Guoshi Li

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

Cholinergic inputs from the basal forebrain regulate multiple olfactory bulb (OB) functions, including odor discrimination, perceptual learning, and short-term memory. Previous studies have shown that nicotinic cholinergic receptor activation sharpens mitral cell chemoreceptive fields, likely via intraglomerular circuitry. Muscarinic cholinergic activation is less well understood, though muscarinic receptors 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 muscarinic neuromodulatory effects. Our simulations show how nicotinic activation within glomerular circuits sharpens mitral cell chemoreceptive 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 excitability 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, rather than the existence, of mitral cell action potentials and perform their computations with respect to a spike timing-based metric. 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.

Impact of infralimbic inputs on intercalated amygdala neurons: A biophysical modeling study

Intercalated (ITC) amygdala neurons regulate fear expression by controlling impulse traffic between the input (basolateral amygdala; BLA) and output (central nucleus; Ce) stations of the amygdala for conditioned fear responses. Previously, stimulation of the infralimbic (IL) cortex was found to reduce fear expression and the responsiveness of Ce neurons to BLA inputs. These effects were hypothesized to result from the activation of ITC cells projecting to Ce. However, ITC cells inhibit each other, leading to the question of how IL inputs could overcome the inter-ITC inhibition to regulate the responses of Ce neurons to aversive conditioned stimuli (CSs). To investigate this, we first developed a compartmental model of a single ITC cell that could reproduce their bistable electroresponsive properties, as observed experimentally. Next, we generated an ITC network that implemented the experimentally observed short-term synaptic plasticity of inhibitory inter-ITC connections. Model experiments showed that strongly adaptive CS-related BLA inputs elicited persistent responses in ITC cells despite the presence of inhibitory interconnections. The sustained CS-evoked activity of ITC cells resulted from an unusual slowly deinactivating K(+) current. Finally, over a wide range of stimulation strengths, brief IL activation caused a marked increase in the firing rate of ITC neurons, leading to a persistent decrease in Ce output, despite inter-ITC inhibition. Simulations revealed that this effect depended on the bistable properties and synaptic heterogeneity of ITC neurons. These results support the notion that IL inputs are in a strategic position to control extinction of conditioned fear via the activation of ITC neurons.

A model of electrophysiological heterogeneity in periglomerular cells

Olfactory bulb (OB) periglomerular (PG) cells are heterogeneous with respect to several features, including morphology, connectivity, patterns of protein expression, and electrophysiological properties. However, these features rarely correlate with one another, suggesting that the differentiating properties of PG cells may arise from multiple independent adaptive variables rather than representing discrete cell classes. We use computational modeling to assess this hypothesis with respect to electrophysiological properties. Specifically, we show that the heterogeneous electrophysiological properties demonstrated in PG cell recordings can be explained solely by differences in the relative expression levels of ion channel species in the cell, without recourse to modifying channel kinetic properties themselves. This PG cell model can therefore be used as the basis for diverse cellular and network-level analyses of OB computations. Moreover, this simple basis for heterogeneity contributes to an emerging hypothesis that glomerular-layer interneurons may be better described as a single population expressing distributions of partially independent, potentially plastic properties, rather than as a set of discrete cell classes.

Functional differentiation of cholinergic and noradrenergic modulation in a biophysical model of olfactory bulb granule cells

Olfactory bulb granule cells are modulated by both acetylcholine (ACh) and norepinephrine (NE), but the effects of these neuromodulators have not been clearly distinguished. We used detailed biophysical simulations of granule cells, both alone and embedded in a microcircuit with mitral cells, to measure and distinguish the effects of ACh and NE on cellular and microcircuit function. Cholinergic and noradrenergic modulatory effects on granule cells were based on data obtained from slice experiments; specifically, ACh reduced the conductance densities of the potassium M current and the calcium-dependent potassium current, whereas NE nonmonotonically regulated the conductance density of an ohmic potassium current. We report that the effects of ACh and NE on granule cell physiology are distinct and functionally complementary to one another. ACh strongly regulates granule cell firing rates and afterpotentials, whereas NE bidirectionally regulates subthreshold membrane potentials. When combined, NE can regulate the ACh-induced expression of afterdepolarizing potentials and persistent firing. In a microcircuit simulation developed to investigate the effects of granule cell neuromodulation on mitral cell firing properties, ACh increased spike synchronization among mitral cells, whereas NE modulated the signal-to-noise ratio. Coapplication of ACh and NE both functionally improved the signal-to-noise ratio and enhanced spike synchronization among mitral cells. In summary, our computational results support distinct and complementary roles for ACh and NE in modulating olfactory bulb circuitry and suggest that NE may play a role in the regulation of cholinergic function.

