  In this article, we investigate the implications of the unsupervised learning
rule known as Input-Correlations (ICO) learning in the nonlinear dynamics of
two linearly coupled PT-symmetric Li\'enard oscillators. The fixed points of
the oscillator have been evaluated analytically and the Jacobian linearization
is employed to study their stability. We find that on increasing the amplitude
of the external periodic drive, the system exhibits period-doubling cascade to
chaos within a specific parametric regime wherein we observe emergent chaotic
dynamics. We further notice that the system indicates an intermittency route to
chaos in the chaotic regime. Finally, in the period-4 regime of our bifurcation
analysis, we predict the emergence of transient chaos which eventually settles
down to a period-2 oscillator response which has been further validated by both
the maximal Finite-Time Lyapunov Exponent (FTLE) using the well-known
Gram-Schmidt orthogonalization technique and the Hilbert Transform of the
time-series. In the transiently chaotic regime, we deploy the ICO learning to
analyze the time-series from which we identify that when the chaotic evolution
transforms into periodic dynamics, the synaptic weight associated with the
time-series of the loss oscillator exhibits stationary temporal evolution. This
signifies that in the periodic regime, there is no overlap between the filtered
signals obtained from the time-series of the coupled PT-symmetric oscillators.
In addition, the temporal evolution of the weight associated with the stimulus
mimics the behaviour of the Hilbert transform of the time-series.
