nsram 0.5.0

NS-RAM: Neuro-Synaptic RAM simulator — floating-body transistor neuron/synapse modeling with charge-trapping plasticity. 14 modules, 5 neuron models, 10 RC benchmarks, spike encoding, analysis tools. 97% XOR, 96.75% MNIST.

nsram — Neuro-Synaptic RAM Simulator

The first open-source Python library for simulating NS-RAM floating-body transistor neurons with charge-trapping synaptic plasticity.

Based on: Pazos et al., "Synaptic and neural behaviours in a standard silicon transistor", Nature 640, 69-76 (2025).

Installation

pip install nsram              # CPU only (numpy + scipy)
pip install nsram[gpu]         # GPU acceleration (PyTorch CUDA/ROCm/MPS)
pip install nsram[all]         # Everything (GPU + plotting)

Quickstart

from nsram import NSRAMReservoir, rc_benchmark

# Create 128-neuron reservoir with heterogeneous short-term plasticity
res = NSRAMReservoir(N=128, stp='heterogeneous')

# Run reservoir computing benchmarks
results = rc_benchmark(res)
# XOR-1: 90.3%, MC: 2.29, NARMA-10: 0.52, Wave-4: 92.5%

Key Results

Benchmark	NS-RAM (N=2000)	ESN (tanh)	Izhikevich	PLIF
XOR-1	97.0%	50.5%	50.2%	50.5%
Waveform-4	97.6%	69.8%	34.2%	42.6%
NARMA-10	0.358	0.000	0.000	0.000
Memory Capacity	3.38	4.13	0.02	1.35
Kernel Rank	230	12	213	3
MNIST (5K neurons)	96.75%	—	—	—

What is NS-RAM?

NS-RAM (Neuro-Synaptic Random Access Memory) is a standard CMOS floating-body transistor that exhibits both neuron-like spiking and synapse-like plasticity from device physics alone — no special materials or processes required.

Key device physics simulated:

• Impact ionization avalanche (Chynoweth model): I_aval = I0 × exp((Vcb - BVpar) / Vt)
• Breakdown voltage control: BVpar = 3.5 - 1.5 × Vg1 (gate-tunable, from Pazos SPICE)
• Temperature dependence: BVpar(T) = BVpar × (1 - 21.3μ × ΔT)
• SRH charge trapping: dQ/dt = k_cap(Vg2) × (1-Q) × rate - k_em × Q
• VG2-controlled mode switching (neuron ↔ synapse)

Library Overview (v0.5.0)

14 modules, 4,034 lines, 81 public exports.

Core Simulation

Module	Description
nsram.physics	Full device physics — SPICE-matched avalanche, SRH trapping, body-charge ODE
nsram.neuron	Single-cell ODE simulation (scipy), IV curves, parameter extraction
nsram.network	GPU-accelerated NS-RAM spiking network (10K+ neurons)
nsram.reservoir	High-level reservoir computing API
nsram.vision	Batch GPU image classifier — 96.75% MNIST at 5K neurons

Alternative Neuron Models

Model	Class	Description
Izhikevich	IzhikevichNetwork	20+ firing patterns (RS, IB, CH, FS, LTS, TC, RZ, mixed)
Parametric LIF	PLIFNetwork	Learnable time constants (Wu et al. 2021)
Hodgkin-Huxley	HHNetwork	4-variable biophysical gold standard

from nsram import IzhikevichNetwork, PLIFNetwork, HHNetwork

iz = IzhikevichNetwork(N=1000, preset='mixed')
result = iz.run(signal)  # → dict with 'states', 'spikes'

Benchmarks

Benchmark	Function	Reference
Temporal XOR	xor_accuracy()	Delay-τ nonlinear memory
Memory Capacity	memory_capacity()	Jaeger 2001
NARMA-N	narma_prediction()	Atiya & Parlos 2000
Waveform Classification	waveform_classification()	N-class temporal
Mackey-Glass	mackey_glass()	Chaotic time series prediction
Kernel Rank	kernel_rank()	Nonlinear transformation capacity
Generalization Rank	generalization_rank()	Generalization vs memorization
Nonlinear Memory	nonlinear_memory_capacity()	Memory × nonlinearity tradeoff

from nsram import mackey_glass, kernel_rank, nonlinear_memory_capacity

mg_r2 = mackey_glass(states, washout=500, tau=17)
kr = kernel_rank(states, washout=500)
nmc = nonlinear_memory_capacity(states, inputs, washout=500)

Spike Encoding & Decoding

Encoder	Function	Description
Rate (Poisson)	rate_encode()	Firing probability ∝ input value
Latency	latency_encode()	Time-to-first-spike
Delta	delta_encode()	DVS-style change detection
Population	population_encode()	Gaussian receptive fields
Phase	phase_encode()	Phase-of-firing coding

from nsram import rate_encode, latency_encode, population_encode

spikes = rate_encode(image, n_steps=100, gain=1.0)
ttfs = latency_encode(features, n_steps=50, tau=5.0)
pop = population_encode(signal, n_neurons=20)

