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diff --git a/spires_memristor_sim/README.md b/spires_memristor_sim/README.md
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+# Spires reservoir simulated on memristor crossbar using memTorch
+
+## Goal
+The goal is to able to simulate a spires reservoir on a memristor crossbar using
+a custom memristor spice model(HIL), thus you can run multiple simulations with 
+multiple models to compare how each performs using benchmark results.
+
+## task
+The plan right now is to give the cart-pole task for benchmarking.
+
+## metrics
+
diff --git a/spires_memristor_sim/memTorch_SNN.py b/spires_memristor_sim/memTorch_SNN.py
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+++ b/spires_memristor_sim/memTorch_SNN.py
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+import torch
+import memtorch
+
+# 1. Standard memTorch setup (Static crossbar weights)
+ann_layer = torch.nn.Linear(100, 10)
+patched_layer = memtorch.mn.Module.patch_model(ann_layer, memristor_model)
+
+# 2. Your custom SNN wrapper loop
+def forward_snn(input_spikes_over_time):
+    # input_spikes_over_time shape: (time_steps, batch_size, input_dim)
+    time_steps = input_spikes_over_time.shape[0]
+    v_mem = torch.zeros(batch_size, 10) # Hidden neuron membrane potentials
+    output_spikes = []
+
+    for t in range(time_steps):
+        # Pass binary spikes through memTorch's physical crossbar simulation
+        current_in = patched_layer(input_spikes_over_time[t])
+        
+        # Leaky Integrate-and-Fire (LIF) logic (Written by you!)
+        v_mem = 0.9 * v_mem + current_in  # Leak & Integrate
+        
+        # Fire threshold
+        spike = (v_mem >= 1.0).float()
+        v_mem[v_mem >= 1.0] = 0.0         # Reset
+        
+        output_spikes.append(spike)
+        
+    return torch.stack(output_spikes)
diff --git a/spires_memristor_sim/spires_interface.py b/spires_memristor_sim/spires_interface.py
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+++ b/spires_memristor_sim/spires_interface.py
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+import ctypes
+import numpy
+import torch
+
+spires_lib = ctypes.CDLL("../spires/build/.libspires.so")
+
+spires_lib.spires_reservoir_step.argtypes = [ctypes.POINTER(ctypes.c_float)]
+spires_lib.spires_reservoir_step.restype = None
+
+#change currents from tensor to a flat C pointer array for spires to read
+def send_currents_to_spires(currents):
+    # ----- extract from pytorhc graph -----
+    # .detach() removes it from auto gradient tracking
+    # .cpu() make sure data is in RAM, not VRAM
+    # .numpy() maps it to numpy array
+    numpy_array = torch_tensor.detach().cpu().numpy()
+
+    # ----- makes suren layout matches 32 bit float C-array -----
+    # .astype(npfloat32) forces standard CC float precistion
+    # .flatten() makes sure the memory is a 1D block
+    contiguous_array = numpy_array.astype(np.float32).flatten()
+
+    # ----- Ensure raw memory pointer -----
+    c_float_ptr = contiguous_array.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
+    array_size = contiguous_array.size
+
+    # ----- Call the C library -----
+    spires_lib.spires_reservoir_step(c_float_ptr, array_size)
+    return contiguous_array
+
+    # NEED TO MODIFY SPIRES FOR THIS?? :((
+
+def read_spikes_from_spires():
+    placeholder = 0
+    return 0
diff --git a/spires_memristor_sim/torch_reservoir.py b/spires_memristor_sim/torch_reservoir.py
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+++ b/spires_memristor_sim/torch_reservoir.py
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+import torch 
+import memtorch
+from spires_interface import (
+        send_currents_to_spires,
+        read_spikes_from_spires,
+)
+
+NUM_INPUTS = 4
+NUM_NEURONS = 800
+
+input_layer = torch.nn.Linear(NUM_INPUTS, NUM_NEURONS, bias=False)
+#this is a recurrent layer sort of???
+reservoir_layer = torch.nn.Linear(NUM_NEURONS, NUM_NEURONS, bias=False)
+
+#make the reservoir sparse and random
+with torch.no_grad():
+    reservoir_layer.weight.data.normal_(0.0, 0.5) #random weight
+
+    #mask so 10% of connections exist
+    mask = (torch.rand(NUM_NEURONS, NUM_NEURONS) < 0.10).float()
+    reservoir_layer.weight.data *= mask
+
+# patch layers into memristor crossbars with memTorch
+memristor_model = memtorch.bh.memristor.VTEAM
+mem_input_layer = memtorch.mn.Module.patch_model(input_layer, memristor_model)
+mem_reservoir_layer = memtorch.mn.Module.patch_model(reservoir_layer, memristor_model)
+
+#run spiking loop with spires
+previous_spikes = torch.zeros(1, NUM_NEURONS)
+for step in range(500):
+    input_tensor = torch.tensor(cartpole_state).float().unsqueeze(0)
+    currents_in = mem_input_layer(input_tensor)
+
+    currents_recv = mem_reservoir_layer(previous_spikes)
+
+    total_currents = currents_in + currents_recv
+    
+    send_currents_to_spires(total_currents)
+
+    current_spikes_np = read_spikes_from_spires()
+    previous_spikes = torch.from_numpy(current_spikes_np).float().unsqueeze(0)
+
+#pass current spikes np to readout layer