# Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing

arXiv: https://arxiv.org/abs/2207.07338 · HTML: https://mlsanity.com/paper/2207.07338/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 102 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science](https://mlsanity.com/paper/1707.04780/) (2017)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Sparse GPU Kernels for Deep Learning](https://mlsanity.com/paper/2006.10901/) (2020)
- [Dendritic cortical microcircuits approximate the backpropagation algorithm](https://mlsanity.com/paper/1810.11393/) (2018)
- [Towards deep learning with segregated dendrites](https://mlsanity.com/paper/1610.00161/) (2016)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [How Can We Be So Dense? The Benefits of Using Highly Sparse Representations](https://mlsanity.com/paper/1903.11257/) (2019)
- [The Sparse Manifold Transform](https://mlsanity.com/paper/1806.08887/) (2018)
- [Multimodal Representation Learning via Maximization of Local Mutual Information](https://mlsanity.com/paper/2103.04537/) (2021)
- [Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis](https://mlsanity.com/paper/2203.17263/) (2022)
- [Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain](https://mlsanity.com/paper/1805.08932/) (2018)
- [The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Function](https://mlsanity.com/paper/1511.02976/) (2015)
- [Lip-Reading Driven Deep Learning Approach for Speech Enhancement](https://mlsanity.com/paper/1808.00046/) (2018)
- [XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification](https://mlsanity.com/paper/1709.00572/) (2017)
- [Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition](https://mlsanity.com/paper/2108.02241/) (2021)
- [CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement](https://mlsanity.com/paper/1909.10407/) (2019)
- [A comparison of partial information decompositions using data from real and simulated layer 5b pyramidal cells](https://mlsanity.com/paper/2206.06456/) (2022)
- [DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation](https://mlsanity.com/paper/1808.00060/) (2018)
- [A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition](https://mlsanity.com/paper/1507.05695/) (2015)

## Built on by (most cited first, 5 of 5)

- [Unlocking the potential of two-point cells for energy-efficient and resilient training of deep nets](https://mlsanity.com/paper/2211.01950/) (2022)
- [BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis](https://mlsanity.com/paper/2402.07310/) (2024)
- [Cooperation Is All You Need](https://mlsanity.com/paper/2305.10449/) (2023)
- [An Overlooked Role of Context-Sensitive Dendrites](https://mlsanity.com/paper/2408.11019/) (2024)
- [Multimodal Speech Enhancement Using Burst Propagation](https://mlsanity.com/paper/2209.03275/) (2022)

## Abstract (arXiv)

Deep learning (DL) has big-data processing capabilities that are as good, or even better, than those of humans in many real-world domains, but at the cost of high energy requirements that may be unsustainable in some applications and of errors, that, though infrequent, can be large. We hypothesise that a fundamental weakness of DL lies in its intrinsic dependence on integrate-and-fire point neurons that maximise information transmission irrespective of whether it is relevant in the current context or not. This leads to unnecessary neural firing and to the feedforward transmission of conflicting messages, which makes learning difficult and processing energy inefficient. Here we show how to circumvent these limitations by mimicking the capabilities of context-sensitive neocortical neurons that receive input from diverse sources as a context to amplify and attenuate the transmission of relevant and irrelevant information, respectively. We demonstrate that a deep network composed of such local processors seeks to maximise agreement between the active neurons, thus restricting the transmission of conflicting information to higher levels and reducing the neural activity required to process large amounts of heterogeneous real-world data. As shown to be far more effective and efficient than current forms of DL, this two-point neuron study offers a possible step-change in transforming the cellular foundations of deep network architectures.