Self-organization in the olfactory system: one shot odor recognition in insects

We show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons

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

Neural Circuit Dynamics and Ensemble Coding in the Locust and Fruit Fly Olfactory System

Raw sensory information is usually processed and reformatted by an organism’s brain to carry out tasks like identification, discrimination, tracking and storage. The work presented in this dissertation focuses on the processing strategies of neural circuits in the early olfactory system in two insects, the locust and the fruit fly. Projection neurons (PNs) in the antennal lobe (AL) respond to an odor presented to the locust’s antennae by firing in slow information-carrying temporal patterns, consistent across trials. Their downstream targets, the Kenyon cells (KCs) of the mushroom body (MB), receive input from large ensembles of transiently synchronous PNs at a time. The information arrives in slices of time corresponding to cycles of oscillatory activity originating in the AL. In the first part of the thesis, ensemble-level analysis techniques are used to understand how the AL-MB system deals with the problem of identifying odors across different concentrations. Individual PN odor responses can vary dramatically with concentration, but invariant patterns in PN ensemble responses are shown to allow odor identity to be extracted across a wide range of intensities by the KCs. Second, the sensitivity of the early olfactory system to stimulus history is examined. The PN ensemble and the KCs are found capable of tracking an odor in most conditions where it is pulsed or overlapping with another, but they occasionally fail (are masked) or reach intermediate states distinct from those seen for the odors presented alone or in a static mixture. The last part of the thesis focuses on the development of new recording techniques in the fruit fly, an organism with well-studied genetics and behavior. Genetically expressed fluorescent sensors of calcium offer the best available option to study ensemble activity in the fly. Here, simultaneous electrophysiology and two-photon imaging are used to estimate the correlation between G-CaMP, a popular genetically expressible calcium sensor, and electrical activity in PNs. The sensor is found to have poor temporal resolution and to miss significant spiking activity. More generally, this combination of electrophysiology and imaging enables explorations of functional connectivity and calibrated imaging of ensemble activity in the fruit fly.</p

Adaptive regulation of sparseness by feedforward inhibition

In the mushroom body of insects, odors are represented by very few spikes in a small number of neurons, a highly efficient strategy known as sparse coding. Physiological studies of these neurons have shown that sparseness is maintained across thousand-fold changes in odor concentration. Using a realistic computational model, we propose that sparseness in the olfactory system is regulated by adaptive feedforward inhibition. When odor concentration changes, feedforward inhibition modulates the duration of the temporal window over which the mushroom body neurons may integrate excitatory presynaptic input. This simple adaptive mechanism could maintain the sparseness of sensory representations across wide ranges of stimulus conditions

Neurons and circuits for odor processing in the piriform cortex

Increased understanding of the early stages of olfaction has lead to a renewed interest in the higher brain regions responsible for forming unified ‘odor images’ from the chemical components detected by the nose. The piriform cortex, which is one of the first cortical destinations of olfactory information in mammals, is a primitive paleocortex that is critical for the synthetic perception of odors. Here we review recent work that examines the cellular neurophysiology of the piriform cortex. Exciting new findings have revealed how the neurons and circuits of the piriform cortex process odor information, demonstrating that, despite its superficial simplicity, the piriform cortex is a remarkably subtle and intricate neural circuit

Relating Neural Dynamics to Olfactory Coding and Behavior

Sensory stimuli often evoke temporal patterns of spiking activity across a population of neurons in the early processing stages. What features of these spatiotemporal responses encode behaviorally relevant information, and how dynamic processing of sensory signals facilitates information processing are fundamental problems in sensory neuroscience that remain to be understood. In this thesis, I have investigated these issues using a relatively simple invertebrate model (locusts; Schistocerca americana). In locusts, odorants are transduced into electrical signals by olfactory sensory neurons in the antenna and are subsequently relayed to the downstream neural circuit in the antennal lobe (analogous to the olfactory bulb in vertebrates). We found that the sensory input evoked by an odorant could vary depending on whether the stimulus was presented solitarily or in an overlapping sequence following another cue. These inconsistent sensory inputs triggered dynamic reorganization of ensemble activity in the downstream antennal lobe. As a result, we found that the neural activities evoked by an odorant pattern-matched across conditions, thereby providing a basis for invariant stimulus recognition. Notably, we found that only the combination of neurons activated by an odorant was conserved across conditions. The temporal structure of the ensemble neural responses, on the other hand, varied depending on stimulus history: synchronous ensemble firings when stimulated by a novel odorant compared to asynchronous activities induced by a redundant stimulus. Furthermore, these neural responses were refined on a slower timescale (on the order of minutes, i.e. happening over trials) such that the same information about odorant identity and intensity was represented with fewer spikes. We validated these interpretations of our physiological data using results from multiple quantitative behavioral assays. In sum, this thesis work provides fundamental insights regarding behaviorally important features of olfactory signal processing in a relatively simple biological olfactory system

Calcium imaging in the ant Camponotus fellah reveals a conserved odour-similarity space in insects and mammals

<p>Abstract</p> <p>Background</p> <p>Olfactory systems create representations of the chemical world in the animal brain. Recordings of odour-evoked activity in the primary olfactory centres of vertebrates and insects have suggested similar rules for odour processing, in particular through spatial organization of chemical information in their functional units, the glomeruli. Similarity between odour representations can be extracted from across-glomerulus patterns in a wide range of species, from insects to vertebrates, but comparison of odour similarity in such diverse taxa has not been addressed. In the present study, we asked how 11 aliphatic odorants previously tested in honeybees and rats are represented in the antennal lobe of the ant <it>Camponotus fellah</it>, a social insect that relies on olfaction for food search and social communication.</p> <p>Results</p> <p>Using calcium imaging of specifically-stained second-order neurons, we show that these odours induce specific activity patterns in the ant antennal lobe. Using multidimensional analysis, we show that clustering of odours is similar in ants, bees and rats. Moreover, odour similarity is highly correlated in all three species.</p> <p>Conclusion</p> <p>This suggests the existence of similar coding rules in the neural olfactory spaces of species among which evolutionary divergence happened hundreds of million years ago.</p