\begin{algorithmic}

\Require {$\mathfrak{c} \in [4, 14]$, P}  \Comment{P is the pattern set}

\State$i \gets 0,$
\State$sum\_{d_2} = 0$
\While{$i \leq |P|,$} 

\State$\mathbf{x} \gets \mathfrak{b}^{(i)}$

\State$sum\_{d_1} = 0$

\While{$j\leq \mathfrak{K}$}  

\State\Comment{$\mathfrak{K}$ : calculating the avg.}

\State$\mathfrak{\bar{b}^{(i)}} \gets$ invert polarities of $\mathfrak{E}\%$ of $\mathfrak{b}^{(i)}$ 

\State$\mathbf{y} \gets CAM(\mathfrak{c}, \mathfrak{\bar{b}}^{(i)})$

\State$d_1 \gets \mathfrak{D}(\mathbf{x,y})$

\State$sum\_{d_1} \gets sum_{d_1} + d_1$
\EndWhile

\State$d_2 \gets \textbf{mean}(sum_{d_1})$

\State$sum\_{d_2} \gets sum\_{d_2} + d_2$
\EndWhile

\State$\textbf{avg\_Hamming\_dist} = mean(sum\_{d_2})$

\Return{\textbf{avg\_Hamming\_dist}}

\end{algorithmic}

\begin{algorithmic}
\Require {$\mathfrak{c} \in [4, 14]$, P} \Comment{P is the pattern set}
\State$i \gets 0,$
\State$sum\_{d_2} = 0$
\While{$i \leq |P|,$}
\State$\mathbf{x} \gets \mathfrak{b}^{(i)}$
\State$sum\_{d_1} = 0$
\While{$j\leq \mathfrak{K}$}
\State\Comment{$\mathfrak{K}$ : calculating the avg.}
\State$\mathfrak{\bar{b}^{(i)}} \gets$ invert polarities of $\mathfrak{E}\%$ of $\mathfrak{b}^{(i)}$
\State$\mathbf{y} \gets CAM(\mathfrak{c}, \mathfrak{\bar{b}}^{(i)})$
\State$d_1 \gets \mathfrak{D}(\mathbf{x,y})$
\State$sum\_{d_1} \gets sum_{d_1} + d_1$
\EndWhile
\State$d_2 \gets \textbf{mean}(sum_{d_1})$
\State$sum\_{d_2} \gets sum\_{d_2} + d_2$
\EndWhile
\State$\textbf{avg\_Hamming\_dist} = mean(sum\_{d_2})$
\Return{\textbf{avg\_Hamming\_dist}}
\end{algorithmic}
{
    "title": "a chaotic associative memory",
    "id": "2401.10922",
    "abstract": "we propose a novel chaotic associative memory model using a network of chaotic rossler systems and investigate the storage capacity and retrieval capabilities of this model as a function of increasing periodicity and chaos. in early models of associate memory networks, memories were modeled as fixed points, which may be mathematically convenient but has poor neurobiological plausibility. since brain dynamics is inherently oscillatory, attempts have been made to construct associative memories using nonlinear oscillatory networks. however, oscillatory associative memories are plagued by the problem of poor storage capacity, though efforts have been made to improve capacity by adding higher order oscillatory modes. the chaotic associative memory proposed here exploits the continuous spectrum of chaotic elements and has higher storage capacity than previously described oscillatory associate memories.",
    "categories": "q-bio.nc nlin.cd",
    "doi": "",
    "created": "2024-01-15",
    "updated": "",
    "authors": [
        "nurani rajagopal rohan",
        "sayan gupta",
        "v. srinivasa chakravarthy"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2401.10922"
}
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