Here’s a practical blueprint for building silicon devices that behave like different neuron classes—not just “a neuron,” but excitatory vs. inhibitory, modulatory (dopamine-like), place/grid cells, etc. Four layers you can actually implement: device, synapse, cell body, and network/topology. Each section includes concrete circuit tricks and materials choices.

0) Guiding idea

Biological diversity = differences in:

• Sign (excite vs inhibit)
• Dynamics (fast spikes, bursts, adaptation, rebound)
• Plasticity (short-term vs long-term, dopamine-gated learning)
• Topology (local microcircuits vs long-range projections)
  We can reproduce those with: mixed-signal CMOS, memristive synapses, phase-change cells, ferroelectric FETs, or spintronic/stochastic devices—plus reconfigurable interconnect.

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1) Device layer (what plays the role of ion channels & synaptic weights)

A. CMOS transistors (baseline)

• Use MOSFETs in subthreshold to get exponential I–V like ion channels.
• Current mirrors + capacitors implement leak, threshold, refractory periods.

B. Memristors / RRAM (weight-in-a-device)

• Conductance = synaptic weight.
• Pulse timing changes conductance → hardware STDP (spike-timing–dependent plasticity).
• Crossbar arrays give thousands of weights per mm² with analog summation (Ohm’s law + KCL).

C. Phase-Change Memory (PCM)

• Partial crystallization encodes analog weights.
• Good for long-term plasticity; pair with CMOS for short-term dynamics.

D. Ferroelectric FETs (FeFET)

• Non-volatile threshold shift → stable weights with low write energy.

E. Spintronic / stochastic devices

• Intrinsic noise and probabilistic switching emulate Poisson spiking and exploration.

F. VO₂ / Mott devices

• Sharp insulator–metal transitions → compact spike generators and oscillators.

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2) Synapse layer (how signals add, adapt, and change sign)

Excitatory vs. inhibitory

• Sign via differential pairs: drive two transconductance paths; choose + or – leg to implement EPSP vs IPSP.
• Or use current steering: excitatory adds current into membrane node; inhibitory sinks it.

Short-term plasticity (STP)

• RC pre-synapse that temporarily scales output (facilitation/depression) based on recent spikes.

Long-term plasticity (LTP/LTD)

• Co-located memristor/PCM updated by pre/post spike timing windows → native STDP.
• For rate-based learning, integrate spike counts into update pulses.

Neuromodulator gating (dopamine/serotonin/NE/ACh)

• Global bias lines that multiply:
  • (a) gain (excitability),
  • (b) learning rate (update step size),
  • (c) eligibility traces (only convert to LTP/LTD when dopamine line goes high).
• Implement with transconductance multipliers or switched-cap gain blocks.

Dendritic nonlinearity

• Compartmental RC trees + local threshold devices for NMDA-like supralinear boost and shunting inhibition.

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3) Cell-body layer (what makes a “neuron type”)

Below, each biological class → circuit recipe.

3.1 Excitatory pyramidal-like cell (cortex/hippocampus)

• Leaky Integrate-and-Fire (LIF) core in subthreshold CMOS.
• Apical/basal compartments as two RC nodes with a coupling conductance → dendritic integration & back-prop spikes.
• Outgoing synapses default positive sign; long-range “axon” is just routing to distant tiles.
• Plasticity: STDP on memristive synapses; ACh line ↑ learning during “attention.”

3.2 Inhibitory interneuron (GABAergic, fast-spiking)

• LIF with short membrane τ (small C, larger leak).
• Output current sinks at targets (negative sign).
• Perisomatic targeting via local routing → implements gain control and rhythm locking (gamma).

3.3 Purkinje-like cell (cerebellum)

• Very fan-in heavy: large crossbar input (thousands of synapses).
• Two input classes:
  • “Parallel fiber” path (weak, numerous) with LTD,
  • “Climbing fiber” path (strong teacher signal) driving complex spikes.
• Learning rule: CF pulses gate LTD on parallel fibers → supervised timing calibration.

3.4 Dopaminergic modulatory neuron (VTA/SNc analog)

• Low fan-in, high fan-out broadcast line.
• Output doesn’t carry features; it scales other neurons’ learning rate & excitability.
• Implement tonic/phasic modes with an oscillator + pulse burst generator; modulate by reward-prediction-error circuit (comparator on expected vs received).

3.5 Serotonergic / noradrenergic / cholinergic modulators

• Similar broadcast cells but tied to different global knobs:
  • 5-HT: increases patience, reduces learning volatility (bias on leak).
  • NE: boosts signal-to-noise (gain on synaptic input).
  • ACh: toggles plasticity on/off & prioritizes bottom-up input.

3.6 Place & grid cells (hippocampus/entorhinal)

• Ring/continuous attractor networks from recurrent excitatory loops + lateral inhibition.
• Grid via multi-oscillator interference (coupled VO₂ or CMOS oscillators) or structured recurrent weights.
• Plastic maps from Hebbian updates when velocity/head-direction inputs are present.

3.7 Spindle (Von Economo)–like “fast decision” cells

• High axonal drive (strong output stage), low input integration depth, short τ → rapid commit.
• Use in salience/intuition modules that shortcut long pipelines.

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4) Network/topology layer (where “brain-like” really shows up)

Local microcircuits

• Tiles of (80% excitatory : 20% inhibitory).
• E→E sparse long-range, I→E/I dense local for balance and oscillations.

Global broadcast buses

• Dedicated metal layers for DA / 5-HT / NE / ACh bias lines.
• Region-specific gains to emulate anatomical gradients.

