Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons
A. Anishchenko (1), E. Bienenstock (1), A. Treves (2) ((1) Brown, University, (2) SISSA)

TL;DR
This paper investigates how small-world network connectivity influences memory retrieval and activity patterns in integrate-and-fire neuron models, revealing that retrieval and localized activity bumps are generally mutually exclusive and not solely determined by small-world properties.
Contribution
It demonstrates that in integrate-and-fire networks, memory retrieval and localized activity bumps are mutually exclusive behaviors influenced by connectivity, challenging simple small-world explanations.
Findings
Memory retrieval occurs near random connectivity
Localized activity bumps occur near ordered connectivity
Transition between behaviors is not directly tied to small-world metrics
Abstract
Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise description useful to better understand how the type of connectivity affects memory retrieval? We have simulated an autoassociative memory network of integrate-and-fire units, positioned on a ring, with the network connectivity varied parametrically between ordered and random. We find that the network retrieves when the connectivity is close to random, and displays the characteristic behavior of ordered nets (localized 'bumps' of activity) when the connectivity is close to ordered. Recent analytical work…
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Taxonomy
TopicsNeural dynamics and brain function · Neural Networks and Applications · Topological and Geometric Data Analysis
