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Marachlian E, Huerta R, Locatelli FF. Gain modulation and odor concentration invariance in early olfactory networks. PLoS Comput Biol 2023; 19:e1011176. [PMID: 37343029 DOI: 10.1371/journal.pcbi.1011176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
The broad receptive field of the olfactory receptors constitutes the basis of a combinatorial code that allows animals to detect and discriminate many more odorants than the actual number of receptor types that they express. One drawback is that high odor concentrations recruit lower affinity receptors which can lead to the perception of qualitatively different odors. Here we addressed the contribution that signal-processing in the antennal lobe makes to reduce concentration dependence in odor representation. By means of calcium imaging and pharmacological approach we describe the contribution that GABA receptors play in terms of the amplitude and temporal profiles of the signals that convey odor information from the antennal lobes to higher brain centers. We found that GABA reduces the amplitude of odor elicited signals and the number of glomeruli that are recruited in an odor-concentration-dependent manner. Blocking GABA receptors decreases the correlation among glomerular activity patterns elicited by different concentrations of the same odor. In addition, we built a realistic mathematical model of the antennal lobe that was used to test the viability of the proposed mechanisms and to evaluate the processing properties of the AL network under conditions that cannot be achieved in physiology experiments. Interestingly, even though based on a rather simple topology and cell interactions solely mediated by GABAergic lateral inhibitions, the AL model reproduced key features of the AL response upon different odor concentrations and provides plausible solutions for concentration invariant recognition of odors by artificial sensors.
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Affiliation(s)
- Emiliano Marachlian
- Instituto de Fisiología Biología Molecular y Neurociencias (IFIByNE-UBA-CONICET) and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Ramón Huerta
- BioCircuits Institute, University of California San Diego, La Jolla, California, United States of America
| | - Fernando F Locatelli
- Instituto de Fisiología Biología Molecular y Neurociencias (IFIByNE-UBA-CONICET) and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
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2
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Lazar AA, Liu T, Yeh CH. The functional logic of odor information processing in the Drosophila antennal lobe. PLoS Comput Biol 2023; 19:e1011043. [PMID: 37083547 PMCID: PMC10156017 DOI: 10.1371/journal.pcbi.1011043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/03/2023] [Accepted: 03/22/2023] [Indexed: 04/22/2023] Open
Abstract
Recent advances in molecular transduction of odorants in the Olfactory Sensory Neurons (OSNs) of the Drosophila Antenna have shown that the odorant object identity is multiplicatively coupled with the odorant concentration waveform. The resulting combinatorial neural code is a confounding representation of odorant semantic information (identity) and syntactic information (concentration). To distill the functional logic of odor information processing in the Antennal Lobe (AL) a number of challenges need to be addressed including 1) how is the odorant semantic information decoupled from the syntactic information at the level of the AL, 2) how are these two information streams processed by the diverse AL Local Neurons (LNs) and 3) what is the end-to-end functional logic of the AL? By analyzing single-channel physiology recordings at the output of the AL, we found that the Projection Neuron responses can be decomposed into a concentration-invariant component, and two transient components boosting the positive/negative concentration contrast that indicate onset/offset timing information of the odorant object. We hypothesized that the concentration-invariant component, in the multi-channel context, is the recovered odorant identity vector presented between onset/offset timing events. We developed a model of LN pathways in the Antennal Lobe termed the differential Divisive Normalization Processors (DNPs), which robustly extract the semantics (the identity of the odorant object) and the ON/OFF semantic timing events indicating the presence/absence of an odorant object. For real-time processing with spiking PN models, we showed that the phase-space of the biological spike generator of the PN offers an intuit perspective for the representation of recovered odorant semantics and examined the dynamics induced by the odorant semantic timing events. Finally, we provided theoretical and computational evidence for the functional logic of the AL as a robust ON-OFF odorant object identity recovery processor across odorant identities, concentration amplitudes and waveform profiles.
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Affiliation(s)
- Aurel A Lazar
- Department of Electrical Engineering, Columbia University, New York, NY, United States of America
| | - Tingkai Liu
- Department of Electrical Engineering, Columbia University, New York, NY, United States of America
| | - Chung-Heng Yeh
- Department of Electrical Engineering, Columbia University, New York, NY, United States of America
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Pannunzi M, Nowotny T. Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies. PLoS Comput Biol 2021; 17:e1009583. [PMID: 34898600 PMCID: PMC8668107 DOI: 10.1371/journal.pcbi.1009583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 10/22/2021] [Indexed: 11/28/2022] Open
Abstract
When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic ("ephaptic") interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. We find that NSIs improve mixture ratio detection and plume structure sensing and do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. The best performance is achieved when both mechanisms work in synergy. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.
