Most neural networks assume computation is instantaneous: an input arrives, a function runs, an output appears. Even with sequences, time is often modeled indirectly (via windowing, stacking, or recurrence), so the model still reacts rather than exists in time.
This project explores Temporal Neural Networks (TNNs): neurons as continuous-time dynamical systems with internal state and inertia. Instead of y = f(x), the network evolves via dV/dt = f(V, x), and predictions emerge through a settling process - not a single forward pass.
On clean data, TNNs typically match classical accuracy. The difference shows up under real-world stress: noise, missing samples, irregular streams. TNNs produce far fewer prediction flips and degrade more gracefully - stability comes from the computation itself, not post-hoc smoothing.
This project explores Temporal Neural Networks (TNNs): neurons as continuous-time dynamical systems with internal state and inertia. Instead of y = f(x), the network evolves via dV/dt = f(V, x), and predictions emerge through a settling process - not a single forward pass.
On clean data, TNNs typically match classical accuracy. The difference shows up under real-world stress: noise, missing samples, irregular streams. TNNs produce far fewer prediction flips and degrade more gracefully - stability comes from the computation itself, not post-hoc smoothing.