Burst firing versus synchrony in a gap junction connected olfactory bulb mitral cell network model

A key player in olfactory processing is the olfactory bulb (OB) mitral cell (MC). We have used dual whole-cell patch-clamp recordings from the apical dendrite and cell soma of MCs to develop a passive compartmental model based on detailed morphological reconstructions of the same cells. Matching the model to traces recorded in experiments we find: Cm = 1.91 ± 0.20 μF cm−2, Rm = 3547 ± 1934 Ω cm2 and Ri = 173 ± 99 Ω cm. We have constructed a six MC gap-junction (GJ) network model of morphologically accurate MCs. These passive parameters (PPs) were then incorporated into the model with Na+, Kdr, and KA conductances and GJs from Migliore et al. (2005). The GJs were placed in the apical dendrite tuft (ADT) and their conductance adjusted to give a coupling ratio between MCs consistent with experimental findings (~0.04). Firing at ~50 Hz was induced in all six MCs with continuous current injections (0.05–0.07 nA) at 20 locations to the ADT of two of the MCs. It was found that MCs in the network synchronized better when they shared identical PPs rather than using their own PPs for the fit suggesting that the OB may have populations of MCs tuned for synchrony. The addition of calcium-activated potassium channels (iKCa) and L-type calcium channels (iCa(L)) (Bhalla and Bower, 1993) to the model enabled MCs to generate burst firing. However, the GJ coupling was no longer sufficient to synchronize firing. When cells were stimulated by a continuous current injection there was an initial period of asynchronous burst firing followed after ~120 ms by synchronous repetitive firing. This occurred as intracellular calcium fell due to reduced iCa(L) activity. The kinetics of one of the iCa(L) gate variables, which had a long activation time constant (τ ~ range 18–150 ms), was responsible for this fall in iCa(L). The model makes predictions about the nature of the kinetics of the calcium current that will need experimental verification

Distributed organization of a brain microcircuit analyzed by three-dimensional modeling : the olfactory bulb

The functional consequences of the laminar organization observed in cortical systems cannot be easily studied using standard experimental techniques, abstract theoretical representations, or dimensionally reduced models built from scratch. To solve this problem we have developed a full implementation of an olfactory bulb microcircuit using realistic three-dimensional (3D) inputs, cell morphologies, and network connectivity. The results provide new insights into the relations between the functional properties of individual cells and the networks in which they are embedded. To our knowledge, this is the first model of the mitral-granule cell network to include a realistic representation of the experimentally-recorded complex spatial patterns elicited in the glomerular layer (GL) by natural odor stimulation. Although the olfactory bulb, due to its organization, has unique advantages with respect to other brain systems, the method is completely general, and can be integrated with more general approaches to other systems. The model makes experimentally testable predictions on distributed processing and on the differential backpropagation of somatic action potentials in each lateral dendrite following odor learning, providing a powerful 3D framework for investigating the functions of brain microcircuits

Towards Brains in the Cloud: A Biophysically Realistic Computational Model of Olfactory Bulb

abstract: The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental data were synthesized to develop an extensible, open-source platform for modeling the mouse main olfactory bulb and other brain regions. A “virtual slice” model of a main olfactory bulb glomerular column that includes detailed models of tufted, mitral, and granule cells was created to investigate the underlying mechanisms of a gamma frequency oscillation pattern (“gamma fingerprint”) often observed in rodent bulbar local field potential recordings. The gamma fingerprint was reproduced by the model and a mechanistic hypothesis to explain aspects of the fingerprint was developed. A series of computational experiments tested the hypothesis. The results demonstrate the importance of interactions between electrical synapses, principal cell synaptic input strength differences, and granule cell inhibition in the formation of the gamma fingerprint. The model, data, results, and reproduction materials are accessible at https://github.com/justasb/olfactorybulb. The discussion includes a detailed description of mechanisms underlying the gamma fingerprint and how the model predictions can be tested experimentally. In summary, the modeling platform can be extended to include other types of cells, mechanisms and brain regions and can be used to investigate a wide range of experimentally testable hypotheses.Dissertation/ThesisDoctoral Dissertation Neuroscience 201