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Affiliation(s)
- Mario Pannunzi
- School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom
| | - Thomas Nowotny
- School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom
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4
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Lopez-Hazas J, Montero A, Rodriguez FB. Influence of bio-inspired activity regulation through neural thresholds learning in the performance of neural networks. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Pannunzi M, Nowotny T. Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies.. [DOI: 10.1101/2020.07.23.217216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
AbstractWhen flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla.We found that NSIs improve mixture ratio detection and plume structure sensing and they do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.Author summaryMyelin is important to isolate neurons and avoid disruptive electrical interference between them; it can be found in almost any neural assembly. However, there are a few exceptions to this rule and it remains unclear why. One particularly interesting case is the electrical interaction between olfactory sensory neurons co-housed in the sensilla of insects. Here, we created a computational model of the early stages of the Drosophila olfactory system and observed that the electrical interference between olfactory receptor neurons can be a useful trait that can help flies, and other insects, to navigate the complex plumes of odorants in their natural environment.With the model we were able to shed new light on the trade-off of adopting this mechanism: We found that the non-synaptic interactions (NSIs) improve both the identification of the concentration ratio in mixtures of odorants and the discrimination of odorant mixtures emanating from a single source from odorants emitted from separate sources – both highly advantageous. However, they also decrease the dynamic range of the olfactory sensory neurons – a clear disadvantage.
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Borthakur A, Cleland TA. A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction. Front Neurosci 2019; 13:656. [PMID: 31316339 PMCID: PMC6610532 DOI: 10.3389/fnins.2019.00656] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 06/07/2019] [Indexed: 01/07/2023] Open
Abstract
We have developed a spiking neural network (SNN) algorithm for signal restoration and identification based on principles extracted from the mammalian olfactory system and broadly applicable to input from arbitrary sensor arrays. For interpretability and development purposes, we here examine the properties of its initial feedforward projection. Like the full algorithm, this feedforward component is fully spike timing-based, and utilizes online learning based on local synaptic rules such as spike timing-dependent plasticity (STDP). Using an intermediate metric to assess the properties of this initial projection, the feedforward network exhibits high classification performance after few-shot learning without catastrophic forgetting, and includes a none of the above outcome to reflect classifier confidence. We demonstrate online learning performance using a publicly available machine olfaction dataset with challenges including relatively small training sets, variable stimulus concentrations, and 3 years of sensor drift.
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Affiliation(s)
- Ayon Borthakur
- Computational Physiology Laboratory, Field of Computational Biology, Cornell University, Ithaca, NY, United States
| | - Thomas A. Cleland
- Computational Physiology Laboratory, Department of Psychology, Cornell University, Ithaca, NY, United States
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Tsai KT, Hu CK, Li KW, Hwang WL, Chou YH. Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity. Sci Rep 2018; 8:8027. [PMID: 29795277 PMCID: PMC5966413 DOI: 10.1038/s41598-018-26286-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 05/09/2018] [Indexed: 01/15/2023] Open
Abstract
Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.
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Affiliation(s)
- Kuo-Ting Tsai
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan.,Institute of Physics, Academia Sinica, Taipei, Taiwan
| | - Chin-Kun Hu
- Institute of Physics, Academia Sinica, Taipei, Taiwan.,Neuroscience Program of Academia Sinica, Academia Sinica, Taipei, Taiwan.,National Center for Theoretical Sciences, National Tsing Hua University, Hsinchu, Taiwan.,Business School, University of Shanghai for Science and Technology, Shanghai, China
| | - Kuan-Wei Li
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan
| | - Wen-Liang Hwang
- Neuroscience Program of Academia Sinica, Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Ya-Hui Chou
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan. .,Neuroscience Program of Academia Sinica, Academia Sinica, Taipei, Taiwan.
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Müller J, Nawrot M, Menzel R, Landgraf T. A neural network model for familiarity and context learning during honeybee foraging flights. BIOLOGICAL CYBERNETICS 2018; 112:113-126. [PMID: 28917001 DOI: 10.1007/s00422-017-0732-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 08/30/2017] [Indexed: 06/07/2023]
Abstract
How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.
