1
|
Zhao Y, Liu J, Dosher BA, Lu ZL. Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling. J Vis 2024; 24:8. [PMID: 38780934 PMCID: PMC11131338 DOI: 10.1167/jov.24.5.8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/13/2024] [Indexed: 05/25/2024] Open
Abstract
Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.
Collapse
Affiliation(s)
- Yukai Zhao
- Center for Neural Science, New York University, New York, NY, USA
| | - Jiajuan Liu
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Barbara Anne Dosher
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Zhong-Lin Lu
- Division of Arts and Sciences, NYU Shanghai, Shanghai, China
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
- NYU-ECNU Institute of Brain and Cognitive Neuroscience, Shanghai, China
| |
Collapse
|
2
|
Ribeiro LDJA, Bastos VHDV, Coertjens M. Breath-holding as model for the evaluation of EEG signal during respiratory distress. Eur J Appl Physiol 2024; 124:753-760. [PMID: 38105311 DOI: 10.1007/s00421-023-05379-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE Research describes the existence of a relationship between cortical activity and the regulation of bulbar respiratory centers through the evaluation of the electroencephalographic (EEG) signal during respiratory challenges. For example, we found evidences of a reduction in the frequency of the EEG (alpha band) in both divers and non-divers during apnea tests. For instance, this reduction was more prominent in divers due to the greater physiological disturbance resulting from longer apnea time. However, little is known about EEG adaptations during tests of maximal apnea, a test that voluntarily stops breathing and induces dyspnea. RESULTS Through this mini-review, we verified that a protocol of successive apneas triggers a significant increase in the maximum apnea time and we hypothesized that successive maximal apnea test could be a powerful model for the study of cortical activity during respiratory distress. CONCLUSION Dyspnea is a multifactorial symptom and we believe that performing a successive maximal apnea protocol is possible to understand some factors that determine the sensation of dyspnea through the EEG signal, especially in people not trained in apnea.
Collapse
Affiliation(s)
- Lucas de Jesus Alves Ribeiro
- Physiotherapy Department, Universidade Federal do Delta do Parnaíba, Av. São Sebastião, CEP: 64.202-020, Parnaíba, PI, 2819, Brazil
- Brain Mapping and Functionality Laboratory, Universidade Federal do Delta do Parnaíba, Piauí, Brazil
| | - Victor Hugo do Vale Bastos
- Physiotherapy Department, Universidade Federal do Delta do Parnaíba, Av. São Sebastião, CEP: 64.202-020, Parnaíba, PI, 2819, Brazil
- Postgraduate Program in Biomedical Sciences, Universidade Federal do Delta do Parnaíba, Piauí, Brazil
- Brain Mapping and Functionality Laboratory, Universidade Federal do Delta do Parnaíba, Piauí, Brazil
| | - Marcelo Coertjens
- Physiotherapy Department, Universidade Federal do Delta do Parnaíba, Av. São Sebastião, CEP: 64.202-020, Parnaíba, PI, 2819, Brazil.
- Postgraduate Program in Biomedical Sciences, Universidade Federal do Delta do Parnaíba, Piauí, Brazil.
| |
Collapse
|
3
|
Sun K, Ray S, Gupta N, Aldworth Z, Stopfer M. Olfactory system structure and function in newly hatched and adult locusts. Sci Rep 2024; 14:2608. [PMID: 38297144 PMCID: PMC10830560 DOI: 10.1038/s41598-024-52879-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
An important question in neuroscience is how sensory systems change as animals grow and interact with the environment. Exploring sensory systems in animals as they develop can reveal how networks of neurons process information as the neurons themselves grow and the needs of the animal change. Here we compared the structure and function of peripheral parts of the olfactory pathway in newly hatched and adult locusts. We found that populations of olfactory sensory neurons (OSNs) in hatchlings and adults responded with similar tunings to a panel of odors. The morphologies of local neurons (LNs) and projection neurons (PNs) in the antennal lobes (ALs) were very similar in both age groups, though they were smaller in hatchlings, they were proportional to overall brain size. The odor evoked responses of LNs and PNs were also very similar in both age groups, characterized by complex patterns of activity including oscillatory synchronization. Notably, in hatchlings, spontaneous and odor-evoked firing rates of PNs were lower, and LFP oscillations were lower in frequency, than in the adult. Hatchlings have smaller antennae with fewer OSNs; removing antennal segments from adults also reduced LFP oscillation frequency. Thus, consistent with earlier computational models, the developmental increase in frequency is due to increasing intensity of input to the oscillation circuitry. Overall, our results show that locusts hatch with a fully formed olfactory system that structurally and functionally matches that of the adult, despite its small size and lack of prior experience with olfactory stimuli.
Collapse
Affiliation(s)
- Kui Sun
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Subhasis Ray
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
- Plaksha University, Sahibzada Ajit Singh Nagar, Punjab, India
| | - Nitin Gupta
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
- Indian Institute of Technology Kanpur, Kanpur, 208016, India
| | - Zane Aldworth
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Mark Stopfer
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
4
|
Chang H, Unni AP, Tom MT, Cao Q, Liu Y, Wang G, Llorca LC, Brase S, Bucks S, Weniger K, Bisch-Knaden S, Hansson BS, Knaden M. Odorant detection in a locust exhibits unusually low redundancy. Curr Biol 2023; 33:5427-5438.e5. [PMID: 38070506 DOI: 10.1016/j.cub.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023]
Abstract
Olfactory coding, from insects to humans, is canonically considered to involve considerable across-fiber coding already at the peripheral level, thereby allowing recognition of vast numbers of odor compounds. We show that the migratory locust has evolved an alternative strategy built on highly specific odorant receptors feeding into a complex primary processing center in the brain. By collecting odors from food and different life stages of the locust, we identified 205 ecologically relevant odorants, which we used to deorphanize 48 locust olfactory receptors via ectopic expression in Drosophila. Contrary to the often broadly tuned olfactory receptors of other insects, almost all locust receptors were found to be narrowly tuned to one or very few ligands. Knocking out a single receptor using CRISPR abolished physiological and behavioral responses to the corresponding ligand. We conclude that the locust olfactory system, with most olfactory receptors being narrowly tuned, differs from the so-far described olfactory systems.
Collapse
Affiliation(s)
- Hetan Chang
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Afairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Anjana P Unni
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Megha Treesa Tom
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Qian Cao
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yang Liu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Guirong Wang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Afairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Lucas Cortés Llorca
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Sabine Brase
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Sascha Bucks
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Kerstin Weniger
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Sonja Bisch-Knaden
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Bill S Hansson
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany
| | - Markus Knaden
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans Knoell Strasse 8, 07745 Jena, Germany.
| |
Collapse
|
5
|
Meyer-Ortmanns H. Heteroclinic networks for brain dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1276401. [PMID: 38020242 PMCID: PMC10663269 DOI: 10.3389/fnetp.2023.1276401] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023]
Abstract
Heteroclinic networks are a mathematical concept in dynamic systems theory that is suited to describe metastable states and switching events in brain dynamics. The framework is sensitive to external input and, at the same time, reproducible and robust against perturbations. Solutions of the corresponding differential equations are spatiotemporal patterns that are supposed to encode information both in space and time coordinates. We focus on the concept of winnerless competition as realized in generalized Lotka-Volterra equations and report on results for binding and chunking dynamics, synchronization on spatial grids, and entrainment to heteroclinic motion. We summarize proposals of how to design heteroclinic networks as desired in view of reproducing experimental observations from neuronal networks and discuss the subtle role of noise. The review is on a phenomenological level with possible applications to brain dynamics, while we refer to the literature for a rigorous mathematical treatment. We conclude with promising perspectives for future research.
