LiquidCortex.jl

GPU-accelerated sparse liquid state machine for neuromorphic computing

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Production-grade CUDA-accelerated sparse Liquid State Machine (LSM) with OU-SDE membrane dynamics, multi-lobe ensemble architecture, cuSPARSE mat-vec, and STDP covariance learning.

Features

• SparseBrain — configurable reservoir: N neurons, connectivity probability, Float16 sparse weights on GPU
• OU-SDE dynamics: dV = ((V_rest - V)/τ + I_rec + I_ext)dt + σ dW
• cuSPARSE Float16 sparse mat-vec on GPU (fits 65k neurons in 16 GB VRAM)
• STDP covariance learning with eligibility traces
• 1000-tick rolling spike history buffer (circular, on-GPU)
• EnsembleBrain — multi-lobe: multiple reservoirs with different time constants
• Hardware proprioception: global inhibition driven by external stress signals
• MarketPulse — optional domain-specific input decoding for financial time-series

Installation

using Pkg
Pkg.add("LiquidCortex")

Quick Start

using LiquidCortex

# Create a 65,536-neuron sparse LSM lobe
brain = SparseBrain(20.0f0)  # τ_m = 20ms

# Or create the full 4-lobe ensemble (262,144 neurons)
ensemble = EnsembleBrain()

# Step the reservoir with a 14-element input vector
u = CUDA.zeros(Float32, 14)  # or use pulse_to_input(market_pulse)
step!(brain, u, 45.0f0, 0.3f0)  # (input, gpu_temp, buffer_load)

# Read the 16-element output
output = get_output(brain)

Public API

Type / Function	Description
SparseBrain(tau_m)	Create a 65,536-neuron sparse reservoir lobe
EnsembleBrain()	Create 4-lobe ensemble (262,144 neurons)
step!(brain, u, gpu_temp, basys_load; ...)	Execute one simulation timestep
get_output(brain)	Copy 16-element readout from GPU to CPU
get_ensemble_output(eb)	Copy aggregated 16-element readout
compute_reservoir_covariance!(brain)	Compute subsampled covariance matrix
diagnostics(brain)	Return diagnostic string
ensemble_diagnostics(eb)	Per-lobe diagnostic summary
MarketPulse	Market data packed struct (120 bytes)
decode_market_pulse(buf)	Zero-copy decode from byte buffer
pulse_to_input(pulse)	Convert MarketPulse → 14-element input vector

OU-SDE Membrane Dynamics

dV = ((V_rest - V)/τ  +  W_rec·s(t)  +  W_in·x(t)) dt  +  σ dW

Discretized as Euler-Maruyama. Spike when V ≥ θ; reset to V_rest.

Ornstein & Uhlenbeck (1930); Maass, Natschläger & Markram (2002)

STDP Covariance Learning

ΔW_ij = η (⟨s_i s_j⟩ - ⟨s_i⟩⟨s_j⟩)

Computed on a subsampled 8192-neuron window to avoid O(N²) blow-up.

Bi & Poo (1998); Hebb (1949)

Provenance

Extracted from Eagle-Lander, a private neuromorphic GPU supervisor. The LSM core has been fully decoupled from domain-specific ingestion and execution so it works with any time-series application.

License

GPL-3.0-or-later