Mathematical definition · 03 / 05

Definition of the logic replicant

Definition 2: the leietanic function, the vortices and their scalar fields, and the classification rule.

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In plain words

Formally, a logic replicant is a pair of things. The first is the map-drawing formula \(L\): a straight-line map of the features plus a sum of "folded" straight-line pieces. Each fold is a hinge whose position and slope are learned, so the formula can bend the feature space wherever the logic needs it. The second is the set of vortices \(V\). Each vortex has three numbers: where it sits, how quickly its influence fades with distance, and how strong it is.

To classify a new example, place it on the map, add up the influence of each class's vortices at that spot, and pick the class with the largest total. That is the whole rule. Counting the adjustable numbers, the replicant that solves the 8-bit parity problem has only 32 of them.

Definition 2

Definition 2 — logic replicant

A logic replicant is a tuple \(\langle L, V\rangle\) able to replicate the hidden classification logic of a problem; it receives \(F\in\mathbb{N}^+\) input signals \(x_1\) through \(x_F\) where:

\(L\colon X\to Q\) is a leietanic transformation function that maps every input case \(\boldsymbol{x}\) into a point \(\boldsymbol{q}\) in the \(D\)-dimensional space \(Q\), where

\[L(\boldsymbol{x})=\boldsymbol{\Lambda}_a\boldsymbol{x}+\sum_{s=1}^{S}\sum_{r=1}^{R}\boldsymbol{\xi}_{sr}\circ\left|\boldsymbol{\Lambda}_s\boldsymbol{x}+\boldsymbol{\beta}_{sr}\right|\](14)

where \(\boldsymbol{\Lambda}_a\) and the other \(\boldsymbol{\Lambda}_s\colon s\in[1,S]\) are \(D\times F\) matrices that multiply the vector instance \(\boldsymbol{x}\). The vector collections \(\boldsymbol{\beta}_{sr}\) and \(\boldsymbol{\xi}_{sr}\) have the same dimension \(D\). The operator \(\circ\) is the Hadamard product, defined for matrices and vectors as entrywise multiplication.

\(V\) is the set of vortices and their respective scalar fields \(U_{ck}\in V\), \(U_{ck}\colon Q\to\mathbb{R}\), \(c\in C\), \(k\in K\subset\mathbb{N}^+\wedge C\cdot K=|V|\), that collectively decide what class to assign to the output of the leietanic function. Every \(c,k\)-th vortex (defined by its scalar field \(U_{ck}\)) is assigned to only one specific output class \(c\), where

\[U_{ck}(\boldsymbol{q})=\frac{\Phi_{ck}}{1+\rho_{ck}^2(\boldsymbol{q}-\boldsymbol{v}_{ck})^2}\](15)

is based on three parameters: its centre \(\boldsymbol{v}_{ck}\in\mathbb{R}^D\), its density \(\rho_{ck}\in\mathbb{R}\) and its intensity \(\Phi_{ck}\in\mathbb{R}\).

Classification rule

Every class \(c\) has assigned exactly \(K\) vortices, and its scalar field is the sum of theirs:

\[U_c(\boldsymbol{q})=\sum_{k=1}^{K}U_{ck}(\boldsymbol{q})=\sum_{k=1}^{K}\frac{\Phi_{ck}}{1+\rho_{ck}^2(\boldsymbol{q}-\boldsymbol{v}_{ck})^2}\](16)

Each class field is evaluated at \(\boldsymbol{q}=L(\boldsymbol{x})\), and the class \(\omega\) with the greatest field is selected:

\[\omega=\operatorname*{arg\,max}_{\omega}U_\omega(\boldsymbol{q})=\left\{\omega\in C\colon U_\omega(L(\boldsymbol{x}))\ge U_c(L(\boldsymbol{x})),\ \forall c\in C\right\}\](17)

Geometrically, the centre \(\boldsymbol{v}_{ck}\) defines where a vortex is placed in \(Q\), while its density and intensity define how big or small it is and how intensively a quon belongs to the class. The leietanic function transforms inputs into quons that are identified with a class \(c\) if that class has the greatest scalar field at \(\boldsymbol{q}\).

Parameter count

Counting the free coefficients in (14) and (15): \(L\) has \((1+S)\,D\,F+2SRD\) parameters and \(V\) has \(|C|\,K\,(D+2)\). For the parity experiment (\(F=8\), \(S=R=0\), \(D=1\), \(|V|=8\)) that is 8 + 24 = 32 parameters, consistent with the paper's remark that the replicant needs fewer than 100 parameters there. Configurations used in the experiments are listed on the results page.