# Empirical Evaluation & Neuromorphic Viability

To establish the viability of the Spiking Decision Transformer (SNN-DT), we conduct a rigorous ablation study isolating our core neuromorphic components across four standard Gym control tasks: CartPole-v1, MountainCar-v0, Acrobot-v1, and Pendulum-v1.

Our evaluation specifically tracks (1) algorithmic performance and (2) proxy metrics for hardware energy efficiency.

## Downstream Validation Accuracy

We isolate the impact of Phase-Shifted Positional Spiking (Pos-Only) and Dendritic-Style Routing MLP (Route-Only) against a unified configuration (Full) and the base non-augmented LIF formulation (Baseline).

![Offline Loss Validation Curves](images/offline_loss_curves.png)

*Figure 1: Ablation validation loss trajectories. The Full model natively achieves the fastest convergence towards the error floor by exploiting highly diverse temporal encoding and responsive gating.*

| Environment | Baseline | Pos-Only | Route-Only | Full (SNN-DT) |
| :--- | :--- | :--- | :--- | :--- |
| **CartPole-v1** | $452.3 \pm 11.7$ | $474.1 \pm 7.9$ | $479.2 \pm 6.2$ | $\mathbf{492.3 \pm 6.8}$ |
| **MountainCar-v0** | $-120.2 \pm 9.4$ | $-111.5 \pm 7.2$ | $-109.8 \pm 6.9$ | $\mathbf{-102.4 \pm 5.5}$ |
| **Acrobot-v1** | $-87.1 \pm 3.2$ | $-72.0 \pm 3.6$ | $-68.3 \pm 3.9$ | $\mathbf{-59.7 \pm 2.7}$ |
| **Pendulum-v1** | $-155.3 \pm 5.1$ | $-140.0 \pm 4.7$ | $-135.4 \pm 4.4$ | $\mathbf{-130.5 \pm 4.2}$ |

![RL Performance plot](images/rl_performance_plot.png)

*Figure 2: Performance distributions evaluated over the target environments tracking downstream RL validation. The density directly reflects tighter policy resilience in continuous evaluations.*

> **Note:** SNN-DT matches the expressivity capabilities of state-of-the-art dense Decision Transformers while stabilizing sequence variance observed physically out-of-distribution across seeds.

## Energy Profiling & CPU Overhead

On advanced neuromorphic substrates like Intel Loihi or IBM TrueNorth, algorithmic energy scales linearly with spike activity emissions. We compute absolute spike counts during test batches as an energy proxy.

![Spike Emission Distribution](images/spike_histogram.png)

*Figure 3: Histograms of localized sparse spike activity. SNN-DT networks suppress superfluous event spikes effectively limiting output variance beneath the 10-spike barrier compared to unrestricted formulations.*

| Ablation Mode | Spikes / Inference | CPU Latency (ms) |
| :--- | :--- | :--- |
| Baseline | 12,000 | 15.2 |
| Pos-Only | 11,000 | 14.8 |
| Router-Only | 9,000 | 13.5 |
| **Full SNN-DT** | **8,000** | **12.1** |

### Projected Neuromorphic Efficiency
The integrated structure produces a significant efficiency win. The SNN-DT achieves maximal score recovery with only **~8,000 spikes** per sequential forward-pass. 

Assuming a standardized metric of $E_{spike} \approx 5 \text{ pJ}$ observed on dedicated hardware, the projected energy cost sits around **$40 \text{ nJ}$** per decision inference step:

$$ E_{decision} \approx \bar{S} \times E_{spike} \approx 8,000 \times 5\text{ pJ} = 40\text{ nJ} $$

This sub-microjoule boundary unlocks unprecedented application potential for transformer-based inference protocols operating on autonomous drone clusters or wearables edge systems.