# Role of Delay in Brain Dynamics

arXiv: https://arxiv.org/abs/2410.11384 · HTML: https://mlsanity.com/paper/2410.11384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent](https://mlsanity.com/paper/1607.01981/) (2016)
- [Phase transitions in Restricted Boltzmann Machines with generic priors](https://mlsanity.com/paper/1612.03132/) (2016)
- [The emergence of a concept in shallow neural networks](https://mlsanity.com/paper/2109.00454/) (2021)
- [Phase transitions in the mini-batch size for sparse and dense two-layer neural networks](https://mlsanity.com/paper/2305.06435/) (2023)
- [Towards a universal mechanism for successful deep learning](https://mlsanity.com/paper/2309.07537/) (2023)
- [Power-law Scaling to Assist with Key Challenges in Artificial Intelligence](https://mlsanity.com/paper/2211.08430/) (2022)
- [Efficient shallow learning as an alternative to deep learning](https://mlsanity.com/paper/2211.11106/) (2022)
- [Significant anisotropic neuronal refractory period plasticity](https://mlsanity.com/paper/2109.02041/) (2021)
- [Learning on tree architectures outperforms a convolutional feedforward network](https://mlsanity.com/paper/2211.11378/) (2022)
- [Enhancing the accuracies by performing pooling decisions adjacent to the output layer](https://mlsanity.com/paper/2303.05800/) (2023)
- [Neuronal Response Impedance Mechanism Implementing Cooperative Networks with Low Firing Rates and Microseconds Precision](https://mlsanity.com/paper/1411.5258/) (2014)
- [Long anisotropic absolute refractory periods with rapid rise-times to reliable responsiveness](https://mlsanity.com/paper/2111.02689/) (2021)
- [Brain inspired neuronal silencing mechanism to enable reliable sequence identification](https://mlsanity.com/paper/2203.13028/) (2022)
- [Porting HPC Applications to AMD InstinctTM MI300A Using Unified Memory and OpenMP](https://mlsanity.com/paper/2405.00436/) (2024)

## Built on by (most cited first, 1 of 1)

- [Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi](https://mlsanity.com/paper/2501.12900/) (2025)

## Abstract (arXiv)

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and M multiple delays between successive layers, thereby generating a polynomial time-series outputs with M. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with M outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.