Modularity and multitasking in neuro-memristive reservoir networks
Read:: - [ ] Loeffler et al. (2021) - Modularity and multitasking in neuro-memristive reservoir networks 🛫2023-12-04 !!2 rd citation todoist Print:: ❌ Zotero Link:: Zotero Files:: attachment Reading Note:: Web Rip:: url:: https://doi.org/10.1088/2634-4386/ac156f
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The human brain seemingly effortlessly performs multiple concurrent and elaborate tasks in response to complex, dynamic sensory input from our environment. This capability has been attributed to the highly modular structure of the brain, enabling specific task assignment among different regions and limiting interference between them. Here, we compare the structure and functional capabilities of different bio-physically inspired and biological networks. We then focus on the influence of topological properties on the functional performance of highly modular, bio-physically inspired neuro-memristive nanowire networks (NWNs). We perform two benchmark reservoir computing tasks (memory capacity and nonlinear transformation) on simulated networks and show that while random networks outperform NWNs on independent tasks, NWNs with highly segregated modules achieve the best performance on simultaneous tasks. Conversely, networks that share too many resources, such as networks with random structure, perform poorly in multitasking. Overall, our results show that structural properties such as modularity play a critical role in trafficking information flow, preventing information from spreading indiscriminately throughout NWNs.
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Figure 1. Network comparison. (A) From left to right, graphical representations of a sparse crossbar array network, a random network, a C. elegans network, and a self-assembled NWN. (B) Structural connectivity measures of each network, including network diameter, modularity (Q), average clustering and small world propensity (SWP). (C) Comparison of NLT accuracy and MC score for different networks. All networks have similar average degree (〈k〉≈13), number of nodes ( 300) and number of edges ( 2000), as well as the same memristive edge–junction model. Error-bars represent standard error of the mean over ten independent statistical representations of each network (except C. elegans). Page 3
Figure 1. Network comparison. (A) From left to right, graphical representations of a sparse crossbar array network, a random network, a C. elegans network, and a self-assembled NWN. (B) Structural connectivity measures of each network, including network diameter, modularity (Q), average clustering and small world propensity (SWP). (C) Comparison of NLT accuracy and MC score for different networks. All networks have similar average degree (〈k〉≈13), number of nodes ( 300) and number of edges ( 2000), as well as the same memristive edge–junction model. Error-bars represent standard error of the mean over ten independent statistical representations of each network (except C. elegans). Page 3