Gain control via feedforward inhibition in noisy and delayed neural circuits
Notice bibliographique
Résumé
The control and scaling of the input-output behavior of neural networks, or gain control, is one of the main strategies used by neural systems for the processing and gating of information. This input-output behavior is often described by the so-called f-I curve, which shows the output firing rate as a function of the input current to the neuron or neural circuit [1]. In particular, the slope of such a dependency constitutes a useful marker of the behavior of the neuron. If the slope of the f-I curve is high, small changes in the input current will be mapped by the cell into large changes in the output firing rate, which implies an increasing of the sensitivity of the neuron to weak stimuli. On the other hand, a small slope of the f-I curve translates large changes in the input current to small changes in the output firing rate, allowing the neuron to encode a broad range of stimulus intensities into a narrow range of firing rates. Several gain control behaviors have been found to be particularly relevant for the gating and transformation of information in neural circuits. For instance, common biological mechanisms have been found to produce subtractive effects in the f-I curve [2], while a few others are thought to induce divisive (i.e. changes in the slope of the f-I curve)or other nonlinear effects [2-4]. However, a common mechanism able to induce these three broad classes of gain control has not been described up to date. We present here (see [5] for further details) a study of gain control in a feedforward neural circuit inspired by the electrosensory lateral-line lobe of weakly electric fish. Our model displays three different gain control regimes: subtractive, divisive and non-monotonic. The neural circuit can shift from one regime to the other by a simple modulation of the synaptic strength of the inhibitory feedforward pathway present in the circuit, which, in the electrosensory circuit used as example, is known to present long-term synaptic plasticity mechanisms. To our knowledge, this is the first example of a network which presents all these gain controls at once. We further study the effect of noise and delays on this gain control mechanism, showing in particular that delays in the feedforward inhibitory pathway linearize the input-to-output relationship of the network, acting as an extra source of noise in this sense. Finally, using physiological evidences, we apply our model to the case of the divisive gain control observed in vivo in weakly electric fish. Our work illustrates how subtractive, divisive and non-monotonic gain control may be obtained in inhibitory feedforward neural circuits. In addition, the analysis of the conditions in which the f-I response curve of superficial pyramidal neurons becomes non-monotonic reveals a novel nonlinear gain control mechanism which agrees with in vitro experimental recordings in the electric fish [6].
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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 | 0,001 |
| 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.
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