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diff --git a/files/data_io/encoders/poisson_encoder.py b/files/data_io/encoders/poisson_encoder.py
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+++ b/files/data_io/encoders/poisson_encoder.py
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+from typing import Optional, Union
+import numpy as np
+import torch
+from .base_encoder import BaseEncoder
+
+class PoissonEncoder(BaseEncoder):
+    r"""
+    PoissonEncoder: convert static intensities to spike trains via per-time-step Bernoulli sampling.
+
+    Given a static input vector x \in [0,1]^{D}, we produce a spike tensor S \in {0,1}^{T \times D}
+    by sampling at each time step t and dimension d:
+        S[t, d] ~ Bernoulli( p[d] ),  where  p[d] = clip( x[d] * max_rate * (dt_ms / 1000), 0, 1 ).
+
+    Parameters
+    ----------
+    max_rate : float
+        Maximum firing rate under unit intensity (Hz). Typical: 20~200. Default: 20.
+    T : int
+        Number of discrete time steps in the encoded spike train. Default: 50.
+    dt_ms : float
+        Time resolution per step in milliseconds. Default: 1.0 ms.
+        Effective per-step probability uses factor (dt_ms/1000) to convert Hz to per-step probability.
+    seed : int or None
+        Optional RNG seed for reproducibility. If None, uses global RNG state.
+
+    Notes
+    -----
+    - Input `data` is expected to be a NumPy 1D array (shape [D]) or 2D array ([B, D]).
+      If 1D, we return S with shape (T, D).
+      If 2D (batched), we broadcast probabilities across batch and return (T, B, D).
+    - Intensities outside [0,1] will be clipped to [0,1].
+    - Device of returned tensor follows torch default device (CPU) unless you move it later.
+    """
+
+    def __init__(self, max_rate: float = 20.0, T: int = 50, dt_ms: float = 1.0, seed: Optional[int] = None):
+        super().__init__()
+        self.max_rate = float(max_rate)
+        self.T = int(T)
+        self.dt_ms = float(dt_ms)
+        self.seed = seed
+        # local generator for reproducibility if seed is provided
+        self._g = torch.Generator()
+        if seed is not None:
+            self._g.manual_seed(int(seed))
+
+    def _ensure_numpy(self, data: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
+        if isinstance(data, torch.Tensor):
+            data = data.detach().cpu().numpy()
+        return np.asarray(data)
+
+    def encode(self, data: Union[np.ndarray, torch.Tensor]) -> torch.Tensor:
+        """
+        Convert input intensities to Poisson spike trains.
+
+        Parameters
+        ----------
+        data : np.ndarray or torch.Tensor
+            Shape [D] or [B, D]. Values are assumed in [0,1] (we clip if not).
+
+        Returns
+        -------
+        spikes : torch.Tensor
+            Shape (T, D) or (T, B, D), dtype=torch.float32 with values {0.,1.}.
+        """
+        x = self._ensure_numpy(data)
+        # clip to [0,1]
+        x = np.clip(x, 0.0, 1.0)
+
+        # compute per-step probability
+        p_step = float(self.max_rate) * (self.dt_ms / 1000.0)  # probability per step for unit intensity
+        # probability tensor (broadcast-friendly)
+        probs = x * p_step
+        probs = np.clip(probs, 0.0, 1.0)
+
+        # create Bernoulli samples for T steps
+        # If input is 1D: probs.shape = (D,) -> output (T, D)
+        # If input is 2D: probs.shape = (B, D) -> output (T, B, D)
+        if probs.ndim == 1:
+            D = probs.shape[0]
+            probs_t = np.broadcast_to(probs, (self.T, D))  # (T, D)
+            probs_t = torch.from_numpy(probs_t.astype(np.float32))
+            spikes = torch.bernoulli(probs_t, generator=self._g)
+            return spikes
+        elif probs.ndim == 2:
+            B, D = probs.shape
+            probs_t = np.broadcast_to(probs, (self.T, B, D))  # (T, B, D)
+            probs_t = torch.from_numpy(probs_t.astype(np.float32))
+            spikes = torch.bernoulli(probs_t, generator=self._g)
+            return spikes
+        else:
+            raise ValueError(f"PoissonEncoder expects data with ndim 1 or 2, got shape {probs.shape}")