The code provided represents a computational model of distributed working memory (DWM) and classical working memory (CWM) within a large-scale network model of the brain. This model is primarily concerned with understanding how working memory is maintained across different areas of the brain and how various parameters affect this system's functionality. ### Biological Basis #### Working Memory Working memory is a cognitive function that allows for the temporary storage and manipulation of information necessary for complex tasks such as learning, reasoning, and comprehension. It is typically believed to involve several interconnected brain regions, including the prefrontal cortex and parietal lobes. #### Large-Scale Network Model This model incorporates a network of 30 areas (as indicated by `Areas=1:30;`), likely corresponding to different regions of the cortex. The connectivity between these areas is based on empirical data loaded from `subgraphData30.mat`, which contains feedforward and feedback projections (`FLN` for Feedforward and `SLN` for Supragranular Layer projections). Such networks are crucial for mimicking the distributed and hierarchical nature of the mammalian brain connectivity. #### Key Parameters - **`fln` and `sln`**: These matrices likely represent the structural connectivity between different cortical regions, which are crucial for the effective transmission of signals necessary for working memory processes. - **`Wplus` and `G`**: These parameters represent synaptic weights and the global coupling within the network. Synaptic weights determine the strength of the connections between neurons or regions and influence the network's ability to maintain activity representing working memory. Global coupling (`G`) may reflect how much one region's activity influences another, akin to the influence of global neuromodulators. - **`Iext`**: The external input current (`Iext`) simulates incoming stimuli or cues necessary for initiating or maintaining working memory tasks. Variations in `Iext` help identify minimal input strengths required for the efficient encoding of working memory. #### Distributed and Classical Working Memory - **Classical Working Memory (CWM)**: Characterized by local, potentially more isolated processing, typically thought to involve the prefrontal cortex heavily, with minimal feedback (`flnx=tril(fln);` indicates restricting FB projections). - **Distributed Working Memory (DWM)**: Involves a more integrated network across various cortical areas, allowing more feedback connections (`flnx=fln;` indicates full connectivity), reflecting a more loopy recurrent network that can more robustly store and manipulate information. #### Input Strengths and Distractors The code is structured to determine the minimal input strengths needed for effective target processing and resistance to distractors in both CWM and DWM models. Such tasks explore how varying the strength of inputs affects the robustness and reliability of working memory, revealing how biological neural circuits might prioritize certain signals while filtering out noise. ### Biological Implications Understanding these parameters and network structures can offer insights into how the brain can maintain and disrupt working memory function, a topic of significant interest for conditions like ADHD, schizophrenia, or Alzheimer's disease, where working memory deficits are prevalent. By comparing classical and distributed models, the study may also uncover evolutionary or developmental aspects of how working memory systems became more efficient or versatile in different species or contexts.