Neuromorphic computing aims to replicate the efficiency and adaptability of biological neural systems in hardware. Spiking neural networks (SNNs) are a critical component of this approach, leveraging temporal coding and spike-based plasticity to achieve energy-efficient, event-driven computation. Materials that emulate biological neurons and synapses are essential for building such systems. Among the most promising candidates are Mott insulators and organic transistors, which exhibit properties suitable for leaky integrate-and-fire (LIF) neuron models.

Temporal coding in SNNs relies on the precise timing of spikes to encode information, mimicking the way biological neurons communicate. Unlike traditional artificial neural networks that use continuous activation values, SNNs process discrete spikes, reducing energy consumption and enabling asynchronous processing. To implement this, materials must demonstrate volatile switching behavior, where a transient spike triggers a temporary change in conductance. Mott insulators, such as vanadium dioxide (VO2), exhibit an abrupt metal-insulator transition under an applied electric field, making them ideal for emulating neuronal spiking. When a voltage pulse is applied, VO2 transitions from an insulating to a metallic state, producing a spike-like current response before relaxing back to the insulating phase, analogous to the firing and resetting of a biological neuron.

Organic transistors, particularly those based on conjugated polymers, offer another pathway for LIF emulation. These materials can be engineered to exhibit short-term plasticity, where conductance changes decay over time, closely resembling the leaky membrane potential of biological neurons. By tuning the ion mobility and charge trapping mechanisms in organic semiconductors, researchers can control the timescale of conductance decay, matching the millisecond-range dynamics of neural activity. For instance, organic electrochemical transistors (OECTs) leverage ion-electron coupling to achieve gradual relaxation of conductance, enabling faithful reproduction of LIF behavior.

Spike-timing-dependent plasticity (STDP) is a fundamental learning rule in SNNs, where synaptic strength is adjusted based on the relative timing of pre- and post-synaptic spikes. Materials that exhibit memristive switching or analog conductance modulation are critical for implementing STDP. Filamentary-type resistive switching materials, such as Ag-doped chalcogenides, can emulate STDP by modifying conductive filament growth based on spike timing. Phase-change materials (PCMs) like Ge2Sb2Te5 also show promise, as their gradual crystallization dynamics allow for fine-tuned synaptic weight updates.

Benchmarking neuromorphic materials against biological systems requires evaluating key metrics such as energy consumption, spike fidelity, and plasticity dynamics. Biological neurons operate at energies as low as 10 femtojoules per spike, setting a challenging target for artificial implementations. VO2-based neurons have demonstrated spiking energies in the picojoule range, still higher than biology but significantly lower than conventional CMOS approaches. Organic transistors, while slower, offer sub-nanojoule energy consumption due to their low operating voltages and ionic coupling mechanisms.

Temporal precision is another critical factor. Biological neurons exhibit spike timing variability on the order of microseconds, a feature that must be replicated for robust SNN operation. Mott insulators achieve nanosecond-scale switching speeds, exceeding biological requirements, while organic devices typically operate in the kilohertz range, suitable for applications where speed is less critical than energy efficiency.

Plasticity mechanisms in artificial synapses must also align with biological counterparts. Long-term potentiation (LTP) and depression (LTD) in biological synapses involve complex calcium-dependent processes, but materials like PCMs and conductive bridge RAM (CBRAM) can replicate these behaviors through gradual conductance changes. The conductance dynamic range, typically 10-100 in biological synapses, has been matched by various memristive materials, enabling multi-level synaptic weight storage.

Challenges remain in scaling these materials for large-scale SNN implementations. Variability in switching thresholds and endurance limitations must be addressed to ensure reliable operation. Mott insulators suffer from cycle-to-cycle variability due to thermal effects, while organic devices face stability issues under prolonged operation. Hybrid approaches, combining inorganic and organic materials, may offer a solution by leveraging the strengths of each class.

Future directions include integrating these materials into crossbar arrays for parallel synaptic processing and exploring novel architectures such as reservoir computing. The ultimate goal is to achieve neuromorphic systems that not only match biological efficiency but also enable new computing paradigms beyond von Neumann architectures. By continuing to refine material properties and device engineering, SNNs based on Mott insulators and organic transistors could bridge the gap between artificial and biological intelligence.