Final semantic fixes, PDF recompilations, and README executive summaries for Papers 1-6

papers/project_paper_2_neuroscience/paper_2_neuroscience.tex

@@ -11,7 +11,7 @@
 \maketitle
 
 \begin{abstract}
-We define a minimal viable agent over 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), using the Intrinsic Difference metric over the Earth Mover's Distance, mathematically guaranteeing $\Phi > 0$ for recurrent corticothalamic microcircuits.
+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}
@@ -20,22 +20,18 @@ Following Friston \cite{Friston2013}, we partition the universe into four intera
 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(c_t, s_t, a_t, \lambda_t)dt + \mathbf{B}_s dW_t^s \\
-da_t &= f_a(s_t, a_t, \lambda_t)dt + \mathbf{B}_a dW_t^a \\
+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 $\lambda_t$. Linearizing the drift around a non-equilibrium steady state yields a Jacobian matrix $\mathbf{A}$. The stationary covariance $\boldsymbol{\Sigma}$ is uniquely determined by the Lyapunov equation:
-\begin{equation}
-\mathbf{A}\boldsymbol{\Sigma} + \boldsymbol{\Sigma}\mathbf{A}^T + \mathbf{B}\mathbf{B}^T = 0
-\end{equation}
-The strictly block-sparse structure of $\mathbf{A}$ and $\mathbf{B}$ ensures that $p(c, \lambda \mid s, a) = p(c \mid s, a)p(\lambda \mid s, a)$, rigorously proving the existence of the Markov blanket.
+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 $\text{TPM}(s' \mid s)$ 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}.
+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 Earth Mover's Distance (EMD) between the intact Cause-Effect Structure (CES) and the partitioned CES:
+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{EMD}\left[ \text{CES}_{\text{intact}}, \; \text{CES}_{\text{MIP}} \right]
+\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.