ArticleBulletin of mathematical biology2019
High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons.
Article in Bulletin of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- The critical dimension of memory engrams and an optimal number of senses.Scientific reports · 2025Article
- Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.Frontiers in computational neuroscience · 2022Review
- Editorial: Toward and beyond human-level AI, volume II.Frontiers in neurorobotics · 2022Article
- A Biomorphic Model of Cortical Column for Content-Based Image Retrieval.Entropy (Basel, Switzerland) · 2021Article
- Universal principles justify the existence of concept cells.Scientific reports · 2020Article
- Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.Sensors (Basel, Switzerland) · 2020Article
- High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality.Entropy (Basel, Switzerland) · 2020Review
- Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot.Frontiers in neuroscience · 2020Article
- Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations.Frontiers in neurorobotics · 2020Article
- Latent Factors Limiting the Performance of sEMG-Interfaces.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.