Analysis & Visualization

Tool	Function	Description
Firing rate	firing_rate()	Per-neuron, sliding window
ISI statistics	isi_statistics()	Mean, CV, burst ratio
Fano factor	fano_factor()	Spike count variability
Correlation	correlation_matrix()	Pairwise spike correlation
Synchrony	synchrony_index()	Population synchrony measure
Entropy	spike_entropy()	Shannon entropy of spike trains
Transfer entropy	transfer_entropy()	Directed information flow
Avalanche analysis	avalanche_analysis()	Power-law criticality test
Effective dimension	effective_dimension()	State space dimensionality
Lyapunov exponent	lyapunov_estimate()	Chaos detection
Raster plot	raster_plot()	Publication-quality spike raster
ISI histogram	isi_histogram()	ISI distribution visualization

from nsram import raster_plot, avalanche_analysis, effective_dimension

raster_plot(spikes, title='NS-RAM Network', save_path='raster.png')
aval = avalanche_analysis(spikes)
print(f"Critical: {aval['is_critical']}, α = {aval['size_exponent']:.2f}")
ed = effective_dimension(states)

Learning Rules

Hardware-realistic (tapeout-compatible):

Rule	Class	Reference
STDP	STDP	Bi & Poo 1998
Reward-STDP	RewardSTDP	Three-factor rule, eligibility traces
Voltage-STDP	VoltageSTDP	Clopath et al. 2010, uses body potential
Homeostatic	HomeostaticPlasticity	Intrinsic plasticity via charge trapping
Forward-Forward	ForwardForward	Hinton 2022, goodness = spike rate²
Equilibrium Propagation	EquilibriumPropagation	Scellier & Bengio 2017
e-prop	Eprop	Bellec et al. 2020, eligibility propagation

Export & Integration

from nsram import to_brian2, to_nestml, to_spice_subcircuit

brian2_eqs = to_brian2()           # Brian2 NeuronGroup equations
nestml_model = to_nestml()         # NESTML model for NEST HPC
spice_subckt = to_spice_subcircuit()  # SPICE .subckt for any simulator

SPICE Bridge

from nsram import NSRAMCell, simulate_iv_curve

cell = NSRAMCell()
cell.generate_netlist('nsram_1t.spice', Vg1=0.3)
iv_data = simulate_iv_curve(Vg1=0.3)  # Runs ngspice

Novel Finding: Charge Trapping = Tsodyks-Markram STP

This library implements the analytical mapping between NS-RAM charge trapping and the Tsodyks-Markram short-term plasticity model:

NS-RAM physics	TM-STP neuroscience
Q (trapped charge)	1 − x (depleted resources)
k_cap(VG2)	U (utilization)
1/k_em	τ_rec (recovery time)
ΔVth = −αQ	PSP amplitude modulation

VG2 voltage controls the STP type: low VG2 → depression, high VG2 → facilitation.

Device Parameters

Default parameters from Pazos et al. SPICE model (Zenodo: 13843362):

Parameter	Value	Source
BVpar₀	3.5 V	BJTparams.txt
dBVpar/dVg1	-1.5 V/V	BJTparams.txt
Tbv1	-21.3 μ/K	Davalanche.txt
Is	1×10⁻¹⁶ A	BJTparams.txt
Bf	50	BJTparams.txt
Vth₀ (NMOS)	0.432 V	PTM130bulk_lite.txt
Energy/spike	21 fJ	Nature 640
Cell area (1T)	8 μm²	Nature 640
Cell area (2T)	17 μm²	Nature 640

Backends

Backend	Devices	Detection
NumPy	CPU (any)	Always available
PyTorch CUDA	NVIDIA GPUs	torch.cuda.is_available()
PyTorch ROCm	AMD GPUs (gfx1100, gfx1151, ...)	torch.cuda.is_available() via HIP
PyTorch MPS	Apple Silicon	torch.backends.mps.is_available()

Examples

See examples/ for 17 runnable scripts:

Script	Description
quickstart.py	Minimal hello-world
architecture_comparison.py	5 neuron models × 10 benchmarks × 3 scales
mnist_scaling.py	MNIST accuracy vs neuron count (96.75% at 8K)
brain_arena.py	90K-neuron cortical brain foraging in 2D arena
brain_plays_pong.py	50K-neuron brain playing Pong
cortical_breakout.py	200K cortical brain on Breakout
learning_benchmark.py	STDP, R-STDP, V-STDP comparison
onchip_mnist.py	Forward-Forward vs EP vs e-prop on MNIST
large_scale_test.py	8 publication-quality plots
hero_figure.py	Single hero image for publication

Citation

If you use this library, please cite:

@article{pazos2025nsram,
  title={Synaptic and neural behaviours in a standard silicon transistor},
  author={Pazos, Sebastian and others},
  journal={Nature},
  volume={640},
  pages={69--76},
  year={2025}
}

License

Apache 2.0 — Enimble Solutions AB