Collapse
Affiliation(s)
- Hildegard Meyer-Ortmanns
- School of Science, Constructor University, Bremen, Germany
- Complexity Science Hub Vienna, Vienna, Austria
| |
Collapse
|
6
|
Barwich AS, Severino GJ. The Wire Is Not the Territory: Understanding Representational Drift in Olfaction With Dynamical Systems Theory. Top Cogn Sci 2023. [PMID: 37690113 DOI: 10.1111/tops.12689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Representational drift is a phenomenon of increasing interest in the cognitive and neural sciences. While investigations are ongoing for other sensory cortices, recent research has demonstrated the pervasiveness in which it occurs in the piriform cortex for olfaction. This gradual weakening and shifting of stimulus-responsive cells has critical implications for sensory stimulus-response models and perceptual decision-making. While representational drift may complicate traditional sensory processing models, it could be seen as an advantage in olfaction, as animals live in environments with constantly changing and unpredictable chemical information. Non-topographical encoding in the olfactory system may aid in contextualizing reactions to promiscuous odor stimuli, facilitating adaptive animal behavior and survival. This article suggests that traditional models of stimulus-(neural) response mapping in olfaction may need to be reevaluated and instead motivates the use of dynamical systems theory as a methodology and conceptual framework.
Collapse
Affiliation(s)
- Ann-Sophie Barwich
- Cognitive Science Program, Indiana University
- Department of History and Philosophy of Science and Medicine, Indiana University
| | | |
Collapse
|
7
|
Brecelj T, Petrič T. Stable Heteroclinic Channel Networks for Physical Human-Humanoid Robot Collaboration. SENSORS (BASEL, SWITZERLAND) 2023; 23:1396. [PMID: 36772433 PMCID: PMC9921709 DOI: 10.3390/s23031396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/10/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Human-robot collaboration is one of the most challenging fields in robotics, as robots must understand human intentions and suitably cooperate with them in the given circumstances. But although this is one of the most investigated research areas in robotics, it is still in its infancy. In this paper, human-robot collaboration is addressed by applying a phase state system, guided by stable heteroclinic channel networks, to a humanoid robot. The base mathematical model is first defined and illustrated on a simple three-state system. Further on, an eight-state system is applied to a humanoid robot to guide it and make it perform different movements according to the forces exerted on its grippers. The movements presented in this paper are squatting, standing up, and walking forwards and backward, while the motion velocity depends on the magnitude of the applied forces. The method presented in this paper proves to be a suitable way of controlling robots by means of physical human-robot interaction. As the phase state system and the robot movements can both be further extended to make the robot execute many other tasks, the proposed method seems to provide a promising way for further investigation and realization of physical human-robot interaction.
Collapse
|
8
|
Brennan C, Aggarwal A, Pei R, Sussillo D, Proekt A. One dimensional approximations of neuronal dynamics reveal computational strategy. PLoS Comput Biol 2023; 19:e1010784. [PMID: 36607933 PMCID: PMC9821456 DOI: 10.1371/journal.pcbi.1010784] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 12/01/2022] [Indexed: 01/07/2023] Open
Abstract
The relationship between neuronal activity and computations embodied by it remains an open question. We develop a novel methodology that condenses observed neuronal activity into a quantitatively accurate, simple, and interpretable model and validate it on diverse systems and scales from single neurons in C. elegans to fMRI in humans. The model treats neuronal activity as collections of interlocking 1-dimensional trajectories. Despite their simplicity, these models accurately predict future neuronal activity and future decisions made by human participants. Moreover, the structure formed by interconnected trajectories-a scaffold-is closely related to the computational strategy of the system. We use these scaffolds to compare the computational strategy of primates and artificial systems trained on the same task to identify specific conditions under which the artificial agent learns the same strategy as the primate. The computational strategy extracted using our methodology predicts specific errors on novel stimuli. These results show that our methodology is a powerful tool for studying the relationship between computation and neuronal activity across diverse systems.
Collapse
Affiliation(s)
- Connor Brennan
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Adeeti Aggarwal
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Rui Pei
- Department of Psychology, Stanford University, Palo Alto, California, United States of America
| | - David Sussillo
- Stanford Neurosciences Institute, Stanford University, Palo Alto, California, United States of America
- Department of Electrical Engineering, Stanford University, Palo Alto, California, United States of America
| | - Alex Proekt
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| |
Collapse
|
9
|
Kiroy V, Kosenko P, Smolikov A, Saevskiy A, Aslanyan E, Shaposhnikov P, Rebrov Y, Arsenyev F. Changes in spontaneous and odorant-induced single-unit activity of mitral/tufted neurons of the rat olfactory bulb during xylazine-tiletamine-zolazepam anesthesia. IBRO Neurosci Rep 2022; 13:207-214. [PMID: 36117854 PMCID: PMC9474852 DOI: 10.1016/j.ibneur.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/04/2022] [Accepted: 09/01/2022] [Indexed: 12/02/2022] Open
Abstract
The nature and severity of mitral/tufted (M/T) cells reactions to odorants presented in anesthesia depend on various factors, and, above all, the nature and concentration of the odor, anesthesia, and the functional state of the olfactory bulb (OB). Compared to wakefulness, under anesthesia, the intensity of OB M/T cells responses to the odorants presented increases. However, the influence of anesthesia dynamics on the intensity of such responses has not been studied. To address this problem in rats, the activity of M/T cells and the local field potentials (LFP) in OB were recorded in the course of xylazine-tiletamine-zolazepam (XTZ) anesthesia. It has been shown that in the course of the anesthesia, the average frequency of background and odorant-induced single-unit activity of M/T cells increases, while the dominant frequency value of LFP in the gamma frequency range (90–170 Hz), on the contrary, decreases. The observed effects are assumed to be associated with changes in the functional state of the OB and systems for processing olfactory information in anesthesia.
Collapse
|
10
|
Lu ZL, Dosher BA. Current directions in visual perceptual learning. NATURE REVIEWS PSYCHOLOGY 2022; 1:654-668. [PMID: 37274562 PMCID: PMC10237053 DOI: 10.1038/s44159-022-00107-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 06/06/2023]
Abstract
The visual expertise of adult humans is jointly determined by evolution, visual development, and visual perceptual learning. Perceptual learning refers to performance improvements in perceptual tasks after practice or training in the task. It occurs in almost all visual tasks, ranging from simple feature detection to complex scene analysis. In this Review, we focus on key behavioral aspects of visual perceptual learning. We begin by describing visual perceptual learning tasks and manipulations that influence the magnitude of learning, and then discuss specificity of learning. Next, we present theories and computational models of learning and specificity. We then review applications of visual perceptual learning in visual rehabilitation. Finally, we summarize the general principles of visual perceptual learning, discuss the tension between plasticity and stability, and conclude with new research directions.
Collapse
Affiliation(s)
- Zhong-Lin Lu
- Division of Arts and Sciences, New York University Shanghai, Shanghai, China
- Center for Neural Science, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
- Institute of Brain and Cognitive Science, New York University - East China Normal University, Shanghai, China
| | | |
Collapse
|
11
|
Krishnamurthy K, Hermundstad AM, Mora T, Walczak AM, Balasubramanian V. Disorder and the Neural Representation of Complex Odors. Front Comput Neurosci 2022; 16:917786. [PMID: 36003684 PMCID: PMC9393645 DOI: 10.3389/fncom.2022.917786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022] Open
Abstract
Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.
Collapse
Affiliation(s)
- Kamesh Krishnamurthy
- Joseph Henry Laboratories of Physics and Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Ann M. Hermundstad
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Thierry Mora
- Laboratoire de Physique Statistique, UMR8550, CNRS, UPMC and École Normale Supérieure, Paris, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique Théorique, UMR8549m CNRS, UPMC and École Normale Supérieure, Paris, France
| | - Vijay Balasubramanian
- David Rittenhouse and Richards Laboratories, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Vijay Balasubramanian
| |
Collapse
|
12
|
Brinkman BAW, Yan H, Maffei A, Park IM, Fontanini A, Wang J, La Camera G. Metastable dynamics of neural circuits and networks. APPLIED PHYSICS REVIEWS 2022; 9:011313. [PMID: 35284030 PMCID: PMC8900181 DOI: 10.1063/5.0062603] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 01/31/2022] [Indexed: 05/14/2023]
Abstract
Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.
