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A Two-Layer Biophysical Model of Cholinergic Neuromodulation in Olfactory Bulb

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A Two-Layer Biophysical Model of Cholinergic Neuromodulation in Olfactory Bulb The Journal of Neuroscience, February 13, 2013 • 33(7):3037–3058 • 3037 Systems/Circuits A Two-Layer Biophysical Model of Cholinergic Neuromodulation in Olfactory Bulb Guoshi Li and Thomas A. Cleland Computational Physiology Laboratory, Department of Psychology, Cornell University, Ithaca, New York 14853 Cholinergic inputs from the basal forebrain regulate multiple olfactory bulb (OB) functions, including odor discrimination, perceptual learning,andshort-termmemory.Previousstudieshaveshownthatnicotiniccholinergicreceptoractivationsharpensmitralcellchemo- receptive fields, likely via intraglomerular circuitry. Muscarinic cholinergic activation is less well understood, though muscarinic recep- tors are implicated in olfactory learning and in the regulation of synchronized oscillatory dynamics in hippocampus and cortex. To understand the mechanisms underlying cholinergic neuromodulation in OB, we developed a biophysical model of the OB neuronal network including both glomerular layer and external plexiform layer (EPL) computations and incorporating both nicotinic and mus- carinic neuromodulatory effects. Our simulations show how nicotinic activation within glomerular circuits sharpens mitral cell chemo- receptive fields, even in the absence of EPL circuitry, but does not facilitate intrinsic oscillations or spike synchronization. In contrast, muscarinic receptor activation increases mitral cell spike synchronization and field oscillatory power by potentiating granule cell excit- ability and lateral inhibitory interactions within the EPL, but it has little effect on mitral cell firing rates and hence does not sharpen olfactory representations under a rate metric. These results are consistent with the theory that EPL interactions regulate the timing, ratherthantheexistence,ofmitralcellactionpotentialsandperformtheircomputationswithrespecttoaspiketiming-basedmetric.This general model suggests that the roles of nicotinic and muscarinic receptors in olfactory bulb are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations. Introduction receptive fields of MCs, the principal output neurons of the OB, The olfactory bulb (OB) is innervated by cholinergic fibers aris- as well as animals’ behavioral discrimination of similar odors ing from the basal forebrain (Macrides et al., 1981; Za´borszky et (Chaudhury et al., 2009), likely via an intraglomerular circuit that al., 1986) that strongly regulate odor representations and olfac- is independent of OB topography (Mandairon et al., 2006). How- tory plasticity (Wilson et al., 2004). Disruption of cholinergic ever, the concerted effects of muscarinic neuromodulation have activity within the olfactory system reduces rodents’ discrimina- not yet been computationally elucidated in OB models. tion of perceptually similar odorants and impairs short-term In slice preparations from rodent OB and accessory olfactory odor memory (Roman et al., 1993; Linster et al., 2001; Mandai- bulb, muscarinic agonists potentiate excitatory synaptic inputs ron et al., 2006), whereas enhancing cholinergic function sharp- from MCs to granule cells (GCs) and also enhance GC inhibition ens mitral cell (MC) tuning curves (Ma and Luo, 2012) and of MCs by increasing GC intrinsic excitability (Pressler et al., improves discrimination among similar odorants (Doty et al., 2007; Ghatpande and Gelperin, 2009; Smith and Araneda, 2010; 1999; Mandairon et al., 2006; Chaudhury et al., 2009). Aging and Takahashi and Kaba, 2010). These dense reciprocal synaptic in- neurodegenerative disorders both have been associated with the teractions between the MC and GC populations comprise the degeneration of olfactory cholinergic regulation (Durand et al., external plexiform layer (EPL) of the OB (Fig. 1A) and are the 1998; Mandairon et al., 2011), suggesting that understanding the primary source of gamma-band local field potential (LFP) oscil- circuit mechanisms of cholinergic neuromodulation in the OB lations within the OB (Neville and Haberly, 2003; Lagier et al., could provide valuable insights into these processes. 