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

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications

Active sensing in a dynamic olfactory world

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.This Perspective highlights the shift from the classic picture of olfaction as slow and static to a view in which dynamics play a critical role at many levels of sensing and behavior. Olfaction is now increasingly seen as a “wide-bandwidth temporal sense” (Ackels et al., 2021; Nagel et al., 2015). A parallel transition is occurring in odor-guided robot navigation, where it has been discovered that sensors can access temporal cues useful for navigation (Schmuker et al., 2016). We are only beginning to understand the implications of this paradigm-shift on our view of olfactory and olfactomotor circuits. Below we review insights into the information encoded in turbulent odor plumes and shine light on how animals could access this information. We suggest that a key challenge for olfactory neuroscience is to re-interpret work based on static stimuli in the context of natural odor dynamics and actively exploring animals.Peer reviewedFinal Published versio

Odorant specificity of three oscillations and the DC signal in the turtle olfactory bulb

Author Posting. © The Author(s), 2003. This is the author's version of the work. It is posted here by permission of Blackwell Publishing for personal use, not for redistribution. The definitive version was published in European Journal of Neuroscience 17 (2003): 436-446, doi:10.1046/j.1460-9568.2003.02457.x.The odour-induced population response in the in vivo turtle (Terepene sp.) olfactory bulb consists of three oscillatory components (rostral, middle and caudal) that ride on top of a DC signal. In an initial step to determine the functional role of these four signals, we compared the signals elicited by different odorants. Most experiments compared isoamyl acetate and cineole, odorants which have very different maps of input to olfactory bulb glomeruli in the turtle and a different perceptual quality for humans. We found substantial differences in the response to the two odours in the rise-time of the DC signal and in the latency of the middle oscillation. The rate of rise for cineole was twice as fast as that for isoamyl acetate. Similarly, the latency for the middle oscillation was about twice as long for isoamyl acetate as it was for cineole. On the other hand, a number of characteristics of the signals were not substantially different for the two odorants. These included the latency of the rostral and caudal oscillation, the frequency and envelope of all three oscillations and their locations and spatial extents. A smaller number of experiments were carried out with hexanone and hexanal; the oscillations elicited by these odorants did not appear to be different from those elicited by isoamyl acetate and cineole. Qualitative differences between the oscillations in the turtle and those in two invertebrate phyla suggest that different odour processing strategies may be used.Supported in part by NIH grant DC05259 and a Brown-Coxe fellowship from the Yale University School of Medicine

Sensor-based machine olfaction with neuromorphic models of the olfactory system

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings

Rapid Encoding and Perception of Novel Odors in the Rat

To gain insight into which parameters of neural activity are important in shaping the perception of odors, we combined a behavioral measure of odor perception with optical imaging of odor representations at the level of receptor neuron input to the rat olfactory bulb. Instead of the typical test of an animal's ability to discriminate two familiar odorants by exhibiting an operant response, we used a spontaneously expressed response to a novel odorant—exploratory sniffing—as a measure of odor perception. This assay allowed us to measure the speed with which rats perform spontaneous odor discriminations. With this paradigm, rats discriminated and began responding to a novel odorant in as little as 140 ms. This time is comparable to that measured in earlier studies using operant behavioral readouts after extensive training. In a subset of these trials, we simultaneously imaged receptor neuron input to the dorsal olfactory bulb with near-millisecond temporal resolution as the animal sampled and then responded to the novel odorant. The imaging data revealed that the bulk of the discrimination time can be attributed to the peripheral events underlying odorant detection: receptor input arrives at the olfactory bulb 100–150 ms after inhalation begins, leaving only 50–100 ms for central processing and response initiation. In most trials, odor discrimination had occurred even before the initial barrage of receptor neuron firing had ceased and before spatial maps of activity across glomeruli had fully developed. These results suggest a coding strategy in which the earliest-activated glomeruli play a major role in the initial perception of odor quality, and place constraints on coding and processing schemes based on simple changes in spike rate

Is there a space–time continuum in olfaction?

The coding of olfactory stimuli across a wide range of organisms may rely on fundamentally similar mechanisms in which a complement of specific odorant receptors on olfactory sensory neurons respond differentially to airborne chemicals to initiate the process by which specific odors are perceived. The question that we address in this review is the role of specific neurons in mediating this sensory system—an identity code—relative to the role that temporally specific responses across many neurons play in producing an olfactory perception—a temporal code. While information coded in specific neurons may be converted into a temporal code, it is also possible that temporal codes exist in the absence of response specificity for any particular neuron or subset of neurons. We review the data supporting these ideas, and we discuss the research perspectives that could help to reveal the mechanisms by which odorants become perceptions

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

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