diff --git a/models/FirerateModel.py b/models/FirerateModel.py
new file mode 100644
index 0000000..4455eef
--- /dev/null
+++ b/models/FirerateModel.py
@@ -0,0 +1,76 @@
+from stimuli.AbstractStimulus import AbstractStimulus
+from models.AbstractModel import AbstractModel
+import numpy as np
+from functions import full_boltzmann
+
+
+class FirerateModel(AbstractModel):
+    KEYS = ["function_params", "f_zero", "adaptation_factor", "a_zero", "a_tau", "step_size"]
+    VALUES = [[200, 0, 0.5, 0], -np.inf, 0.05, 0, 8, 0.01]
+
+    def __init__(self, params: dict = None):
+        super().__init__(params)
+
+        self.frequency_trace = []
+        self.adaption_trace = []
+        self.stimulus = None
+
+    def simulate(self, stimulus: AbstractStimulus, total_time_s):
+        self.stimulus = stimulus
+        if self.parameters["f_zero"] is -np.inf:
+            current_freq = self.frequency_step(stimulus.value_at_time_in_ms(0))
+        else:
+            current_freq = self.parameters["f_zero"]
+
+        current_adaptation = self.parameters["a_zero"]
+
+        freq_trace = []
+        a_trace = []
+
+        for time_point in np.arange(0, total_time_s * 1000, self.parameters["step_size"]):
+            freq_trace.append(current_freq)
+            a_trace.append(current_adaptation)
+
+            current_stimulus = stimulus.value_at_time_in_ms(time_point) - current_adaptation
+            current_freq = self.frequency_step(current_stimulus)
+            current_adaptation = self.adaptation_step(current_adaptation, current_freq)
+
+        self.frequency_trace = freq_trace
+        self.adaption_trace = a_trace
+
+        return freq_trace
+
+    def adaptation_step(self, current_a, current_f):
+        alpha = self.parameters["adaptation_factor"]
+        tau = self.parameters["a_tau"]
+
+        next_a = current_a + ((-current_a + alpha * current_f) / tau) * self.parameters["step_size"]
+        return next_a
+
+    def frequency_step(self, current_stimulus):
+        params = self.parameters["function_params"]
+        return full_boltzmann(current_stimulus, params[0], params[1], params[2], params[3])
+
+    def simulates_voltage_trace(self) -> bool:
+        return False
+
+    def simulates_spiketimes(self) -> bool:
+        return False
+
+    def simulates_frequency(self) -> bool:
+        return True
+
+    def get_voltage_trace(self):
+        raise NotImplementedError("This model type doesn't simulate a voltage trace!")
+
+    def get_spiketimes(self):
+        raise NotImplementedError("This model type doesn't simulate spiketimes!")
+
+    def get_frequency(self):
+        return self.frequency_trace
+
+    def min_stimulus_strength_to_spike(self):
+        pass
+
+    def get_sampling_interval(self):
+        return self.parameters["step_size"]