Rewiring & growth

• Use FPGA-class crossbars or CMOS switch matrices for reconfigurable routing.
• Periodic “sleep cycles”: freeze inference, run consolidation (copy high-confidence weights from SRAM to NVM; prune dead routes).

3D integration

• Stack synapse arrays (RRAM/PCM) over neuron CMOS (face-to-face bonding) → dendrite-like density with low wire length.

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5) Mapping table — neuron type → silicon recipe

Biological type	Device & synapse	Cell dynamics	Plasticity	Modulators	Topology
Excitatory (pyramidal)	RRAM/PCM crossbar (+ sign)	Multi-compartment LIF	STDP/LTP	ACh gates learning	Long-range projections
Inhibitory (interneuron)	Current-sink synapses (– sign)	Fast LIF, short τ	Homeostatic	NE for gain	Dense local targeting
Purkinje	Huge fan-in; two input classes	LIF + complex spike	LTD gated by “teacher”	ACh/NE for timing	Cerebellar microzone pattern
Dopaminergic	CMOS, low fan-in	Burst oscillator (phasic)	— (controls others)	Dopamine line	High fan-out broadcast
Serotonergic	CMOS	Slow tonic	—	5-HT line (stability)	Region-wide
Noradrenergic	CMOS	Phasic attention	—	NE line (SNR)	Region-wide
Grid/Place	Recurrent weights; oscillators	Attractor dynamics	Hebbian	ACh↑ mapping	Layered sheets, loops
Spindle (VEN)	CMOS, strong driver	Fast commit	Reward-tagged	DA gating	Sparse long-range

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6) Learning rules you can implement now

• Pair-based STDP (hardware-friendly): Δw ∝ exp(±Δt/τ).
• Three-factor learning: weight updates require pre, post, and neuromodulator (DA) → matches reward learning.
• Homeostatic scaling: periodically normalize fan-in to prevent runaway.
• Consolidation (“sleep mode”): copy short-term SRAM weights to NVM if eligibility > threshold; prune the rest.

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7) What “neuron diversity” looks like electrically

• Excitatory vs inhibitory: sign of transconductance path.
• Bursting vs tonic: add an adaptation current (slow K⁺ analog) or Mott oscillator.
• Rebound spiking: include T-type Ca²⁺-like negative conductance region (can be mimicked with gm-boosted stages).
• Adaptive threshold: shift comparator threshold with a leaky integrator tied to recent activity.

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8) Toolchain & bring-up plan

1. Start in mixed-signal CMOS (no exotic fab needed).
2. Add a small RRAM/FeFET macro for synapses; validate STDP with timing pulses.
3. Build a cortical tile (1k E, 250 I) + 4 global bias lines.
4. Implement sleep/consolidation firmware to copy/prune.
5. Layer on cerebellar microzone and modulatory nucleus tiles.
6. Add reconfigurable routing (NoC or switch matrices) to emulate long-range tracts.
7. Benchmark on: pattern completion (hippocampus), sequence timing (cerebellum), navigation (grid/place), and reinforcement tasks (dopamine gating).

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9) Limits & gotchas (so you don’t get burned)

• Device drift & variability: embrace it; many brainlike algorithms benefit from noise. Calibrate occasionally.
• IR drop / analog summation errors: tile-local ADCs with modest resolution (6–8b) are usually enough.
• Write endurance (RRAM/PCM): use two-timescale learning; keep fast updates in SRAM “eligibility” and commit sparsely.
• Thermals: modulatory bursts should be brief; schedule “sleep” to cool and consolidate.

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10) TL;DR recipe

• Use CMOS LIF neurons with memristive/PCM synapses.
• Implement sign-selectable synapses (E/I), compartmental dendrites, and global neuromodulator lines that scale gain and learning.
• Shape cell dynamics (bursting, adaptation) with small analog blocks or Mott oscillators.
• Achieve “different neuron types” by combining sign, dynamics, plasticity gate, and topology, not by inventing a brand-new device per type.
• Add sleep/consolidation + pruning to keep the network stable and efficient.

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📚 References & Further Reading

1. Neuromorphic engineering: Artificial brains for artificial intelligence
   https://pmc.ncbi.nlm.nih.gov/articles/PMC11668493/
2. Neuromorphic Computing and Applications: A Topical Review
   https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.70014
3. Hardware implementation of memristor-based artificial neural networks
   https://www.nature.com/articles/s41467-024-45670-9
4. Memristor-based spiking neural networks: cooperative devices and architectures
   https://www.sciencedirect.com/science/article/pii/S270947232400011X
5. Roadmap to neuromorphic computing with emerging technologies
   https://pubs.aip.org/aip/apm/article/12/10/109201/3317314/Roadmap-to-neuromorphic-computing-with-emerging
6. Physics for neuromorphic computing
   https://www.nature.com/articles/s42254-020-0208-2
7. A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing
   https://arxiv.org/abs/1506.01069
8. Hardware design of LIF with Latency neuron model with memristive STDP synapses
   https://arxiv.org/abs/1804.00149
9. Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
   https://arxiv.org/abs/1909.01771
10. A review on memristive hardware for neuromorphic computing
    https://pubs.aip.org/aip/jap/article/124/15/151903/347883/Perspective-A-review-on-memristive-hardware-for
11. Neuromorphic Photonics Circuits: Contemporary Review
    https://www.mdpi.com/2079-4991/13/24/3139
12. Neuromorphic computing at scale
    https://www.nature.com/articles/s41586-024-08253-8
13. A Quarter of a Century of Neuromorphic Architectures
    https://arxiv.org/html/2502.20415v2