Collapse
Affiliation(s)
| | - H. Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, People's Republic of China
| | | | | | | | - J. Wang
- Authors to whom correspondence should be addressed: and
| | - G. La Camera
- Authors to whom correspondence should be addressed: and
| |
Collapse
|
13
|
Visual learning in a virtual reality environment upregulates immediate early gene expression in the mushroom bodies of honey bees. Commun Biol 2022; 5:130. [PMID: 35165405 PMCID: PMC8844430 DOI: 10.1038/s42003-022-03075-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 01/26/2022] [Indexed: 11/08/2022] Open
Abstract
Free-flying bees learn efficiently to solve numerous visual tasks. Yet, the neural underpinnings of this capacity remain unexplored. We used a 3D virtual reality (VR) environment to study visual learning and determine if it leads to changes in immediate early gene (IEG) expression in specific areas of the bee brain. We focused on kakusei, Hr38 and Egr1, three IEGs that have been related to bee foraging and orientation, and compared their relative expression in the calyces of the mushroom bodies, the optic lobes and the rest of the brain after color discrimination learning. Bees learned to discriminate virtual stimuli displaying different colors and retained the information learned. Successful learners exhibited Egr1 upregulation only in the calyces of the mushroom bodies, thus uncovering a privileged involvement of these brain regions in associative color learning and the usefulness of Egr1 as a marker of neural activity induced by this phenomenon.
Collapse
|
14
|
Abstract
The smell of coffee is the same whether it is smelled in a coffee shop or grocery shop (different backgrounds), on a hot day or a cold day (different ambient conditions), after lunch or dinner (different temporal contexts), or using a deep inhalation or normal inhalation (different stimulus dynamics). This feat of pattern recognition that is still difficult to achieve in artificial chemical sensing systems is performed by most sensory systems for their survival. How is this capability achieved? We explored this issue. We found that there are two orthogonal ensembles of neurons, one activated during stimulus presence (ON neurons) and one activated after its termination (OFF neurons), and both contribute to this important computation in a complementary fashion. Invariant stimulus recognition is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus are perturbed in a multitude of ways, how can this computational capability be achieved? We examine this issue in the locust olfactory system. We find that locusts trained in an appetitive-conditioning assay robustly recognize the trained odorant independent of variations in stimulus durations, dynamics, or history, or changes in background and ambient conditions. However, individual- and population-level neural responses vary unpredictably with many of these variations. Our results indicate that linear statistical decoding schemes, which assign positive weights to ON neurons and negative weights to OFF neurons, resolve this apparent confound between neural variability and behavioral stability. Furthermore, simplification of the decoder using only ternary weights ({+1, 0, −1}) (i.e., an “ON-minus-OFF” approach) does not compromise performance, thereby striking a fine balance between simplicity and robustness.
Collapse
|
15
|
Peter A, Stauch BJ, Shapcott K, Kouroupaki K, Schmiedt JT, Klein L, Klon-Lipok J, Dowdall JR, Schölvinck ML, Vinck M, Schmid MC, Fries P. Stimulus-specific plasticity of macaque V1 spike rates and gamma. Cell Rep 2021; 37:110086. [PMID: 34879273 PMCID: PMC8674536 DOI: 10.1016/j.celrep.2021.110086] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 06/28/2021] [Accepted: 11/11/2021] [Indexed: 11/02/2022] Open
Abstract
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here, we show in awake macaque area V1 that both repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects show some persistence on the timescale of minutes. Gamma increases are specific to the presented stimulus location. Further, repetition effects on gamma and on firing rates generalize to images of natural objects. These findings support the notion that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters.
Collapse
Affiliation(s)
- Alina Peter
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; International Max Planck Research School for Neural Circuits, 60438 Frankfurt, Germany.
| | - Benjamin Johannes Stauch
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; International Max Planck Research School for Neural Circuits, 60438 Frankfurt, Germany
| | - Katharine Shapcott
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Frankfurt Institute for Advanced Studies, 60438 Frankfurt, Germany
| | - Kleopatra Kouroupaki
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Joscha Tapani Schmiedt
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Liane Klein
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; International Max Planck Research School for Neural Circuits, 60438 Frankfurt, Germany; Max Planck Institute for Brain Research, 60438 Frankfurt, Germany
| | - Johanna Klon-Lipok
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Max Planck Institute for Brain Research, 60438 Frankfurt, Germany
| | - Jarrod Robert Dowdall
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; International Max Planck Research School for Neural Circuits, 60438 Frankfurt, Germany
| | - Marieke Louise Schölvinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Michael Christoph Schmid
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; University of Fribourg, Faculty of Science and Medicine, Chemin du Musée 5, 1700 Fribourg, Switzerland; Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Framlington Place, Newcastle NE2 4HH, UK
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; International Max Planck Research School for Neural Circuits, 60438 Frankfurt, Germany; Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, the Netherlands.
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Schittler Neves F, Timme M. Decoding complex state space trajectories for neural computing. CHAOS (WOODBURY, N.Y.) 2021; 31:123105. [PMID: 34972334 DOI: 10.1063/5.0053429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
In biological neural circuits as well as in bio-inspired information processing systems, trajectories in high-dimensional state-space encode the solutions to computational tasks performed by complex dynamical systems. Due to the high state-space dimensionality and the number of possible encoding trajectories rapidly growing with input signal dimension, decoding these trajectories constitutes a major challenge on its own, in particular, as exponentially growing (space or time) requirements for decoding would render the original computational paradigm inefficient. Here, we suggest an approach to overcome this problem. We propose an efficient decoding scheme for trajectories emerging in spiking neural circuits that exhibit linear scaling with input signal dimensionality. We focus on the dynamics near a sequence of unstable saddle states that naturally emerge in a range of physical systems and provide a novel paradigm for analog computing, for instance, in the form of heteroclinic computing. Identifying simple measures of coordinated activity (synchrony) that are commonly applicable to all trajectories representing the same percept, we design robust readouts whose sizes and time requirements increase only linearly with the system size. These results move the conceptual boundary so far hindering the implementation of heteroclinic computing in hardware and may also catalyze efficient decoding strategies in spiking neural networks in general.
Collapse
Affiliation(s)
- Fabio Schittler Neves
- Center for Advancing Electronics Dresden (CFAED) and Institute for Theoretical Physics, TU Dresden, 01062 Dresden, Germany
| | - Marc Timme
- Center for Advancing Electronics Dresden (CFAED) and Institute for Theoretical Physics, TU Dresden, 01062 Dresden, Germany
| |
Collapse
|
18
|
Abstract
[Figure: see text].
Collapse
Affiliation(s)
- Liqun Luo
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
19
|
Sirio Carmantini G, Schittler Neves F, Timme M, Rodrigues S. Stochastic facilitation in heteroclinic communication channels. CHAOS (WOODBURY, N.Y.) 2021; 31:093130. [PMID: 34598472 DOI: 10.1063/5.0054485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/26/2021] [Indexed: 06/13/2023]
Abstract
Biological neural systems encode and transmit information as patterns of activity tracing complex trajectories in high-dimensional state spaces, inspiring alternative paradigms of information processing. Heteroclinic networks, naturally emerging in artificial neural systems, are networks of saddles in state space that provide a transparent approach to generate complex trajectories via controlled switches among interconnected saddles. External signals induce specific switching sequences, thus dynamically encoding inputs as trajectories. Recent works have focused either on computational aspects of heteroclinic networks, i.e., Heteroclinic Computing, or their stochastic properties under noise. Yet, how well such systems may transmit information remains an open question. Here, we investigate the information transmission properties of heteroclinic networks, studying them as communication channels. Choosing a tractable but representative system exhibiting a heteroclinic network, we investigate the mutual information rate (MIR) between input signals and the resulting sequences of states as the level of noise varies. Intriguingly, MIR does not decrease monotonically with increasing noise. Intermediate noise levels indeed maximize the information transmission capacity by promoting an increased yet controlled exploration of the underlying network of states. Complementing standard stochastic resonance, these results highlight the constructive effect of stochastic facilitation (i.e., noise-enhanced information transfer) on heteroclinic communication channels and possibly on more general dynamical systems exhibiting complex trajectories in state space.
