1 citations · 3 across the 10 of their papers we have counts for
12 papers
Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery
Yuzhou Cheng, Tom Yates, Ignacio Alzugaray +3
Gaussian Belief Propagation (GBP) is a distributed inference algorithm that passes messages in graphical models, making it attractive for scalable spatial intelligence. However, we…
Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks
Jatin Sharma, Dan F. M Goodman, Danyal Akarca
Understanding how biological and artificial neural networks implement computation from connectivity is a central problem in neuroscience and machine learning. In neural systems, st…
The Principle of Maximum Heterogeneity Optimises Productivity in Distributed Production Systems Across Biology, Economics, and Computing
Guillhem Artis, Danyal Akarca, Jascha Achterberg
The world is full of systems of distributed agents, collaborating and competing in complex ways: firms and workers specialise within economies, neurons adapt their tuning across br…
Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs
Tom Yates, Yuzhou Cheng, Ignacio Alzugaray +3
Belief Propagation (BP) is a powerful algorithm for distributed inference in probabilistic graphical models, however it quickly becomes infeasible for practical compute and memory…
Self-motion as a structural prior for coherent and robust formation of cognitive maps
Yingchao Yu, Pengfei Sun, Yaochu Jin +7
Most computational accounts of cognitive maps assume that stability is achieved primarily through sensory anchoring, with self-motion contributing to incremental positional updates…
Algorithm-hardware co-design of neuromorphic networks with dual memory pathways
Pengfei Sun, Zhe Su, Jascha Achterberg +3
Spiking neural networks excel at event-driven sensing. Yet, maintaining task-relevant context over long timescales both algorithmically and in hardware, while respecting both tight…
Space as Time Through Neuron Position Learning
Balázs Mészáros, James C. Knight, Danyal Akarca +1
Biological neural networks exist in physical space where distance influences communication delays: a fundamental coupling between space and time absent in most artificial neural ne…
Exploiting heterogeneous delays for efficient computation in low-bit neural networks
Pengfei Sun, Jascha Achterberg, Zhe Su +2
Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is…
Brain-Like Processing Pathways Form in Models With Heterogeneous Experts
Jack Cook, Danyal Akarca, Rui Ponte Costa +1
The brain is made up of a vast set of heterogeneous regions that dynamically organize into pathways as a function of task demands. Examples of such pathways can be found in the int…
Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics
Cornelia Sheeran, Andrew S. Ham, Duncan E. Astle +2
Understanding how biological constraints shape neural computation is a central goal of computational neuroscience. Spatially embedded recurrent neural networks provide a promising…
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
Zhonghao He, Jascha Achterberg, Katie Collins +13
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this pro…
Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture
Jascha Achterberg, Danyal Akarca, Moataz Assem +3
There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide…