  Neural processing on devices and circuits is fast becoming a popular approach
to emulate biological neural networks. Elaborate CMOS and memristive
technologies have been employed to achieve this, including chalcogenide-based
in-memory computing concepts. Here we show that nano-scaled films of
chalcogenide semiconductors can serve as building-blocks for novel types of
neural computations where their tunable electronic and optical properties are
jointly exploited. We demonstrate that ultrathin photoactive cavities of
Ge-doped Selenide can emulate the computationally powerful non-linear
operations of three-factor neo-Hebbian plasticity and the shunting inhibition.
We apply this property to solve a maze game through reinforcement learning, as
well as a single-neuron solution to the XOR, which is a linearly inseparable
problem with point-neurons. Our results point to a new breed of memristors with
broad implications for neuromorphic computing.
