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A modular neural network model of grasp movement generation

https://doi.org/10.1101/742189
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Connectional Heterogeneity of the Ventral Part of the Macaque Area 46
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A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex
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Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
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ImageNet: A large-scale hierarchical image database
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inferredcites →https://doi.org/10.1109/tbme.2007.901024openalex-snapshot
OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement
inferredcites →https://doi.org/10.1109/tnsre.2014.2364776openalex-snapshot
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inferredcites →https://doi.org/10.1146/annurev.neuro.31.060407.125547openalex-snapshot
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inferredcites →https://doi.org/10.1152/jn.01001.2007openalex-snapshot
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inferredcites →https://doi.org/10.1152/jn.1995.73.2.891openalex-snapshot
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inferredcites →https://doi.org/10.1152/jn.1997.78.4.2226openalex-snapshot
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inferredcites →https://doi.org/10.1152/jn.2000.83.5.2580openalex-snapshot
Selectivity for the Shape, Size, and Orientation of Objects for Grasping in Neurons of Monkey Parietal Area AIP
inferredcites →https://doi.org/10.1159/000114375openalex-snapshot
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inferredcites →https://doi.org/10.1162/089976604774201631openalex-snapshot
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inferredcites →https://doi.org/10.1371/journal.pcbi.1003963openalex-snapshot
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inferredcites →https://doi.org/10.1523/eneuro.0085-16.2016openalex-snapshot
The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type
inferredcites →https://doi.org/10.1523/jneurosci.1745-11.2011openalex-snapshot
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inferredcites →https://doi.org/10.1523/jneurosci.3343-10.2010openalex-snapshot
Context-Specific Grasp Movement Representation in Macaque Ventral Premotor Cortex
inferredcites →https://doi.org/10.1523/jneurosci.3594-14.2015openalex-snapshot
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inferredcites →https://doi.org/10.1523/jneurosci.5479-08.2009openalex-snapshot
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inferredcites →https://doi.org/10.1523/jneurosci.5568-12.2013openalex-snapshot
Reach and Gaze Representations in Macaque Parietal and Premotor Grasp Areas
inferredcites →https://doi.org/10.3389/fncom.2015.00043openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
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Task-Driven Convolutional Recurrent Models of the Visual System
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Similarity of Neural Network Representations Revisited
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Object vision to hand action in macaque parietal, premotor, and motor cortices

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
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Quantifying behavior to understand the brain
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Low-dimensional dynamics for working memory and time encoding
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Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory units
inferred← citeshttps://doi.org/10.1101/2021.01.14.426741openalex-snapshot
The role of mental simulation in primate physical inference abilities
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One-to-one mapping between deep network units and real neurons uncovers a visual population code for social behavior
inferred← citeshttps://doi.org/10.1101/2023.07.18.549575openalex-snapshot
Nonlinear manifolds underlie neural population activity during behaviour
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Constructing Biologically Constrained RNNs via Dale’s Backprop and Topologically-Informed Pruning
inferred← citeshttps://doi.org/10.1126/sciadv.adw4970openalex-snapshot
Constructing biologically constrained RNNs via Dale’s backpropagation and topologically informed pruning
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Two “What” Networks in the Human Brain
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The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1008621openalex-snapshot
Neural manifold under plasticity in a goal driven learning behaviour
inferred← citeshttps://doi.org/10.31219/osf.io/58qwjopenalex-snapshot
Rethinking brain-wide interactions through multi-region “network of networks” models
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Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity
inferred← citeshttps://doi.org/10.7554/elife.67256openalex-snapshot
Rotational dynamics in motor cortex are consistent with a feedback controller
inferred← citeshttps://doi.org/10.7554/elife.67620openalex-snapshot
Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity
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