Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb

The reshaping and decorrelation of similar activity patterns by neuronal networks can enhance their discriminability, storage, and retrieval. How can such networks learn to decorrelate new complex patterns, as they arise in the olfactory system? Using a computational network model for the dominant neural populations of the olfactory bulb we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The decorrelation is quite robust with respect to various types of perturbations of the reciprocity. The model parsimoniously captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures

Olfactory object recognition, segmentation, adaptation, target seeking, and discrimination by the network of the olfactory bulb and cortex: computational model and experimental data

Mammals are poor at individuating the separate components that comprise odor mixtures, but not when components enter environment serially and when there is top-down expectation. Li proposed in 1990 an odor segmentation mechanism using the centrifugal feedback from the olfactory cortex to the olfactory bulb. This feedback suppresses the bulbar responses to the ongoing and already recognized odors so that a subsequent addition of a foreground odor can be singled out for recognition. Additionally, the feedback can depend on context so as to, for example, enhance sensitivity to a target odor or improve discrimination between similar odors. I review experimental data that have since emerged in relation to the computational predictions and implications, and suggest experiments to test the model further

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe

Recordings from neurons in the insects' olfactory primary processing center, the antennal lobe (AL), reveal that the AL is able to process the input from chemical receptors into distinct neural activity patterns, called olfactory neural codes. These exciting results show the importance of neural codes and their relation to perception. The next challenge is to \emph{model the dynamics} of neural codes. In our study, we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a neural network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons, and is capable of producing unique olfactory neural codes for the tested odorants. Specifically, we (i) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii) characterize scent recognition, i.e., decision-making based on olfactory signals and (iii) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study answers a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns

ROLE OF INHIBITION AND SPIKING VARIABILITY IN ORTHO- AND RETRONASAL OLFACTORY PROCESSING

Odor perception is the impetus for important animal behaviors, most pertinently for feeding, but also for mating and communication. There are two predominate modes of odor processing: odors pass through the front of nose (ortho) while inhaling and sniffing, or through the rear (retro) during exhalation and while eating and drinking. Despite the importance of olfaction for an animal’s well-being and specifically that ortho and retro naturally occur, it is unknown whether the modality (ortho versus retro) is transmitted to cortical brain regions, which could significantly instruct how odors are processed. Prior imaging studies show different brain activity for the two modes, even with identical odors. However, odors are first processed via coordinated spiking of neurons in the olfactory bulb (OB) before being relayed downstream to higher cortical regions. Thus, we investigate responses of mitral cells (MC), one of principle neurons in OB, to ortho and retro stimulus to elucidate how the OB processes and codes this information. We analyze our collected in vivo rat data to inform modeling of the OB circuitry and MC responses to both modes of olfaction. Our efforts show that the OB does indeed process odors differently and that the temporal profile of each stimulus route to the OB is crucial for distinguishing ortho and retro odors. Additionally, we detail the rich spiking dynamics observed in our MC model and use a phenomenological model to explain the unexpected non-monotonic spike variability observed as weak-to-moderate background noise increases. Lastly in both anesthetized and awake rodents, we show that MCs with synaptic connections to cortical regions reliably transmit ortho versus retro input stimulus information. Drug manipulation affecting GABAA-mediated synaptic inhibition leads to changes in decoding of ortho/retro and only affects firing response for one of the two modes. We have not only shown that ortho versus retro information is encoded to downstream brain regions, but with models and analysis, we uncover the network dynamics that promote this encoding