diff --git a/src/lava/lib/optimization/solvers/generic/solution_receiver/models.py b/src/lava/lib/optimization/solvers/generic/solution_receiver/models.py
index e9949e82..5c1eda10 100644
--- a/src/lava/lib/optimization/solvers/generic/solution_receiver/models.py
+++ b/src/lava/lib/optimization/solvers/generic/solution_receiver/models.py
@@ -136,19 +136,62 @@ def __init__(self, proc):
 
         connection_config = proc.proc_params.get("connection_config")
 
-        weights_state_in = self._get_input_weights(
+        weights_state_in_0 = self._get_input_weights(
             num_vars=num_bin_variables,
             num_spike_int=num_spike_integrators,
             num_vars_per_int=num_message_bits,
+            weight_exp=0
         )
-        self.synapses_state_in = Sparse(
-            weights=weights_state_in,
+        self.synapses_state_in_0 = Sparse(
+            weights=weights_state_in_0,
             #sign_mode=SignMode.EXCITATORY,
             num_weight_bits=8,
             num_message_bits=num_message_bits,
             weight_exp=0,
         )
 
+        weights_state_in_1 = self._get_input_weights(
+            num_vars=num_bin_variables,
+            num_spike_int=num_spike_integrators,
+            num_vars_per_int=num_message_bits,
+            weight_exp=8
+        )
+        self.synapses_state_in_1 = Sparse(
+            weights=weights_state_in_1,
+            #sign_mode=SignMode.EXCITATORY,
+            num_weight_bits=8,
+            num_message_bits=num_message_bits,
+            weight_exp=8,
+        )
+
+        weights_state_in_2 = self._get_input_weights(
+            num_vars=num_bin_variables,
+            num_spike_int=num_spike_integrators,
+            num_vars_per_int=num_message_bits,
+            weight_exp=16
+        )
+        self.synapses_state_in_2 = Sparse(
+            weights=weights_state_in_2,
+            #sign_mode=SignMode.EXCITATORY,
+            num_weight_bits=8,
+            num_message_bits=num_message_bits,
+            weight_exp=16,
+        )
+
+        weights_state_in_3 = self._get_input_weights(
+            num_vars=num_bin_variables,
+            num_spike_int=num_spike_integrators,
+            num_vars_per_int=num_message_bits,
+            weight_exp=24
+        )
+        self.synapses_state_in_3 = Sparse(
+            weights=weights_state_in_3,
+            #sign_mode=SignMode.EXCITATORY,
+            num_weight_bits=8,
+            num_message_bits=num_message_bits,
+            weight_exp=24,
+        )
+
         #CAREFUL! Weights are negated here, since CostIn will always be < 0
         # but SpikeIntegrators only deal with positive numbers.
         # This is accounted for in self._decompress_state()
@@ -186,12 +229,18 @@ def __init__(self, proc):
         )
 
         # Connect the parent InPort to the InPort of the child-Process.
-        proc.in_ports.states_in.connect(self.synapses_state_in.s_in)
+        proc.in_ports.states_in.connect(self.synapses_state_in_0.s_in)
+        proc.in_ports.states_in.connect(self.synapses_state_in_1.s_in)
+        proc.in_ports.states_in.connect(self.synapses_state_in_2.s_in)
+        proc.in_ports.states_in.connect(self.synapses_state_in_3.s_in)
         proc.in_ports.cost_in.connect(self.synapses_cost_in.s_in)
         proc.in_ports.timestep_in.connect(self.synapses_timestep_in.s_in)
 
         # Connect intermediate ports
-        self.synapses_state_in.a_out.connect(self.spike_integrators.a_in)
+        self.synapses_state_in_0.a_out.connect(self.spike_integrators.a_in)
+        self.synapses_state_in_1.a_out.connect(self.spike_integrators.a_in)
+        self.synapses_state_in_2.a_out.connect(self.spike_integrators.a_in)
+        self.synapses_state_in_3.a_out.connect(self.spike_integrators.a_in)
         self.synapses_cost_in.a_out.connect(self.spike_integrators.a_in)
         self.synapses_timestep_in.a_out.connect(self.spike_integrators.a_in)
 
@@ -204,7 +253,7 @@ def __init__(self, proc):
         proc.vars.best_cost.alias(self.solution_receiver.best_cost)
 
     @staticmethod
-    def _get_input_weights(num_vars, num_spike_int, num_vars_per_int):
+    def _get_input_weights(num_vars, num_spike_int, num_vars_per_int, weight_exp):
         """To be verified. Deprecated due to efficiency"""
 
         weights = np.zeros((num_spike_int, num_vars), dtype=np.int8)
@@ -212,13 +261,14 @@ def _get_input_weights(num_vars, num_spike_int, num_vars_per_int):
         #print(f"{num_spike_int=}")
         #print(f"{num_vars_per_int=}")
         for spike_integrator in range(2, num_spike_int - 1):
-            variable_start = num_vars_per_int*spike_integrator
+            variable_start = 32 * (spike_integrator - 2) + weight_exp
             weights[spike_integrator, variable_start:variable_start +
-                                                     num_vars_per_int] = 1
+                                                     9] = np.power(2, np.arange(8))
         # The last spike integrator might be connected by less than
         # num_vars_per_int neurons
         # This happens when mod(num_variables, num_vars_per_int) != 0
-        weights[-1, num_vars_per_int*(num_spike_int - 3):] = 1
+        variable_start = 32 * (num_spike_int - 3) + weight_exp
+        weights[-1, variable_start:] = np.power(2, np.arange(weights.shape[1]-variable_start))
 
         #print("=" * 20)
         #print(f"{weights=}")