A coupled-oscillator model of olfactory bulb gamma oscillations

The olfactory bulb transforms not only the information content of the primary sensory representation, but also its underlying coding metric. High-variance, slow-timescale primary odor representations are transformed by bulbar circuitry into secondary representations based on principal neuron spike patterns that are tightly regulated in time. This emergent fast timescale for signaling is reflected in gamma-band local field potentials, presumably serving to efficiently integrate olfactory sensory information into the temporally regulated information networks of the central nervous system. To understand this transformation and its integration with interareal coordination mechanisms requires that we understand its fundamental dynamical principles. Using a biophysically explicit, multiscale model of olfactory bulb circuitry, we here demonstrate that an inhibition-coupled intrinsic oscillator framework, pyramidal resonance interneuron network gamma (PRING), best captures the diversity of physiological properties exhibited by the olfactory bulb. Most importantly, these properties include global zero-phase synchronization in the gamma band, the phase-restriction of informative spikes in principal neurons with respect to this common clock, and the robustness of this synchronous oscillatory regime to multiple challenging conditions observed in the biological system. These conditions include substantial heterogeneities in afferent activation levels and excitatory synaptic weights, high levels of uncorrelated background activity among principal neurons, and spike frequencies in both principal neurons and interneurons that are irregular in time and much lower than the gamma frequency. This coupled cellular oscillator architecture permits stable and replicable ensemble responses to diverse sensory stimuli under various external conditions as well as to changes in network parameters arising from learning-dependent synaptic plasticity.

Coherent olfactory bulb gamma oscillations arise from coupling independent columnar oscillators

Spike timing-based representations of sensory information depend on embedded dynamical frameworks within neural structures that establish the rules of local computation and interareal communication. Here, we investigated the dynamical properties of mouse olfactory bulb circuitry. Neurochemical activation or optogenetic stimulation of sensory afferents evoked persistent (minutes) gamma oscillations in the local field potential. These oscillations arose from slower, GABA(A) receptor-independent intracolumnar oscillators coupled by GABA(A)-ergic synapses into a faster, broadly coherent network oscillation. Consistent with the theoretical properties of coupled-oscillator networks, the spatial extent of zero-phase coherence was bounded in slices by the reduced density of lateral interactions. The intact in vivo network, however, exhibits long-range lateral interactions theoretically sufficient to enable zero-phase coherence across the complete network. These coupled-oscillator dynamics thereby establish a common clock, robust to biological heterogeneities, that is capable of supporting gamma-band phase coding across the spiking output of olfactory bulb principal neurons.

Computational Modeling: A Tool for New Psychiatric Medication Development

omputational models are being used in a variety of medical applications, including drug discovery research, where genomic and proteomic software tools facili-tate modeling complex intracellular pathways. Increasing understanding of brain functioning due to advances in basic neuroscience techniques and imaging modalities has led to the emergence of computational model-ing as an important tool for studying brain mechanisms and circuits. Re-cent advances in the areas of cellular neurophysiology and synaptic plas-ticity permit the development of bio-physically realistic models that more closely approximate learning, both

Generative Biophysical Modeling of Dynamical Networks in the Olfactory System

Generative models are computational models designed to generate appropriate values for all of their embedded variables, thereby simulating the response properties of a complex system based on the coordinated interactions of a multitude of physical mechanisms. In systems neuroscience, generative models are generally biophysically based compartmental models of neurons and networks that are explicitly multiscale, being constrained by experimental data at multiple levels of organization from cellular membrane properties to large-scale network dynamics. As such, they are able to explain the origins of emergent properties in complex systems, and serve as tests of sufficiency and as quantitative instantiations of working hypotheses that may be too complex to simply intuit. Moreover, when adequately constrained, generative biophysical models are able to predict novel experimental outcomes, and consequently are powerful tools for experimental design. We here outline a general strategy for the iterative design and implementation of generative, multiscale biophysical models of neural systems. We illustrate this process using our ongoing, iteratively developing model of the mammalian olfactory bulb. Because the olfactory bulb exhibits diverse and interesting properties at multiple scales of organization, it is an attractive system in which to illustrate the value of generative modeling across scales.