A digital hardware design for real-time simulation of large neural-system models in physical settings
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Notice bibliographique
Résumé
The organization of neural systems reflects the specific complexities of the physical environments in which they operate. In order to address this relationship more directly, there is increasing interest in testing real-time neural simulations that interface with the physical world. We describe a new simulation approach that allows us to run large, sophisticated neural models on low-power embedded commodity hardware such as field-programmable gate arrays (FPGAs). A custom digital circuit was designed to approximate the collective outputs of populations of neurons that have correlated activity. These populations are taken to represent physical quantities in their spike rates. Information processing (e.g. function approximation) is taken to be determined by synaptic weights. This design is based on the Neural Engineering Framework (NEF), which bridges the gap between neural activity and higher-level behaviour [1,2]. Populations are grouped together on hardware execution components, which we call “population units”, that perform time-multiplexing in order to simulate 1024 populations per timestep. The population unit represents each population as a weighted sum of principal components of the neural tuning curves summed with a model of the associated high-frequency spike-related fluctuations. These principal components span the functions that weighted sums of spikes can approximate without being dominated by spike-related noise. Populations running on the same population unit use the same principal components, which saves memory and improves the speed of the simulation. Clustering is performed prior to simulation, which groups together populations which can be accurately represented by shared principal components. The hardware does not need to be customized or regenerated in order to simulate different networks. It can be programmed with a network description generated by a compiler that operates as a backend to the Nengo simulator. (Nengo was used to run the Spaun model [2].) The design was implemented on an FPGA and was able to run simulations of up to 45 thousand populations of neurons (a realistic surrogate model of about 1-5 million point neurons) in real-time at 12-bit accuracy on a 1 millisecond timestep. Input and output to the hardware is over Gigabit Ethernet and can be collected from a PC running Nengo for simulation control and visualization. This implementation allows real-time approximate simulation of about the same scale as the largest real-time GPU simulations in Nengo, but using much less power. Furthermore, the FPGA chip is suitable for embedded applications such as mobile robots, cameras, etc. This work greatly facilitates simulation of an essential feature of neural systems, their embodiment and interaction with the physical world.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle