Neuromorphic tissues: Soft Biomolecular Networks for Brain-Inspired Temporal Computing

This repository contains all MATLAB scripts and example input data required to reproduce the main computational results presented in the companion manuscript (see Connected_Reservoir_Paper_Science.pdf). The package implements and compares two physical-model reservoir computing systems built from droplet-interface-bilayer (DIB) ionic devices: Connected Reservoir Network – simulates a spatially connected droplet lattice driven by masked current inputs. Parallel Reservoir Network – simulates independent (uncoupled) memristive nodes in parallel as a reference system. Both reservoirs are modeled with the Alamethicin-type Richard’s differential equation (RDE) pore-state dynamics, solved numerically using MATLAB’s stiff ODE solvers (ode15s). Main capabilities NARMA tasks: Generates masked input sequences and trains linear readouts to predict NARMA orders 2–10; reports train/test NRMSE. Lorenz-63 task: Demonstrates prediction of all three Lorenz-63 chaotic states using the optimized 43-node connected reservoir. Hyperparameter sweeps: Grid-search scripts for input amplitude, mask length, and node count for performance benchmarking. Reproducible I/O: Includes functions for input-mask generation, state sampling after each pulse, and standard feature-matrix construction. How to reproduce figures/results ParallelReservoirNARMA_Optimized.m — reproduces the main NARMA benchmarking shown for the uncoupled network. HyperParameterTuningOptimized.m — performs grid search and NARMA evaluation for the connected reservoir. Lorenz63_Prediction.m — trains on Lorenz-63 time series and plots the 3-D attractors for training vs. testing datasets. Sub-functions in /src/Codes… provide ODE models, input masking (binary and weighted), and De Bruijn-based temporal masks. All random seeds, pulse widths, and parameter values are pre-set to match the paper’s reported results. The scripts save intermediate .mat files for sequences, states, and readout predictions so figures and error metrics can be regenerated without code modification. Requirements MATLAB R2023a or later (tested up to R2024b). No external toolboxes beyond MATLAB’s built-in ODE suite and Statistics & Machine Learning Toolbox (for fitlm).