Collapse
Affiliation(s)
| | - Fabio Schittler Neves
- Center for Advancing Electronics Dresden (cfaed) and Institute for Theoretical Physics, TU Dresden, 01062 Dresden, Germany
| | - Marc Timme
- Center for Advancing Electronics Dresden (cfaed) and Institute for Theoretical Physics, TU Dresden, 01062 Dresden, Germany
| | - Serafim Rodrigues
- BCAM-Basque Center for Applied Mathematics, 48009 Bilbao, Bizkaia, Spain
| |
Collapse
|
20
|
Singh V, Tchernookov M, Balasubramanian V. What the odor is not: Estimation by elimination. Phys Rev E 2021; 104:024415. [PMID: 34525542 PMCID: PMC8892575 DOI: 10.1103/physreve.104.024415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 11/07/2022]
Abstract
Olfactory systems use a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose a method of decoding such distributed representations by exploiting a statistical fact: Receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not rather than what the odor is. For realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. We construct a neural network realization of our algorithm based on the structure of the olfactory pathway.
Collapse
Affiliation(s)
- Vijay Singh
- Department of Physics, North Carolina A&T State University, Greensboro, NC, 27410, USA
- Department of Physics, & Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martin Tchernookov
- Department of Physics, University of Wisconsin, Whitewater, WI, 53190, USA
| | - Vijay Balasubramanian
- Department of Physics, & Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
21
|
Abrevaya G, Dumas G, Aravkin AY, Zheng P, Gagnon-Audet JC, Kozloski J, Polosecki P, Lajoie G, Cox D, Dawson SP, Cecchi G, Rish I. Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Comput 2021; 33:2087-2127. [PMID: 34310676 DOI: 10.1162/neco_a_01401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 02/19/2021] [Indexed: 01/16/2023]
Abstract
Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.
Collapse
Affiliation(s)
- Germán Abrevaya
- Departamento de Física, FCEyN, UBA and IFIBA, CONICET, 1428 Buenos Aires, Argentina
| | - Guillaume Dumas
- Mila-Quebec Artificial Intelligence Institute, and CHU Sainte-Justine Research Center, Department of Psychiatry, Universitéde Montréal, Montreal H3A OE8, Canada
| | | | - Peng Zheng
- University of Washington, Seattle, WA 98195, U.S.A.
| | | | - James Kozloski
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Pablo Polosecki
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Guillaume Lajoie
- Mila-Quebec Artificial Intelligence Institute, Universitéde Montréal, Montreal H3A OE8, Canada
| | - David Cox
- MIT-IBM Watson AI Lab, Cambridge, MA 02139, U.S.A.
| | - Silvina Ponce Dawson
- Departamento de Física, FCEyN, UBA and IFIBA, CONICET, 1428 Buenos Aires, Argentina
| | - Guillermo Cecchi
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Irina Rish
- Mila-Quebec Artificial Intelligence Institute, Université de Montréal, Montreal H3A OE8, Canada
| |
Collapse
|
22
|
Olfactory encoding within the insect antennal lobe: The emergence and role of higher order temporal correlations in the dynamics of antennal lobe spiking activity. J Theor Biol 2021; 522:110700. [PMID: 33819477 DOI: 10.1016/j.jtbi.2021.110700] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/20/2021] [Accepted: 03/23/2021] [Indexed: 11/22/2022]
Abstract
In this review, we focus on the antennal lobe (AL) of three insect species - the fruit fly, sphinx moth, and locust. We first review the experimentally elucidated anatomy and physiology of the early olfactory system of each species; empirical studies of AL activity, however, often focus on assessing firing rates (averaged over time scales of about 100 ms), and hence the AL odor code is often analyzed in terms of a temporally evolving vector of firing rates. However, such a perspective necessarily misses the possibility of higher order temporal correlations in spiking activity within a single cell and across multiple cells over shorter time scales (of about 10 ms). Hence, we then review our prior theoretical work, where we constructed biophysically detailed, species-specific AL models within the fly, moth, and locust, finding that in each case higher order temporal correlations in spiking naturally emerge from model dynamics (i.e., without a prioriincorporation of elements designed to produce correlated activity). We therefore use our theoretical work to argue the perspective that temporal correlations in spiking over short time scales, which have received little experimental attention to-date, may provide valuable coding dimensions (complementing the coding dimensions provided by the vector of firing rates) that nature has exploited in the encoding of odors within the AL. We further argue that, if the AL does indeed utilize temporally correlated activity to represent odor information, such an odor code could be naturally and easily deciphered within the Mushroom Body.
Collapse
|
23
|
Rouse NA, Daltorio KA. Visualization of Stable Heteroclinic Channel-Based Movement Primitives. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3061382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
24
|
Chockanathan U, Crosier EJW, Waddle S, Lyman E, Gerkin RC, Padmanabhan K. Changes in pairwise correlations during running reshape global network state in the main olfactory bulb. J Neurophysiol 2021; 125:1612-1623. [PMID: 33656931 DOI: 10.1152/jn.00464.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Neural codes for sensory inputs have been hypothesized to reside in a broader space defined by ongoing patterns of spontaneous activity. To understand the structure of this spontaneous activity in the olfactory system, we performed high-density recordings of neural populations in the main olfactory bulb of awake mice. We observed changes in pairwise correlations of spontaneous activity between mitral and tufted (M/T) cells when animals were running, which resulted in an increase in the entropy of the population. Surprisingly, pairwise maximum entropy models that described the population activity using only assumptions about the firing rates and correlations of neurons were better at predicting the global structure of activity when animals were stationary as compared to when they were running, implying that higher order (3rd, 4th order) interactions governed population activity during locomotion. Taken together, we found that locomotion alters the functional interactions that shape spontaneous population activity at the earliest stages of olfactory processing, one synapse away from the sensory receptors in the nasal epithelium. These data suggest that the coding space available for sensory representations responds adaptively to the animal's behavioral state.NEW & NOTEWORTHY The organization and structure of spontaneous population activity in the olfactory system places constraints of how odor information is represented. Using high-density electrophysiological recordings of mitral and tufted cells, we found that running increases the dimensionality of spontaneous activity, implicating higher order interactions among neurons during locomotion. Behavior, thus, flexibly alters neuronal activity at the earliest stages of sensory processing.
Collapse
Affiliation(s)
- Udaysankar Chockanathan
- Medical Scientist Training Program (MSTP), University of Rochester School of Medicine, Rochester, New York.,Department of Neuroscience and Neuroscience Graduate Program (NGP), University of Rochester School of Medicine, Rochester, New York
| | - Emily J W Crosier
- Department of Neuroscience and Neuroscience Graduate Program (NGP), University of Rochester School of Medicine, Rochester, New York
| | - Spencer Waddle
- Department of Physics, University of Delaware, Newark, Delaware
| | - Edward Lyman
- Department of Physics, University of Delaware, Newark, Delaware
| | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, Arizona
| | - Krishnan Padmanabhan
- Medical Scientist Training Program (MSTP), University of Rochester School of Medicine, Rochester, New York.,Department of Neuroscience and Neuroscience Graduate Program (NGP), University of Rochester School of Medicine, Rochester, New York.,Center for Visual Sciences, University of Rochester School of Medicine, Rochester, New York
| |
Collapse
|
25
|
Network mechanism for insect olfaction. Cogn Neurodyn 2021; 15:103-129. [PMID: 33786083 DOI: 10.1007/s11571-020-09640-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/25/2020] [Accepted: 09/30/2020] [Indexed: 10/22/2022] Open
Abstract
Early olfactory pathway responses to the presentation of an odor exhibit remarkably similar dynamical behavior across phyla from insects to mammals, and frequently involve transitions among quiescence, collective network oscillations, and asynchronous firing. We hypothesize that the time scales of fast excitation and fast and slow inhibition present in these networks may be the essential element underlying this similar behavior, and design an idealized, conductance-based integrate-and-fire model to verify this hypothesis via numerical simulations. To better understand the mathematical structure underlying the common dynamical behavior across species, we derive a firing-rate model and use it to extract a slow passage through a saddle-node-on-an-invariant-circle bifurcation structure. We expect this bifurcation structure to provide new insights into the understanding of the dynamical behavior of neuronal assemblies and that a similar structure can be found in other sensory systems.
