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Teruki Mayama

Publications and source records attributed to Teruki Mayama.

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Finite-state enumeration of adjacency-constrained 132-avoiding permutations

For a fixed integer $m\ge 1$, let $\mathcal{A}_n^{(m)}$ be the set of permutations $\pi\in S_n$ that avoid the pattern $132$ and satisfy the adjacency bound $|\pi_{i+1}-\pi_i|\le m$ for all $i$. Here, a pattern $132$ means three indices $i<j<k$ such that $\pi_i<\pi_k<\pi_j$. A recent study initiated the enumeration of these constrained 132-avoiding permutations, treating the case $m=2$ by deriving a rational ordinary generating function and asking for finite-state decompositions, rational generating functions, and explicit rational formulas for larger fixed $m$. We introduce a two-sided endpoint-state decomposition that works uniformly for every fixed $m$. The state variables impose threshold bounds on the endpoint deficiencies $n-\pi_1$ and $n-\pi_n$, with thresholds in $\{0,1,\ldots,m-1,\infty\}$. This gives at most $(m+1)^2$ states and proves that, for every fixed $m$, the ordinary generating function $A^{(m)}(x)$ is rational and can be computed effectively by exact linear algebra. We also identify cyclic strongly connected components of the dependency graph in the finite-state system to give an explicit upper bound for the order of an eventual constant-coefficient recurrence satisfied by the sequence $a_n^{(m)}=|\mathcal{A}_n^{(m)}|$. We then recover the known case $m=2$ from this state system and work out the case $m=3$ explicitly. On the asymptotic side, we prove that the exponential growth constant exists for every $m$; for $m\ge2$ it is obtained from the spectral radii of the two cyclic components with more than one vertex in the state system. We determine the simple-pole asymptotics for $m=2$ and $m=3$, and we prove that the growth constants are nondecreasing in $m$, strictly smaller than the Catalan growth constant $4$ for every finite $m$, and converge to $4$ as $m\to\infty$.

math.CO

Bridging integrated information theory and the free-energy principle in living neuronal networks

Integrated Information Theory (IIT) links consciousness to integrated causal structure, whereas the Free-Energy Principle (FEP) explains self-organization through variational free-energy minimization. Their relationship in living neural systems remains unclear. We analyzed dissociated neuronal cultures learning to infer hidden signal sources. Across repeated stimulation, variational free energy decreased, while inference accuracy and Bayesian surprise, defined as the divergence between prior and posterior beliefs, increased. An IIT-inspired integrated-information proxy and main-complex size followed a non-monotonic, hill-shaped trajectory. The proxy correlated most strongly with Bayesian surprise and more weakly with accuracy and variational free energy. An Ising-model analysis indicated that Bayesian surprise and integrated information can be jointly amplified near shared positive critical modes and suggested how early connectivity development followed by response stabilization could generate the observed trajectory. These results link belief updating to integrated information in living neuronal networks and provide an empirical point of contact between IIT and the FEP.

q-bio.NC