Diffusion Models (DMs) have recently set state-of-the-art on many generation benchmarks.
However, there are myriad ways to describe them mathematically, which makes it difficult to …

Graph representation learning methods, such as node embeddings, are powerful
approaches to map nodes into a latent vector space, allowing their use for various graph …

In recent years, heightened interest has been ignited in associative memory networks,
largely attributed to their perceived equivalence with the attention mechanism, a …

Continual learning is a problem for artificial neural networks that their biological counterparts
are adept at solving. Building on work using Sparse Distributed Memory (SDM) to connect a …

Neural networks are vulnerable to catastrophic forgetting when data distributions are non-
stationary during continual online learning; learning of a later task often leads to forgetting of …

As spiking neural networks receive more attention, we look toward applications of this
computing paradigm in fields other than computer vision and signal processing. One major …

We report an improvement to the conventional Echo State Network (ESN) across three
benchmark chaotic time-series prediction tasks using fruit fly connectome data alone. We …

Recently, the use bio-plausible learning techniques such as Hebbian and Spike-Timing-
Dependent Plasticity (STDP) have drawn significant attention for the design of compute …

Maximum inner product search (MIPS), combined with the hashing method, has become a
standard solution to similarity search problems. It often achieves an order of magnitude …

Wiring patterns of brain networks embody a trade-off between information transmission,
geometric constraints, and metabolic cost, all of which must be balanced to meet functional …