by changing
the DC pump, we are able to modify the amount of spontaneous emission photons
of the system, this then allow us to explore how the laser noise impact the
performance of the reservoir computing system. Through manipulating these
variables, we show a deeper understanding on the proposed system, which is
helpful for the practical applications of reservoir computing.
