  Most classical (non-spiking) neural network models disregard internal neuron
dynamics and treat neurons as simple input integrators. However, biological
neurons have an internal state governed by complex dynamics that plays a
crucial role in learning, adaptation and the overall network activity and
behaviour. This paper presents the Membrane Potential and Activation Threshold
Homeostasis (MPATH) neuron model, which combines several biologically inspired
mechanisms to efficiently simulate internal neuron dynamics with a single
parameter analogous to the membrane time constant in biological neurons. The
model allows neurons to maintain a form of dynamic equilibrium by automatically
regulating their activity when presented with fluctuating input. One
consequence of the MPATH model is that it imbues neurons with a sense of time
without recurrent connections, paving the way for modelling processes that
depend on temporal aspects of neuron activity. Experiments demonstrate the
model's ability to adapt to and continually learn from its input.
