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\title{The Cortical Markov Blanket: Stochastic Active Inference and Intrinsic Integrated Information (Letter)}
\author{Mark Randall Havens \\ \textit{The Fold Within Research Institute}}
\date{\today}

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\maketitle

\begin{abstract}
We define a minimal viable agent bounded by a full Fristonian Markov Blanket explicitly grounded in the canonical cortical microcircuit. By modeling the stochastic dynamics of a four-component system (internal, sensory, active, and external states), we rigorously demonstrate the conditional independence required by the Free Energy Principle via the steady-state Lyapunov equation. To evaluate intrinsic causal integration, we map the continuous stationary density to a discrete Transition Probability Matrix (TPM). We apply Tononi's Integrated Information Theory (IIT 4.0), replacing the Earth Mover's Distance with the Intrinsic Difference metric, mathematically guaranteeing $\Phi > 0$ for recurrent corticothalamic microcircuits.
\end{abstract}

\section{Stochastic Neural Dynamics and the Markov Blanket}
Following Friston \cite{Friston2013}, we partition the universe into four interacting states: internal ($c_t$), sensory ($s_t$), active ($a_t$), and external ($\lambda_t$). We ground this topologically in the canonical microcircuit for predictive coding \cite{Bastos2012}: $s_t$ represents L4 thalamocortical inputs, $c_t$ represents the recurrent L2/3 and L5 populations, $a_t$ represents L5 deep outputs and L6 corticothalamic feedback, and $\lambda_t$ represents the environmental hidden states.

The continuous dynamics are governed by a coupled system of Stochastic Differential Equations (SDEs) driven by standard Wiener processes:
\begin{align}
dc_t &= f_c(c_t, s_t, a_t)dt + \mathbf{B}_c dW_t^c \\
ds_t &= f_s(s_t, a_t, \lambda_t)dt + \mathbf{B}_s dW_t^s \\
da_t &= f_a(c_t, s_t, a_t)dt + \mathbf{B}_a dW_t^a \\
d\lambda_t &= f_\lambda(s_t, a_t, \lambda_t)dt + \mathbf{B}_\lambda dW_t^\lambda
\end{align}
Crucially, there is no direct coupling between $c_t$ and $\lambda_t$, and sensory states $s_t$ do not depend on internal states $c_t$. This structural asymmetry breaks the v-structure, preventing $s_t$ from acting as a collider, ensuring that conditioning on the blanket does not inadvertently open an information path between $c_t$ and $\lambda_t$. Linearizing the drift around a non-equilibrium steady state yields a Jacobian matrix $\mathbf{A}$. The stationary covariance $\boldsymbol{\Sigma}$ is determined by the Helmholtz decomposition $\mathbf{A} = (\mathbf{Q} - \mathbf{D})\boldsymbol{\Sigma}^{-1}$, where $\mathbf{Q}$ is the anti-symmetric solenoidal flow and $\mathbf{D}$ is the diffusion tensor. Provided the solenoidal flow preserves the boundary topology, the precision matrix is block-sparse ($\boldsymbol{\Sigma}^{-1}_{c\lambda} = 0$), ensuring $p(c, \lambda \mid s, a) = p(c \mid s, a)p(\lambda \mid s, a)$ and rigorously proving the Markov blanket.

\section{Intrinsic Integrated Information ($\Phi$)}
To evaluate Tononi's $\Phi$, we assess the intrinsic cause-effect power of the internal states $c_t$. We derive a discrete Transition Probability Matrix $\text{TPM}(c' \mid c)$ from the exact Fokker-Planck stationary distribution $p(\mathbf{x})$ over a minimal timescale $\Delta t$, applying maximum entropy priors to the boundary conditions \cite{Albantakis2023}.

Using the IIT 4.0 framework \cite{Albantakis2023, Oizumi2014}, we measure the irreducible intrinsic information across the Minimum Information Partition (MIP) using the Intrinsic Difference (ID) between the intact Cause-Effect Structure (CES) and the partitioned CES:
\begin{equation}
\Phi = \min_{\text{MIP}} \text{ID}\left[ \text{CES}_{\text{intact}}, \; \text{CES}_{\text{MIP}} \right]
\end{equation}
Because the internal cortical microcircuit $(c_t)$ possesses strong recurrent loops (e.g., L2/3 $\to$ L5 and L5 $\to$ L2/3), the localized block of the Lyapunov covariance $\boldsymbol{\Sigma}_{cc}$ is strictly irreducible under any bisection. Consequently, the intrinsic difference is strictly positive, mathematically guaranteeing $\Phi > 0$ for biological cortical columns.

\bibliographystyle{plain}
\begin{thebibliography}{10}
\bibitem{Friston2013} K. Friston, \textit{J. R. Soc. Interface} \textbf{10}, 20130475 (2013).
\bibitem{Bastos2012} A. M. Bastos et al., \textit{Neuron} \textbf{76}, 695 (2012).
\bibitem{Oizumi2014} M. Oizumi, L. Albantakis, G. Tononi, \textit{PLOS Comput. Biol.} \textbf{10}, e1003588 (2014).
\bibitem{Albantakis2023} L. Albantakis et al., \textit{PLOS Comput. Biol.} \textbf{19}, e1011465 (2023).
\end{thebibliography}
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