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Affiliation(s)
- Jurek Müller
- Institute for Computer Science, Free University Berlin, Berlin, Germany
| | - Martin Nawrot
- Computational Systems Neuroscience, Institute for Zoology, University of Cologne, Cologne, Germany
| | - Randolf Menzel
- Institute for Neurobiology, Free University Berlin, Berlin, Germany
| | - Tim Landgraf
- Institute for Computer Science, Free University Berlin, Berlin, Germany.
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9
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Naveros F, Garrido JA, Carrillo RR, Ros E, Luque NR. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks. Front Neuroinform 2017; 11:7. [PMID: 28223930 PMCID: PMC5293783 DOI: 10.3389/fninf.2017.00007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 01/18/2017] [Indexed: 12/12/2022] Open
Abstract
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.
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Affiliation(s)
- Francisco Naveros
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Jesus A Garrido
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Richard R Carrillo
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Niceto R Luque
- Vision Institute, Aging in Vision and Action LabParis, France; CNRS, INSERM, Pierre and Marie Curie UniversityParis, France
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Daviaux Y, Cremoux S, Tallet J, Amarantini D, Cornu C, Deschamps T. I can’t reach it! Focus on theta sensorimotor rhythm toward a better understanding of impaired action–perception coupling. Neuroscience 2016; 339:32-46. [DOI: 10.1016/j.neuroscience.2016.09.033] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 09/06/2016] [Accepted: 09/20/2016] [Indexed: 01/16/2023]
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Montero A, Huerta R, Rodriguez FB. Regulation of specialists and generalists by neural variability improves pattern recognition performance. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.09.073] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Sorooshyari S, Huerta R, de Lecea L. A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition. Front Neurol 2015; 6:32. [PMID: 25767461 PMCID: PMC4341569 DOI: 10.3389/fneur.2015.00032] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 02/08/2015] [Indexed: 12/14/2022] Open
Abstract
Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep–wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically driven data do not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders.
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Affiliation(s)
| | - Ramón Huerta
- BioCircuits Institute, University of California San Diego , La Jolla, CA , USA
| | - Luis de Lecea
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine , Stanford, CA , USA
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13
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Mosqueiro TS, Huerta R. Computational models to understand decision making and pattern recognition in the insect brain. CURRENT OPINION IN INSECT SCIENCE 2014; 6:80-85. [PMID: 25593793 PMCID: PMC4289906 DOI: 10.1016/j.cois.2014.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Odor stimuli reaching olfactory systems of mammals and insects are characterized by remarkable non-stationary and noisy time series. Their brains have evolved to discriminate subtle changes in odor mixtures and find meaningful variations in complex spatio-temporal patterns. Insects with small brains can effectively solve two computational tasks: identify the presence of an odor type and estimate the concentration levels of the odor. Understanding the learning and decision making processes in the insect brain can not only help us to uncover general principles of information processing in the brain, but it can also provide key insights to artificial chemical sensing. Both olfactory learning and memory are dominantly organized in the Antennal Lobe (AL) and the Mushroom Bodies (MBs). Current computational models yet fail to deliver an integrated picture of the joint computational roles of the AL and MBs. This review intends to provide an integrative overview of the computational literature analyzed in the context of the problem of classification (odor discrimination) and regression (odor concentration estimation), particularly identifying key computational ingredients necessary to solve pattern recognition.
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Shlizerman E, Riffell JA, Kutz JN. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe. Front Comput Neurosci 2014; 8:70. [PMID: 25165442 PMCID: PMC4131428 DOI: 10.3389/fncom.2014.00070] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 06/20/2014] [Indexed: 11/13/2022] Open
Abstract
The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) 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 suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.
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Affiliation(s)
- Eli Shlizerman
- Department of Applied Mathematics, University of Washington Seattle, WA, USA
| | | | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington Seattle, WA, USA
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15
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Mejias JF, Payeur A, Selin E, Maler L, Longtin A. Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays. Front Comput Neurosci 2014; 8:19. [PMID: 24616694 PMCID: PMC3934558 DOI: 10.3389/fncom.2014.00019] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 02/08/2014] [Indexed: 11/30/2022] Open
Abstract
The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry-also known as "open-loop feedback"-, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves) via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain.
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Affiliation(s)
- Jorge F. Mejias
- Department of Physics, University of OttawaOttawa, ON, Canada
- Center for Neural Science, New York UniversityNew York, NY, USA
| | | | - Erik Selin
- Department of Physics, University of OttawaOttawa, ON, Canada
| | - Leonard Maler
- Department of Cell and Molecular Medicine, University of OttawaOttawa, ON, Canada
| | - André Longtin
- Department of Physics, University of OttawaOttawa, ON, Canada
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