ABOUT US

At Synthetic Intelligence Labs, we are at the forefront of bio-silicon intelligence systems. Our innovative research and cutting-edge technologies blend biological and silicon-based systems to create advanced synthetic intelligence solutions. Founded in 2023, our lab focuses on green tech, offering lower-cost computing with reduced energy consumption, benefiting research labs and consumers alike.

SERVICES

We specialize in developing both software and hardware solutions for bio-silicon systems, feature extraction, and more. Our services include advanced analytical tools and techniques for neural signal processing, as well as data-encoded electrical brain stimulation, incorporating both analogue continuous-value and digital binary methods.

Current Features Available

Signal Variance, Signal Standard Deviation, Root Mean Square, Delta Band Power, Theta Band Power, Alpha Band Power, Beta Band Power, Spectral Centroids, Spectral Edge Densities, Higuchi Fractal Dimension, Analytic Signal, Envelope, Derivative, Logarithmic Mean of Lk, Sum of Absolute Differences, Maximum kk Value, FFT Result, FFT Frequencies, Magnitude Spectrum, Cumulative Sum of Magnitude Spectrum, Total Power, Power Threshold, Power Spectral Density, False Nearest Neighbours, 2D Phase Space, 3D Phase Space, 4,5,6,7,8 Dimensional Phase Space, Spectral Entropy, Multiscale Entropy, Transfer Entropy, BDF Kuramoto Arnold Tongues, Mode Locked Kuramoto Circle Map Hilbert Arnold Tongues, Rotation Numbers Circle Map Hilbert Arnold Tongues, Multifractal Detrended Fluctuation Analysis, Hurst Exponents, Wavelet Hurst Exponents, Phase Synchronization, 2Dembedded Recurrence Quantification, 3Dembedded Recurrence Quantification, Recurrence Quantification Raw Live, Recurrence Quantification Raw Large Dataset, Cross Recurrence Quantification Raw, Joint Recurrence Quantification Raw, UMAP and t-SNE, Topological Data Analysis, Lyapunov Exponents, Hamiltonian Matrix, Hermitian and Quantum, Quantum, Quantum-Inspired Connectivity Analysis and Measurement, Harmonics, Harmonics Detection Using Lyapunov Exponents, Weighted Undirected Network, Riemannian Geometry, Jacobian Matrix, Amplitude Envelope Synchronisation, Soul theorem, Growth measure and the polar vortices based on the work of Lawrence Edwards, Kakutani's theorem, Minkowski Space, Lie group, Sample Entropy, Box Counting Dimension, Approximate Entropy

Other Features We’re Working On

Fractal Dimension of Network Connectivity, Resonance Frequency Analysis using CFC, Dynamic Wavelet Analysis, Adaptive STFT, Phase Space Centroids, Multipartite Concurrence, Dynamic Time Warping, Nonlinear Correlation Coefficient, Symbolic Dynamics, Symbolic Transfer Entropy, Multivariate Multiscale Dispersion Lempel-Ziv Complexity (mvMDLZC), Conditional Mutual Information, Information Transfer and Connectivity, Phase Lag Index, Granger Causality, Partial Directed Coherence, Directed Transfer Function, Phase Transfer Entropy, Dynamic Causal Modeling, Granger Causality in Frequency Domain, Cross-Frequency Coupling, Synchronization Likelihood, Adaptive Filtering for Information Flow, Effective Connectivity via Bayesian Networks, Time-Varying Graph Theoretical Analysis, Cross-Frequency Phase-Amplitude Coupling, Manifold Learning, Geometric Learning Algorithms, Persistent Homology, Topological Regularization, Topological Summaries, Probabilistic Inference on Manifolds, Modeling Neural Manifolds, Curvature Analysis, Electromagnetic Field and Neural Modeling, Nonlinear Energy Operators, Finite Element Method, Multipole Expansion, Discrete Dipole Approximation, Minimum Norm Estimates, Beamforming, Modified Quantum Stochastic Processes, Modified Quantum Field Theory, Integrated Information Theory, Network Motifs, Graph Spectral Analysis, Wavelet Coherence, Predictive Coding, Neurosymbolic Computing, Free Energy Minimization, Divergence, Log-Evidence, Negative Free Energy, Evidence Bound, Geodesic Paths, Renormalization Group Analysis, Information Flow Networks, Cartesian Coordinates, Circular Coordinates, Impulse Response, Continuous-time LTI System, Haar Measure, Differential Entropy, Diffeomorphism, Banach Space, Fréchet Space, Laplace Transform, Topological Space Plotting of Neural Manifolds, Symplectic Geometry, Differential Topology, Hyperbolic Space, Gaussian Curvature, Theorema Egregium, Lyapunov Vectors, Lyapunov Spectrum, Lyapunov Stability, Lyapunov Dimension, Ott–Grebogi–Yorke (OGY) Method, Time Dilation, Relativistic Composition of Velocities, Matrix Lie Groups, Complex Manifolds, Kähler and Calabi–Yau Manifolds, Quaternionic Manifold, Hermitian Manifold, Banach Manifold, Fréchet Manifold, Homomorphisms and Isomorphisms, Infinitesimal Generators, Ehresmann Connection, Vector Bundles and Covariant Derivatives

Feature Combinations and AI Technologies

2D Embedding Isomap Manifold Learning, 2D Embedding Locally Linear Embedding Manifold Learning, 2D Embedding Spectral Manifold Learning, 2D Embedding t-SNE Manifold Learning, 3D Embedding Isomap Manifold Learning, 3D Embedding Locally Linear Embedding Manifold Learning, 3D Embedding Spectral Manifold Learning, 3D Embedding t-SNE Manifold Learning, Graph Convolutional Neural Nets, Convolutional Neural Nets, Transformers