Understanding how biological neurons encode information through neural signaling is crucial in building rich computational hardware neuromorphic systems. This paper creates a neuromorphic neuron model capable of processing and performing complex tasks autonomously and in real-time through neural modulation and dynamic neural encoding. We test the autonomous behavioral capability by mimicking Layer 5 Pyramidal Neurons' (L5PNs) coincident detection encoding behavior using a small network of neurons. The results show that individual output neurons can autonomously encode different received Action Potential (AP) patterns to their time of occurrence into unique output AP patterns. Such property might influence future neuromorphic autonomous encoding systems without the need for a large number of neurons. Implementation of circuits was conducted using the 45nm CMOS technology node, and functional verification is discussed in detail using Cadence Virtuoso Simulator tools.