ArticleScientific reports2015
Causal Inference and Explaining Away in a Spiking Network.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Feature-specific inhibitory connectivity augments the accuracy of cortical representations.bioRxiv : the preprint server for biology · 2025Article
- Article
- Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.bioRxiv : the preprint server for biology · 2025Article
- Computational methods to study information processing in neural circuits.Computational and structural biotechnology journal · 2023Review
- The structures and functions of correlations in neural population codes.Nature reviews. Neuroscience · 2022Review
- Article
- Micro-pursuit: A class of fixational eye movements correlating with smooth, predictable, small-scale target trajectories.Journal of vision · 2021Article
- A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns.Frontiers in neuroscience · 2020Article
- Cortical reliability amid noise and chaos.Nature communications · 2019Article
- Neural correlates of sparse coding and dimensionality reduction.PLoS computational biology · 2019Review
- Causal inference accounts for heading perception in the presence of object motion.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Article
- Back-Propagation Learning in Deep Spike-By-Spike Networks.Frontiers in computational neuroscience · 2019Article
- High-Level Prediction Signals in a Low-Level Area of the Macaque Face-Processing Hierarchy.Neuron · 2017Article
- Efficient codes and balanced networks.Nature neuroscience · 2016Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.