2004). As gamma oscillations are indicative of the dynamical syn- The effects of acetylcholine (ACh) in the OB are mediated by chronization of neuronal populations (Kashiwadani et al., 1999; both nicotinic and muscarinic cholinergic receptor subtypes Rojas-Líbano and Kay, 2008), and the regulation of spike timing (Castillo et al., 1999). Nicotinic activation sharpens the chemo- is thought to be an important feature of OB output (Linster and Cleland, 2010; Gschwend et al., 2012), muscarinic effects on this circuit are likely to regulate afferent information exported from Received June 13, 2012; revised Nov. 21, 2012; accepted Dec. 14, 2012. Authorcontributions:T.A.C.designedresearch;G.L.performedresearch;G.L.analyzeddata;G.L.andT.A.C.wrote the OB even if mitral cell spike rates are not affected. Moreover, as the paper. muscarinic activation in vivo presumably coincides with nicotinic The authors declare no competing financial interests. activation, it is important to understand how these two mecha- This work was supported by NIDCD Grants DC009948 and DC012249. nisms interact. Correspondence should be addressed to Guoshi Li, Department of Psychology, Cornell University, Ithaca, NY 14853. E-mail: [email protected]. We developed a biophysically constrained network model of DOI:10.1523/JNEUROSCI.2831-12.2013 the OB including both glomerular-layer and EPL computations Copyright © 2013 the authors 0270-6474/13/333037-22$15.00/0 and incorporating both nicotinic and muscarinic neuromodula- 3038 • J. Neurosci., February 13, 2013 • 33(7):3037–3058 Li and Cleland • Cholinergic Model of Olfactory Bulb GCs, for a total of 150 model cells. Each glomerulus in the model con- tained one MC and one PGC that were coactivated by afferent (odor) input[i.e., the PGC was modeled as a PGo cell (Shao et al., 2009)]. Each MC was reciprocally synaptically connected to its coglomerular PGC; specifically, the MC excited the PGC dendritic spine, whereas the latter inhibited the MC tuft compartment via graded inhibition (Fig. 1A; Cle- land and Sethupathy, 2006). In the EPL, deep to the glomerular layer, the lateral dendrites of mitral cells also make a large number of reciprocal synaptic contacts with granule cell spines (Isaacson and Strowbridge, 1998). As these lateral dendrites of mitral cells extend broadly across the olfactory bulb, and support action potential propagation, the MC–GC connectivity matrix is not likely to depend strongly on distance [see Cleland (2010) for discussion of this point]. In the model, each MC connected to GCs with a uniform probability (p ϭ 0.3). Hence, for a network with 25 MCs and 100 GCs, one MC received an average of 30 GC inputs and one GC received an average of 7.5 MC inputs. The location of each dendrodendritic contact along the MC lateral dendrite was deter- mined by drawing randomly from a uniform distribution. Because of the explicit modeling of synaptic locations along the length of the dendritic cable, the network dynamics of the model thereby incorporated a realistic distribution of lateral propagation delays. Mitral cell model. All model neurons in the network were conductance- based compartmental models. The MC model consisted of a soma (single compartment; diameter, 20 ␮m; length, 25 ␮m), a primary (apical) den- drite (five compartments; diameter, 3.5 ␮m; length, 370 ␮m), a second- ary (lateral) dendrite (seven compartments; diameter, 3.4 ␮m; length, 500 ␮m), and a glomerular tuft at the distal end of the primary dendrite (one compartment; diameter, 0.5 ␮m; length, 20 ␮m). The values for the specific membrane resistance, membrane capacitance, and cytoplasmic ϭ ⍀ 2 ϭ ␮ (axial) resistivity were, respectively, Rm 30 K -cm , Cm 1.2 F/ 2 ϭ ⍀ cm , and Ra 70 -cm. Cellular dimensions and passive electrotonic parameters were based on experimental measurements (Shen et al., Ϫ 1999). The leak current reversal potential (EL) was set to 60 mV, and the resulting resting potential was Ϫ68.8 mV. The MC model cell contained the following active ionic currents, con- sistent with previous modeling studies (Bhalla and Bower, 1993; Davison et al., 2000; Bathellier et al., 2006; Rubin and Cleland, 2006): fast, spike- generating (I ) and persistent (I ) sodium currents; a potassium de- Figure1. A,SchematicrepresentationofdendrodendriticsynapticconnectivityamongMCs, Na NaP layed rectifier (I ); two transient potassium currents (fast-inactivating PGCs,andGCs.Thefirstcomputationallayeroftheolfactorybulbistheglomerularlayer(GL),in DR IA and slow-inactivating IKS); an L-type calcium current (ICaL); and a which each glomerulus comprises a single input from a convergent olfactory sensory neuron ϩ Ca 2 -dependent potassium current (I ). The kinetics of I , I , I , population(OSNinput)alongwithdendrodendriticinteractionsbetweenanMCandaPGC.Two KCa Na DR CaL and I were based on those of a previous MC model by Bhalla and glomeruli are depicted. Mitral cells innervating different glomeruli interact with one another KCa Bower (1993), whereas the kinetics of I and I were derived from (indirectly) in the second computational layer, the EPL. Here, MC lateral dendrites excite GC NaP KS Wang (1993), who modeled layer V cortical pyramidal neurons. The dendrites and GCs inhibit MCs, producing a functionally lateral inhibitory network among MCs. mathematical description of I was taken from a previous model by See Materials and Methods for details. B, Example
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