ArticleFrontiers in computational neuroscience2016
Toward an Integration of Deep Learning and Neuroscience.
Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 2 of them syntheses that pooled it.
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
176 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Meta-analysis of human prediction error for incentives, perception, cognition, and action.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Pooled it
- Article
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- Recognising dog movement with behaviour-specific machine learning models: bout length as a biologically relevant parameter for window size.BMC veterinary research · 2026Article
- Multi-timescale Computation by Astrocytes.bioRxiv : the preprint server for biology · 2026Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
- Learning spatial hearing via innate mechanisms.PLoS computational biology · 2025Article
- NeuroQ: Quantum-Inspired Brain Emulation.Biomimetics (Basel, Switzerland) · 2025Article
- SpyDen: simplifying molecular and structural analysis across spines and dendrites.Bioinformatics (Oxford, England) · 2025Article
- STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG.Scientific reports · 2025Article
- A normative principle governing memory transfer in cerebellar motor learning.Nature communications · 2025Article
- Article
- CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING.bioRxiv : the preprint server for biology · 2025Article
- High-level visual representations in the human brain are aligned with large language models.Nature machine intelligence · 2025Article
- Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks.PloS one · 2025Article
- Irrationality in humans and creativity in AI.Frontiers in artificial intelligence · 2025Review
- Exploring neural architectures for simultaneously recognizing multiple visual attributes.Scientific reports · 2024Article
- Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability-Plasticity Dilemma.Brain sciences · 2024Article
- Shaping dynamical neural computations using spatiotemporal constraints.Biochemical and biophysical research communications · 2024Review
116 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.
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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.