diff --git a/rectipy/utility.py b/rectipy/utility.py
index ffdf7d8..047e10b 100644
--- a/rectipy/utility.py
+++ b/rectipy/utility.py
@@ -113,6 +113,43 @@ def circular_connectivity(N: int, p: float, spatial_distribution: rv_discrete, h
     return C
 
 
+def line_connectivity(N: int, p: float, spatial_distribution: rv_discrete, homogeneous_weights: bool = True) -> np.ndarray:
+    """Generate a coupling matrix between nodes aligned on a circle.
+
+    Parameters
+    ----------
+    N
+        Number of nodes.
+    p
+        Connection probability.
+    spatial_distribution
+        Probability distribution defined over space. Will be used to draw indices of nodes from which each node in the
+        circular network receives inputs.
+    homogeneous_weights
+
+    Returns
+    -------
+    np.ndarray
+        2D coupling matrix (N x N).
+    """
+    C = np.zeros((N, N))
+    n_conns = int(N*p)
+    for n in range(N):
+        idxs = spatial_distribution.rvs(size=n_conns)
+        signs = 1 * (bernoulli.rvs(p=0.5, loc=0, size=n_conns) > 0)
+        signs[signs == 0] = -1
+        conns = n + idxs*signs
+        conns = conns[conns > 0]
+        conns = conns[conns < N]
+        conns_unique = np.unique(conns)
+        if homogeneous_weights:
+            C[n, conns_unique] = 1.0/len(conns_unique)
+        else:
+            for idx in conns_unique:
+                C[n, idx] = np.sum(conns == idx)/len(conns)
+    return C
+
+
 def random_connectivity(n: int, m: int, p: float, normalize: bool = True) -> np.ndarray:
     """Generate a random coupling matrix.