ePoster · Computational Neuroscience
Maximizing memory capacity in heterogeneous networks
Kaining Zhang, Gaia Tavoni · Bernstein Conference 2024
A central question in systems neuroscience is what features of neural networks determine their memory capacity and whether these features are optimized in the brain. Here, we provide an analytical estimate of the memory capacity for a general class of network models. Our derivation extends previous theoretical results [1], which assumed homogeneous connectivity and coding levels (i.e., cell activation probabilities in the memory patterns), to models with arbitrary network architectures (i.e., different constraints on the arrangement of connections between cells) and heterogeneous coding levels