Olfactory learning in Drosophila larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory pathway model, which embodies the principles of synaptic plasticity and associative learning through prediction error coding mediated by specific neuromodulating neurons in the mushroom body, like dopaminergic neurons. There is a pressing need to develop novel computational frameworks that capture the spatio-temporal processes while remaining compatible with the constraints of small-scale neural networks. These frameworks should draw inspiration from the biophysical properties of neurons within the olfactory pathway model, enabling accurate emulation of neural dynamics and efficient learning processes using spiking neural networks. This thesis proposes a framework based on a phenomenological conductance-based leaky integrate-and-fire (COBALIF) neuron model, inspired by the olfactory pathway model of Drosophila larvae. By first prototyping the spiking neural network in Intel's Lava Python-based framework, we validated the design on a neuron and system level for a neuromorphic hardware implementation. This was the foundation of a programmable, neuromorphic FPGA architecture capable of adaptive optimization, employed on a Zynq 7000 SoC FPGA. By implementing this architecture in a single-precision floating-point format, we model the real-time neural dynamics of the COBALIF neuron in one-tenth of a millisecond precision. Moreover, our FPGA implementation serves as a feasible prototype for deploying such biologically inspired neurons and their spatio-temporal dependencies in digital design, paving the way for scaling up to small-scale networks.