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--- liquid_brains [2026/09/21 01:06] – neilwinterburn
+++ liquid_brains [2026/09/27 19:46] (current) – neilwinterburn
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 Jordi Piñero, Ricard Solé; Statistical physics of liquid brains.
+Experts from [[https://royalsocietypublishing.org/rstb/article/374/1774/20180376/42328/Statistical-physics-of-liquid-brainsStatistical|article]]
 Liquid neural networks (or ‘liquid brains’) are a widespread class of cognitive living networks characterized by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems.
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 {{ :m_rstb20180376f01.png?600 |}}
-Network interactions in liquid versus solid brains. The three case studies analysed in this paper are shown, with examples of the agents involved in each case. (a) Standard neural networks involve spatially localized cells connected through synaptic weights. In contrast with this architecture, liquid brains, including (b) ant colonies and (c) the IS include mobile agents (or cell subsets) interacting in space and time with no fixed pairwise weights. A schematic of each case study is outlined in the row below. Standard neural networks are defined in terms of connected excitable elements that can be roughly classified in active (firing) and inactive (quiescent) neurons, here indicated as filled and open circles, respectively (d). The wiring matrix remains basically the same in terms of topology (who is connected with whom) but will be modified in strength due to experience. By contrast, ant colonies must be represented by disconnected graphs (e) where interactions are possible within a given spatial range, here indicated by means of the grey circle. The IS allows several representations of the interactions, but in many cases it is the molecular interaction between epitopes (strings of symbols in (f)) that truly represents the underlying liquid brain dynamics.
+Network interactions in liquid versus solid brains. The three case studies analysed in this paper are shown, with examples of the agents involved in each case.
+(a) Standard neural networks involve spatially localized cells connected through synaptic weights. In contrast with this architecture, liquid brains, including
+(b) ant colonies and
+(c) the IS include mobile agents (or cell subsets) interacting in space and time with no fixed pairwise weights. A schematic of each case study is outlined in the row below. Standard neural networks are defined in terms of connected excitable elements that can be roughly classified in active (firing) and inactive (quiescent) neurons, here indicated as filled and open circles, respectively
+(d). The wiring matrix remains basically the same in terms of topology (who is connected with whom) but will be modified in strength due to experience. By contrast, ant colonies must be represented by disconnected graphs
+(e) where interactions are possible within a given spatial range, here indicated by means of the grey circle. The IS allows several representations of the interactions, but in many cases it is the molecular interaction between epitopes (strings of symbols in (f)) that truly represents the underlying liquid brain dynamics.