Collapse
|
26
|
Morrison M, Kutz JN. Nonlinear control of networked dynamical systems. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021; 8:174-189. [PMID: 33997094 PMCID: PMC8117950 DOI: 10.1109/tnse.2020.3032117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The sparse identification of nonlinear dynamics (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on established bistable, low-dimensional biological systems, showing how control signals are found that generate switches between the fixed points. We then demonstrate our control procedure for high-dimensional systems on random high-dimensional networks and Hopfield memory networks.
Collapse
Affiliation(s)
- Megan Morrison
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195 USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195 USA
| |
Collapse
|
27
|
Fuscà D, Kloppenburg P. Odor processing in the cockroach antennal lobe-the network components. Cell Tissue Res 2021; 383:59-73. [PMID: 33486607 PMCID: PMC7872951 DOI: 10.1007/s00441-020-03387-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/07/2020] [Indexed: 02/06/2023]
Abstract
Highly interconnected neural networks perform olfactory signal processing in the central nervous system. In insects, the first synaptic processing of the olfactory input from the antennae occurs in the antennal lobe, the functional equivalent of the olfactory bulb in vertebrates. Key components of the olfactory network in the antennal lobe are two main types of neurons: the local interneurons and the projection (output) neurons. Both neuron types have different physiological tasks during olfactory processing, which accordingly require specialized functional phenotypes. This review gives an overview of important cell type-specific functional properties of the different types of projection neurons and local interneurons in the antennal lobe of the cockroach Periplaneta americana, which is an experimental system that has elucidated many important biophysical and cellular bases of intrinsic physiological properties of these neurons.
Collapse
Affiliation(s)
- Debora Fuscà
- Biocenter, Institute for Zoology, and Cologne Excellence Cluster On Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Zülpicher Str. 47b, 50674, Cologne, Germany
| | - Peter Kloppenburg
- Biocenter, Institute for Zoology, and Cologne Excellence Cluster On Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Zülpicher Str. 47b, 50674, Cologne, Germany.
| |
Collapse
|
28
|
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.
Collapse
|
29
|
Oettl LL, Scheller M, Filosa C, Wieland S, Haag F, Loeb C, Durstewitz D, Shusterman R, Russo E, Kelsch W. Phasic dopamine reinforces distinct striatal stimulus encoding in the olfactory tubercle driving dopaminergic reward prediction. Nat Commun 2020; 11:3460. [PMID: 32651365 PMCID: PMC7351739 DOI: 10.1038/s41467-020-17257-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 06/16/2020] [Indexed: 01/07/2023] Open
Abstract
The learning of stimulus-outcome associations allows for predictions about the environment. Ventral striatum and dopaminergic midbrain neurons form a larger network for generating reward prediction signals from sensory cues. Yet, the network plasticity mechanisms to generate predictive signals in these distributed circuits have not been entirely clarified. Also, direct evidence of the underlying interregional assembly formation and information transfer is still missing. Here we show that phasic dopamine is sufficient to reinforce the distinctness of stimulus representations in the ventral striatum even in the absence of reward. Upon such reinforcement, striatal stimulus encoding gives rise to interregional assemblies that drive dopaminergic neurons during stimulus-outcome learning. These assemblies dynamically encode the predicted reward value of conditioned stimuli. Together, our data reveal that ventral striatal and midbrain reward networks form a reinforcing loop to generate reward prediction coding. It is not entirely understood how network plasticity produces the coding of predicted value during stimulus-outcome learning. Here, the authors reveal a reinforcing loop in distributed limbic circuits, transforming sensory stimuli into reward prediction coding broadcasted by dopamine neurons to the brain.
Collapse
Affiliation(s)
- Lars-Lennart Oettl
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.,Sainsbury Wellcome Centre for Neural Circuits and Behaviour, London, W1T 4JG, UK
| | - Max Scheller
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Carla Filosa
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131, Mainz, Germany
| | - Sebastian Wieland
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Franziska Haag
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Cathrin Loeb
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Roman Shusterman
- Institute of Neuroscience, University of Oregon, Eugene, OR, 97403, USA
| | - Eleonora Russo
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.
| | - Wolfgang Kelsch
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany. .,Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131, Mainz, Germany.
| |
Collapse
|
30
|
Neural Circuit Dynamics for Sensory Detection. J Neurosci 2020; 40:3408-3423. [PMID: 32165416 DOI: 10.1523/jneurosci.2185-19.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/19/2020] [Accepted: 02/28/2020] [Indexed: 11/21/2022] Open
Abstract
We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns (i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment). We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low-dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high-fidelity tracking of a nominal representation in this latent space in an energy-efficient manner. It turns out that the optimal motifs emerging from this framework possess morphologic similarity with prototypical onset and offset responses observed in vivo in locusts (Schistocerca americana) of either sex. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory-inhibitory competitive dynamics, similar to interactions between projection neurons and local neurons in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and trade-offs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection.SIGNIFICANCE STATEMENT A key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we examine not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. Through optimization-based synthesis, we examine how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.
Collapse
|
31
|
Perl O, Nahum N, Belelovsky K, Haddad R. The contribution of temporal coding to odor coding and odor perception in humans. eLife 2020; 9:49734. [PMID: 32031520 PMCID: PMC7007219 DOI: 10.7554/elife.49734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 01/15/2020] [Indexed: 11/17/2022] Open
Abstract
Whether neurons encode information through their spike rates, their activity times or both is an ongoing debate in systems neuroscience. Here, we tested whether humans can discriminate between a pair of temporal odor mixtures (TOMs) composed of the same two components delivered in rapid succession in either one temporal order or its reverse. These TOMs presumably activate the same olfactory neurons but at different times and thus differ mainly in the time of neuron activation. We found that most participants could hardly discriminate between TOMs, although they easily discriminated between a TOM and one of its components. By contrast, participants succeeded in discriminating between the TOMs when they were notified of their successive nature in advance. We thus suggest that the time of glomerulus activation can be exploited to extract odor-related information, although it does not change the odor perception substantially, as should be expected from an odor code per se.
Collapse
Affiliation(s)
- Ofer Perl
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Nahum Nahum
- Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel
| | - Katya Belelovsky
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Rafi Haddad
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| |
Collapse
|
32
|
Odor-Induced Multi-Level Inhibitory Maps in Drosophila. eNeuro 2020; 7:ENEURO.0213-19.2019. [PMID: 31888962 PMCID: PMC6957311 DOI: 10.1523/eneuro.0213-19.2019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 12/06/2019] [Accepted: 12/16/2019] [Indexed: 12/04/2022] Open
Abstract
Optical imaging of intracellular Ca2+ influx as a correlate of neuronal excitation represents a standard technique for visualizing spatiotemporal activity of neuronal networks. However, the information-processing properties of single neurons and neuronal circuits likewise involve inhibition of neuronal membrane potential. Here, we report spatially resolved optical imaging of odor-evoked inhibitory patterns in the olfactory circuitry of Drosophila using a genetically encoded fluorescent Cl- sensor. In combination with the excitatory component reflected by intracellular Ca2+ dynamics, we present a comprehensive functional map of both odor-evoked neuronal activation and inhibition at different levels of olfactory processing. We demonstrate that odor-evoked inhibition carried by Cl- influx is present both in sensory neurons and second-order projection neurons (PNs), and is characterized by stereotypic, odor-specific patterns. Cl--mediated inhibition features distinct dynamics in different neuronal populations. Our data support a dual role of inhibitory neurons in the olfactory system: global gain control across the neuronal circuitry and glomerulus-specific inhibition to enhance neuronal information processing.
