A spiking-neuron model of memory encoding and replay in hippocampus
Notice bibliographique
Résumé
Hippocampal cells replay sequences of neural activity that have been experienced in the past, implicating the hippocampus in episodic memory. We present a spiking-neuron attractor network model that can encode arbitrary experiences in a combination of learned connection weights and neural activity. The model can then recall part or all of the encoded experience based on contextual cues, simulating hippocampal replay. The simulation is run using the Neural Engineering Framework (NEF) [1] and the Nengo neural modelling tool [2]. It consists of 77740 simulated spiking LIF neurons, divided into populations representing hippocampal areas CA3 and CA1, and part of EC. The NEF provides a method for dynamically representing vector-values and computing transformations on them with neural populations. Our model takes as input a sequence of high-dimensional vectors representing sensory information entering the hippocampus from EC. These vectors can represent position information (place cells) or any other form of processed sensory data. Strong recurrently connected populations in area CA3 cycle through a series of temporal indices, and a Hebbian learning rule allows for these indices to be uniquely generated for a given environment. Using the NEF to compute a binding operator [3], the network associatively binds the sensory input vectors with an index. These bindings are stored in the neural activity of the recurrent CA1-EC loop. The network can switch between encoding and recall modes, and when in recall mode can look up the corresponding index bound to the experience being recalled and, using the same indexing system in CA3, can complete the pattern of neural activity corresponding to its previously remembered experience. The model was run in encoding mode on a sequence of 5 different random HRR vectors in 64 dimensions over a period of 2.5 seconds. It was then given the first in the sequence of vectors as input. After 1 second of re-orientation, the model was switched into recall mode. Figure Figure1A1A shows spike trains from neurons in the model at a point in the original run and at the corresponding point in the recalled sequence. For comparison, figure figure1B1B shows hippocampal recordings from rodents in [4]. Figure 1 Neural spike data collected from the model (A) and rats (B) showing similar neural spike data during the experiment (RUN) and during recall (POST). To the best of our knowledge, this is the first spiking neural model to take into account both timing and sensory tuning of hippocampal cells, while exhibiting the ability to recall previously experienced sequences. In addition, it operates on arbitrary sensory vectors as input, thus not constraining the model to spatial or non-spatial information and allowing it to be extended to perform spatial navigation tasks.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».