change noise term back to the output
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@ -300,10 +300,10 @@ def simulate_fast(rectified_stimulus_array, total_time_s, parameters: np.ndarray
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noise = noise_strength * noise_value / np.sqrt(step_size)
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noise = noise_strength * noise_value / np.sqrt(step_size)
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input_voltage[i] = input_voltage[i - 1] + (
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input_voltage[i] = input_voltage[i - 1] + (
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(-input_voltage[i - 1] + rectified_stimulus_array[i] + noise) / dend_tau) * step_size
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(-input_voltage[i - 1] + rectified_stimulus_array[i]) / dend_tau) * step_size
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output_voltage[i] = output_voltage[i - 1] + ((v_base - output_voltage[i - 1] + v_offset + (
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output_voltage[i] = output_voltage[i - 1] + ((v_base - output_voltage[i - 1] + v_offset + (
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input_voltage[i] * input_scaling) - adaption[i - 1]) / mem_tau) * step_size
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input_voltage[i] * input_scaling) - adaption[i - 1] + noise) / mem_tau) * step_size
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adaption[i] = adaption[i - 1] + ((-adaption[i - 1]) / tau_a) * step_size
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adaption[i] = adaption[i - 1] + ((-adaption[i - 1]) / tau_a) * step_size
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