Collapse
|
33
|
Human Odour Coding in the Yellow Fever Mosquito, Aedes aegypti. Sci Rep 2019; 9:13336. [PMID: 31527631 PMCID: PMC6746732 DOI: 10.1038/s41598-019-49753-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 08/30/2019] [Indexed: 11/16/2022] Open
Abstract
Insects use their olfactory systems to obtain chemical information on mating partners, oviposition sites and food. The yellow fever mosquito Aedes aegypti, an important vector of human infectious diseases, shows strong preference for human blood meals. This study investigated the chemical basis of host detection by characterizing the neuronal responses of antennal olfactory sensilla of female Ae. aegypti to 103 compounds from human skin emanations. The effect of blood feeding on the responses of olfactory sensilla to these odorants was examined as well. Sensilla SBTII, GP, and three functional subtypes of SST (SST1, SST2, and SST3) responded to most of the compounds tested. Olfactory receptor neurons (ORNs) ‘A’ and ‘B’ in the trichoid sensilla, either activated or inhibited, were involved in the odour coding process. Compounds from different chemical classes elicited responses with different temporal structures and different response patterns across the olfactory sensilla. Except for their increased responses to several odorants, blood-fed mosquitoes generally evoked reduced responses to specific aldehydes, alcohols, aliphatics/aromatics, ketones, and amines through the SST1, SST2, SBTI, SBTII and GP sensilla. The odorants eliciting diminished responses in female mosquitoes after blood feeding may be important in Ae. aegypti host-seeking activity and thus can be candidates for mosquito attractants in the process of this disease vector management.
Collapse
|
34
|
Hemberger M, Shein-Idelson M, Pammer L, Laurent G. Reliable Sequential Activation of Neural Assemblies by Single Pyramidal Cells in a Three-Layered Cortex. Neuron 2019; 104:353-369.e5. [PMID: 31439429 DOI: 10.1016/j.neuron.2019.07.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 05/10/2019] [Accepted: 07/12/2019] [Indexed: 10/26/2022]
Abstract
Recent studies reveal the occasional impact of single neurons on surround firing statistics and even simple behaviors. Exploiting the advantages of a simple cortex, we examined the influence of single pyramidal neurons on surrounding cortical circuits. Brief activation of single neurons triggered reliable sequences of firing in tens of other excitatory and inhibitory cortical neurons, reflecting cascading activity through local networks, as indicated by delayed yet precisely timed polysynaptic subthreshold potentials. The evoked patterns were specific to the pyramidal cell of origin, extended over hundreds of micrometers from their source, and unfolded over up to 200 ms. Simultaneous activation of pyramidal cell pairs indicated balanced control of population activity, preventing paroxysmal amplification. Single cortical pyramidal neurons can thus trigger reliable postsynaptic activity that can propagate in a reliable fashion through cortex, generating rapidly evolving and non-random firing sequences reminiscent of those observed in mammalian hippocampus during "replay" and in avian song circuits.
Collapse
Affiliation(s)
- Mike Hemberger
- Max Planck Institute for Brain Research, Frankfurt am Main, 60438 Germany
| | - Mark Shein-Idelson
- Max Planck Institute for Brain Research, Frankfurt am Main, 60438 Germany; Department of Neurobiology, George S. Wise Faculty of Life Sciences, Sagol School for Neuroscience, Tel-Aviv University, Tel Aviv, Israel
| | - Lorenz Pammer
- Max Planck Institute for Brain Research, Frankfurt am Main, 60438 Germany
| | - Gilles Laurent
- Max Planck Institute for Brain Research, Frankfurt am Main, 60438 Germany.
| |
Collapse
|
35
|
Lagzi F, Atay FM, Rotter S. Bifurcation analysis of the dynamics of interacting subnetworks of a spiking network. Sci Rep 2019; 9:11397. [PMID: 31388027 PMCID: PMC6684592 DOI: 10.1038/s41598-019-47190-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 07/10/2019] [Indexed: 12/04/2022] Open
Abstract
We analyze the collective dynamics of hierarchically structured networks of densely connected spiking neurons. These networks of sub-networks may represent interactions between cell assemblies or different nuclei in the brain. The dynamical activity pattern that results from these interactions depends on the strength of synaptic coupling between them. Importantly, the overall dynamics of a brain region in the absence of external input, so called ongoing brain activity, has been attributed to the dynamics of such interactions. In our study, two different network scenarios are considered: a system with one inhibitory and two excitatory subnetworks, and a network representation with three inhibitory subnetworks. To study the effect of synaptic strength on the global dynamics of the network, two parameters for relative couplings between these subnetworks are considered. For each case, a bifurcation analysis is performed and the results have been compared to large-scale network simulations. Our analysis shows that Generalized Lotka-Volterra (GLV) equations, well-known in predator-prey studies, yield a meaningful population-level description for the collective behavior of spiking neuronal interaction, which have a hierarchical structure. In particular, we observed a striking equivalence between the bifurcation diagrams of spiking neuronal networks and their corresponding GLV equations. This study gives new insight on the behavior of neuronal assemblies, and can potentially suggest new mechanisms for altering the dynamical patterns of spiking networks based on changing the synaptic strength between some groups of neurons.
Collapse
Affiliation(s)
- Fereshteh Lagzi
- Bernstein Center Freiburg, Freiburg, Germany. .,Faculty of Biology, University of Freiburg, Freiburg, Germany.
| | - Fatihcan M Atay
- Department of Mathematics, Bilkent University, Ankara, Turkey
| | - Stefan Rotter
- Bernstein Center Freiburg, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| |
Collapse
|
36
|
Koppe G, Toutounji H, Kirsch P, Lis S, Durstewitz D. Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. PLoS Comput Biol 2019; 15:e1007263. [PMID: 31433810 PMCID: PMC6719895 DOI: 10.1371/journal.pcbi.1007263] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 09/03/2019] [Accepted: 07/11/2019] [Indexed: 12/31/2022] Open
Abstract
A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical systems as well as on experimental fMRI time series, and demonstrate that the learnt dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.
Collapse
Affiliation(s)
- Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Hazem Toutounji
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefanie Lis
- Institute for Psychiatric and Psychosomatic Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| |
Collapse
|
37
|
Burton SD, Wipfel M, Guo M, Eiting TP, Wachowiak M. A Novel Olfactometer for Efficient and Flexible Odorant Delivery. Chem Senses 2019; 44:173-188. [PMID: 30657873 PMCID: PMC6410398 DOI: 10.1093/chemse/bjz005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Understanding how sensory space maps to neural activity in the olfactory system requires efficiently and flexibly delivering numerous odorants within single experimental preparations. Such delivery is difficult with current olfactometer designs, which typically include limited numbers of stimulus channels and are subject to intertrial and interchannel contamination of odorants. Here, we present a novel olfactometer design that is easily constructed, modular, and capable of delivering an unlimited number of odorants in air with temporal precision and no detectable intertrial or interchannel contamination. The olfactometer further allows for the flexible generation of odorant mixtures and flexible timing of odorant sequences. Odorant delivery from the olfactometer is turbulent but reliable from trial to trial, supporting operant conditioning of mice in an odorant discrimination task and permitting odorants and concentrations to be mapped to neural activity with a level of precision equivalent to that obtained with a flow dilution olfactometer. This novel design thus provides several unique advantages for interrogating olfactory perception and for mapping sensory space to neural activity in the olfactory system.
