Encoding and tuning curves.

The top panel shows the input signal and the target x-intercept, the state space value at which our neurons should begin spiking. The middle panel shows neural activity over time and the target y-intercept, the desired activity of our neurons when the input is at its maximum value. The bottom panel shows the tuning curves derived from these data. All four neuron models exhibit minimal spiking activity when the input is below the target x-intercept; neural activities also increase as the value of the input increases, up to the target y-intercept. Shaded error regions indicate 95% confidence intervals for smoothed activity, and demonstrate that all simulated neuron models have a natural variation in firing rate for any given state space value x. The significant overlap between the four trained neuron models and the target (ReLU) activities shows the success of our online learning rule.