THE FIRST 3D MODEL OF THE OLFACTORY BULB:A STUDY ON ODOR LEARNING AND REPRESENTATION

Le attuali tecniche sperimentali non permettono di studiare come il bulbo olfattivo processa gli odori, quindi ne abbiamo sviluppato un modello tridimensionale su larga scala. Questi riproduce in maniera realistica gli stimoli dovuti alla presenza di odori naturali, le morfologie di cellule mitrali e granulari, insieme alla loro connettivit\ue0. Il nostro modello ritorna predizioni che sono sperimentalmente verificabili, fornendo un potente strumento per lo studio delle computazioni del bulbo olfattivo, quali ad esempio l'apprendimento e la rappresentazione degli odori. Con l'apprendimento di un odore, il bulbo olfattivo si auto-organizza in gruppi di colonne, ciascuna in corrispondenza di un singolo glomerulo o unit\ue0 glomerulare. Usando il nostro modello, abbiamo identificato i meccanismi su cui si basa la formazione di una o pi\uf9 colonne/unit\ue0 glomerulari in seguito alla presentazione di un odore. In aggiunta, abbiamo esaminato come le interazioni fra unit\ue0 glomerulari durante l'apprendimento possono influenzare la configurazione finale delle colonne. In seguito, abbiamo studiato come il bulbo olfattivo elabora gli ingressi provenienti dai recettori olfattivi attivati dagli odori naturali. Questo avviene su due livelli computazionali: lo strato glomerulare al livello di input, e lo strato delle cellule granulari al livello di output verso la corteccia olfattiva. Ci\uf2 suggerisce che le funzioni postulate nei circuiti glomerulari hanno come ruolo primario la trasformazione di un input complesso e disorganizzato in una rappresentazione dove i livelli di attivazione sono normalizzati, e il loro contrasto intensificato. Tuttavia l\u2019output del livello glomerulare non pu\uf2 sincronizzare l\u2019attivit\ue0 dei glomerulari. Pertanto, a livello delle cellule granulari, le interazioni dendrodendritiche inducono una decorrelazione temporale dei pattern rappresentativi dei vari odori, a sua volta dipendente da quella precedentemente realizzata nel livello glomerulare. Questi risultati forniscono importanti indizi riguardanti la computazione/rappresentazione del bulbo olfattivo, dimostrando l'importanza della sua auto-organizzazione modulare in unit\ue0 glomerulari. La sua organizzazione a strati \ue8 particolarmente importante per la rappresentazione degli odori naturali, dal momento che le aree da essi attivate sulla superficie del bubo sono sovrapposte.How the olfactory bulb processes odor input cannot be easily addressed using standard experimental techniques, therefore we have developed a large scale model of olfactory bulb, using realistic three-dimensional inputs, cell morphologies of mitral and granule cells, and connectivity. The model makes experimentally testable prediction, providing a powerful framework for investigating the olfactory bulb computations, such as the odor learning and representation. By the odor learning, the olfactory bulb organizes itself in synaptic columnar clusters related to individual glomeruli, called glomerular units. Using our 3D model, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning. Together, we have analyzed how the olfactory bulb processes inputs from olfactory receptor neurons activated by natural odors. This is realized through two computational tiers: the glomerular layer at the site of input, and the granule cell level at the site of output to the olfactory cortex. We suggest that the postulated functions of glomerular circuits have as their primary role transforming a complex and disorganized input into a contrast-enhanced and normalized representation, but cannot provide for synchronization of the distributed glomerular outputs. By contrast, at the granule cell layer, the dendrodendritic interactions mediate temporal decorrelation, which we show is dependent on the preceding contrast enhancement by the glomerular layer. The results provide the first insights into the successive operations in the olfactory bulb, and demonstrate the significance of the modular organization around glomeruli. This layered organization is especially important for natural odor inputs, because they activate many overlapping glomeruli