Collapse
Affiliation(s)
- Shawn D Burton
- Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, UT, USA
| | - Mia Wipfel
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Michael Guo
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Thomas P Eiting
- Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, UT, USA
| | - Matt Wachowiak
- Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, UT, USA
| |
Collapse
|
38
|
Slankster E, Odell SR, Mathew D. Strength in diversity: functional diversity among olfactory neurons of the same type. J Bioenerg Biomembr 2019; 51:65-75. [PMID: 30604088 PMCID: PMC6382560 DOI: 10.1007/s10863-018-9779-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 11/13/2018] [Indexed: 01/01/2023]
Abstract
Most animals depend upon olfaction to find food, mates, and to avoid predators. An animal's olfactory circuit helps it sense its olfactory environment and generate critical behavioral responses. The general architecture of the olfactory circuit, which is conserved across species, is made up of a few different neuronal types including first-order receptor neurons, second- and third-order neurons, and local interneurons. Each neuronal type differs in their morphology, physiology, and neurochemistry. However, several recent studies have suggested that there is intrinsic diversity even among neurons of the same type and that this diversity is important for neural function. In this review, we first examine instances of intrinsic diversity observed among individual types of olfactory neurons. Next, we review potential genetic and experience-based plasticity mechanisms that underlie this diversity. Finally, we consider the implications of intrinsic neuronal diversity for circuit function. Overall, we hope to highlight the importance of intrinsic diversity as a previously underestimated property of circuit function.
Collapse
Affiliation(s)
- Eryn Slankster
- Department of Biology, University of Nevada, 1664 N. Virginia St., MS: 0314, Reno, NV, 89557, USA
| | - Seth R Odell
- Department of Biology, University of Nevada, 1664 N. Virginia St., MS: 0314, Reno, NV, 89557, USA
- Integrated Neuroscience Program, University of Nevada, Reno, NV, 89557, USA
| | - Dennis Mathew
- Department of Biology, University of Nevada, 1664 N. Virginia St., MS: 0314, Reno, NV, 89557, USA.
- Integrated Neuroscience Program, University of Nevada, Reno, NV, 89557, USA.
| |
Collapse
|
39
|
Elucidating the Neuronal Architecture of Olfactory Glomeruli in the Drosophila Antennal Lobe. Cell Rep 2018; 16:3401-3413. [PMID: 27653699 DOI: 10.1016/j.celrep.2016.08.063] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Revised: 07/15/2016] [Accepted: 08/18/2016] [Indexed: 11/21/2022] Open
Abstract
Olfactory glomeruli are morphologically conserved spherical compartments of the olfactory system, distinguishable solely by their chemosensory repertoire, anatomical position, and volume. Little is known, however, about their numerical neuronal composition. We therefore characterized their neuronal architecture and correlated these anatomical features with their functional properties in Drosophila melanogaster. We quantitatively mapped all olfactory sensory neurons (OSNs) innervating each glomerulus, including sexually dimorphic distributions. Our data reveal the impact of OSN number on glomerular dimensions and demonstrate yet unknown sex-specific differences in several glomeruli. Moreover, we quantified uniglomerular projection neurons for each glomerulus, which unraveled a glomerulus-specific numerical innervation. Correlation between morphological features and functional specificity showed that glomeruli innervated by narrowly tuned OSNs seem to possess a larger number of projection neurons and are involved in less lateral processing than glomeruli targeted by broadly tuned OSNs. Our study demonstrates that the neuronal architecture of each glomerulus encoding crucial odors is unique.
Collapse
|
40
|
Mastrodonato A, Barbati SA, Leone L, Colussi C, Gironi K, Rinaudo M, Piacentini R, Denny CA, Grassi C. Olfactory memory is enhanced in mice exposed to extremely low-frequency electromagnetic fields via Wnt/β-catenin dependent modulation of subventricular zone neurogenesis. Sci Rep 2018; 8:262. [PMID: 29321633 PMCID: PMC5762682 DOI: 10.1038/s41598-017-18676-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 12/15/2017] [Indexed: 12/03/2022] Open
Abstract
Exposure to extremely low-frequency electromagnetic fields (ELFEF) influences the expression of key target genes controlling adult neurogenesis and modulates hippocampus-dependent memory. Here, we assayed whether ELFEF stimulation affects olfactory memory by modulating neurogenesis in the subventricular zone (SVZ) of the lateral ventricle, and investigated the underlying molecular mechanisms. We found that 30 days after the completion of an ELFEF stimulation protocol (1 mT; 50 Hz; 3.5 h/day for 12 days), mice showed enhanced olfactory memory and increased SVZ neurogenesis. These effects were associated with upregulated expression of mRNAs encoding for key regulators of adult neurogenesis and were mainly dependent on the activation of the Wnt pathway. Indeed, ELFEF stimulation increased Wnt3 mRNA expression and nuclear localization of its downstream target β-catenin. Conversely, inhibition of Wnt3 by Dkk-1 prevented ELFEF-induced upregulation of neurogenic genes and abolished ELFEF’s effects on olfactory memory. Collectively, our findings suggest that ELFEF stimulation increases olfactory memory via enhanced Wnt/β-catenin signaling in the SVZ and point to ELFEF as a promising tool for enhancing SVZ neurogenesis and olfactory function.
Collapse
Affiliation(s)
- Alessia Mastrodonato
- Università Cattolica del Sacro Cuore, Institute of Human Physiology, Rome, 00168, Italy.,Columbia University, Department of Psychiatry, New York, NY, 10032, USA.,Research Foundation for Mental Hygiene Inc. (RFMH), Division of Integrative Neuroscience, New York State Psychiatric Institute (NYSPI), New York, NY, 10032, USA
| | | | - Lucia Leone
- Università Cattolica del Sacro Cuore, Institute of Human Physiology, Rome, 00168, Italy
| | - Claudia Colussi
- CNR, Institute of Cell Biology and Neurobiology, Monterotondo (RM), 00015, Italy
| | - Katia Gironi
- Università Cattolica del Sacro Cuore, Institute of Human Physiology, Rome, 00168, Italy
| | - Marco Rinaudo
- Università Cattolica del Sacro Cuore, Institute of Human Physiology, Rome, 00168, Italy
| | - Roberto Piacentini
- Università Cattolica del Sacro Cuore, Institute of Human Physiology, Rome, 00168, Italy
| | - Christine A Denny
- Columbia University, Department of Psychiatry, New York, NY, 10032, USA.,Research Foundation for Mental Hygiene Inc. (RFMH), Division of Integrative Neuroscience, New York State Psychiatric Institute (NYSPI), New York, NY, 10032, USA
| | - Claudio Grassi
- Università Cattolica del Sacro Cuore, Institute of Human Physiology, Rome, 00168, Italy. .,Fondazione Policlinico Universitario A. Gemelli, Rome, 00168, Italy.
| |
Collapse
|
41
|
Guo H, Smith DP. Odorant Receptor Desensitization in Insects. J Exp Neurosci 2017; 11:1179069517748600. [PMID: 29308015 PMCID: PMC5751911 DOI: 10.1177/1179069517748600] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 11/14/2017] [Indexed: 11/20/2022] Open
Abstract
Insects and other arthropods transmit devastating human diseases, and these vectors use chemical senses to target humans. Understanding how these animals detect, respond, and adapt to volatile odorants may lead to novel ways to disrupt host localization or mate recognition in these pests. The past decade has led to remarkable progress in understanding odorant detection in arthropods. Insects use odorant-gated ion channels, first discovered in Drosophila melanogaster, to detect volatile chemicals. In flies, 60 "tuning" receptor subunits combine with a common subunit, Orco (odorant receptor coreceptor) to form ligand-gated ion channels. The mechanisms underlying odorant receptor desensitization in insects are largely unknown. Recent work reveals that dephosphorylation of serine 289 on the shared Orco subunit is responsible for slow, odor-induced receptor desensitization. Dephosphorylation has no effect on the localization of the receptor protein, and activation of the olfactory neurons in the absence of odor is sufficient to induce dephosphorylation and desensitization. These findings reveal a major component of receptor modulation in this important group of disease vectors, and implicate a second messenger feedback mechanism in this process.
