Immune networks: multi-tasking capabilities at medium load

TY - JOUR

T1 - Immune networks: multi-tasking capabilities at medium load

AU - Agliari, E.

AU - Annibale, A.

AU - Barra, A.

AU - Coolen, A. C. C.

AU - Tantari, D.

PY - 2013/8/23

Y1 - 2013/8/23

N2 - Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P similar to logN. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P similar to N-delta with delta is an element of (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for delta <1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime delta = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval.

AB - Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P similar to logN. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P similar to N-delta with delta is an element of (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for delta <1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime delta = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval.

KW - ATTRACTOR NEURAL-NETWORKS

KW - SELF-TOLERANCE

KW - SYSTEM

KW - MODEL

KW - CONNECTIVITY

KW - IMMUNOLOGY

KW - DYNAMICS

KW - HEALTHY

KW - MICE

U2 - 10.1088/1751-8113/46/33/335101

DO - 10.1088/1751-8113/46/33/335101

M3 - Article

SN - 1751-8113

VL - 46

JO - Journal Of Physics A-Mathematical And Theoretical

JF - Journal Of Physics A-Mathematical And Theoretical

IS - 33

M1 - 335101

ER -