Collapse
Affiliation(s)
- Hao Guo
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Dean P Smith
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
42
|
Li G, Cleland TA. A coupled-oscillator model of olfactory bulb gamma oscillations. PLoS Comput Biol 2017; 13:e1005760. [PMID: 29140973 PMCID: PMC5706731 DOI: 10.1371/journal.pcbi.1005760] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/29/2017] [Accepted: 09/01/2017] [Indexed: 11/19/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Guoshi Li
- Dept. Psychology, Cornell University, Ithaca, NY United States of America
| | - Thomas A. Cleland
- Dept. Psychology, Cornell University, Ithaca, NY United States of America
| |
Collapse
|
43
|
Fore S, Palumbo F, Pelgrims R, Yaksi E. Information processing in the vertebrate habenula. Semin Cell Dev Biol 2017; 78:130-139. [PMID: 28797836 DOI: 10.1016/j.semcdb.2017.08.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 07/12/2017] [Accepted: 08/05/2017] [Indexed: 10/19/2022]
Abstract
The habenula is a brain region that has gained increasing popularity over the recent years due to its role in processing value-related and experience-dependent information with a strong link to depression, addiction, sleep and social interactions. This small diencephalic nucleus is proposed to act as a multimodal hub or a switchboard, where inputs from different brain regions converge. These diverse inputs to the habenula carry information about the sensory world and the animal's internal state, such as reward expectation or mood. However, it is not clear how these diverse habenular inputs interact with each other and how such interactions contribute to the function of habenular circuits in regulating behavioral responses in various tasks and contexts. In this review, we aim to discuss how information processing in habenular circuits, can contribute to specific behavioral programs that are attributed to the habenula.
Collapse
Affiliation(s)
- Stephanie Fore
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Olav Kyrres Gate 9, Norwegian Brain Centre, 7491 Trondheim, Norway
| | - Fabrizio Palumbo
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Olav Kyrres Gate 9, Norwegian Brain Centre, 7491 Trondheim, Norway
| | - Robbrecht Pelgrims
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Olav Kyrres Gate 9, Norwegian Brain Centre, 7491 Trondheim, Norway
| | - Emre Yaksi
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Olav Kyrres Gate 9, Norwegian Brain Centre, 7491 Trondheim, Norway.
| |
Collapse
|
44
|
Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function. J Neurosci 2017; 36:12448-12467. [PMID: 27927961 DOI: 10.1523/jneurosci.2586-16.2016] [Citation(s) in RCA: 271] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2016] [Revised: 09/24/2016] [Accepted: 10/12/2016] [Indexed: 12/22/2022] Open
Abstract
The need to breathe links the mammalian olfactory system inextricably to the respiratory rhythms that draw air through the nose. In rodents and other small animals, slow oscillations of local field potential activity are driven at the rate of breathing (∼2-12 Hz) in olfactory bulb and cortex, and faster oscillatory bursts are coupled to specific phases of the respiratory cycle. These dynamic rhythms are thought to regulate cortical excitability and coordinate network interactions, helping to shape olfactory coding, memory, and behavior. However, while respiratory oscillations are a ubiquitous hallmark of olfactory system function in animals, direct evidence for such patterns is lacking in humans. In this study, we acquired intracranial EEG data from rare patients (Ps) with medically refractory epilepsy, enabling us to test the hypothesis that cortical oscillatory activity would be entrained to the human respiratory cycle, albeit at the much slower rhythm of ∼0.16-0.33 Hz. Our results reveal that natural breathing synchronizes electrical activity in human piriform (olfactory) cortex, as well as in limbic-related brain areas, including amygdala and hippocampus. Notably, oscillatory power peaked during inspiration and dissipated when breathing was diverted from nose to mouth. Parallel behavioral experiments showed that breathing phase enhances fear discrimination and memory retrieval. Our findings provide a unique framework for understanding the pivotal role of nasal breathing in coordinating neuronal oscillations to support stimulus processing and behavior. SIGNIFICANCE STATEMENT Animal studies have long shown that olfactory oscillatory activity emerges in line with the natural rhythm of breathing, even in the absence of an odor stimulus. Whether the breathing cycle induces cortical oscillations in the human brain is poorly understood. In this study, we collected intracranial EEG data from rare patients with medically intractable epilepsy, and found evidence for respiratory entrainment of local field potential activity in human piriform cortex, amygdala, and hippocampus. These effects diminished when breathing was diverted to the mouth, highlighting the importance of nasal airflow for generating respiratory oscillations. Finally, behavioral data in healthy subjects suggest that breathing phase systematically influences cognitive tasks related to amygdala and hippocampal functions.
Collapse
|
45
|
Jiang H, Schuele S, Rosenow J, Zelano C, Parvizi J, Tao JX, Wu S, Gottfried JA. Theta Oscillations Rapidly Convey Odor-Specific Content in Human Piriform Cortex. Neuron 2017; 94:207-219.e4. [PMID: 28384472 DOI: 10.1016/j.neuron.2017.03.021] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 01/26/2017] [Accepted: 03/09/2017] [Indexed: 12/21/2022]
Abstract
Olfactory oscillations are pervasive throughout vertebrate and invertebrate nervous systems. Such observations have long implied that rhythmic activity patterns play a fundamental role in odor coding. Using intracranial EEG recordings from rare patients with medically resistant epilepsy, we find that theta oscillations are a distinct electrophysiological signature of olfactory processing in the human brain. Across seven patients, odor stimulation enhanced theta power in human piriform cortex, with robust effects at the level of single trials. Importantly, classification analysis revealed that piriform oscillatory activity conveys olfactory-specific information that can be decoded within 110-518 ms of a sniff, and maximally within the theta frequency band. This temporal window was also associated with increased theta-specific phase coupling between piriform cortex and hippocampus. Together these findings suggest that human piriform cortex has access to olfactory content in the time-frequency domain and can utilize these signals to rapidly differentiate odor stimuli.
Collapse
Affiliation(s)
- Heidi Jiang
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
| | - Stephan Schuele
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Joshua Rosenow
- Department of Neurosurgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Christina Zelano
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Josef Parvizi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - James X Tao
- Department of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Shasha Wu
- Department of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Jay A Gottfried
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Department of Psychology, Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL 60208, USA.
| |
Collapse
|
46
|
Olfactory bulb plasticity ensures proper olfaction after severe impairment in postnatal neurogenesis. Sci Rep 2017; 7:5654. [PMID: 28720887 PMCID: PMC5516035 DOI: 10.1038/s41598-017-05970-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 06/06/2017] [Indexed: 11/08/2022] Open
Abstract
The olfactory bulb (OB) neurons establish a complex network that ensures the correct processing of the olfactory inputs. Moreover, the OB presents a lifelong addition of new neurons into its existing circuitry. This neurogenesis is considered essential for the OB function. However, its functional impact on physiology and behavior is still unclear. Here, we investigate the mechanisms of OB plasticity that underlie bulbar physiology in relation to severe damage of neurogenesis. The neurogenesis of young mice was altered by ionizing radiation. Afterwards, both multi-channel olfactometry and electrophysiological studies were performed. Furthermore, neurogenesis and differentiation of the newly formed cells were assessed using bromodeoxyuridine labeling combined with a wide battery of neuronal markers. Our results demonstrate a reduction in both neurogenesis and volume of the OB in irradiated animals. The number of neuroblasts reaching the OB was reduced and their differentiation rate into interneurons selectively changed; some populations were noticeably affected whereas others remained preserved. Surprisingly, both olfactory detection and discrimination as well as electrophysiology presented almost no alterations in irradiated mice. Our findings suggest that after damaging postnatal neurogenesis, the neurochemical fate of some interneurons changes within a new biological scenario, while maintaining homeostasis and olfaction.
Collapse
|
47
|
Regeneration of synapses in the olfactory pathway of locusts after antennal deafferentation. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2017; 203:867-877. [PMID: 28685185 DOI: 10.1007/s00359-017-1199-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 06/29/2017] [Accepted: 06/30/2017] [Indexed: 12/31/2022]
Abstract
The olfactory pathway of the locust is capable of fast and precise regeneration on an anatomical level. Following deafferentation of the antenna either of young adult locusts, or of fifth instar nymphs, severed olfactory receptor neurons (ORNs) reinnervate the antennal lobe (AL) and arborize in AL microglomeruli. In the present study we tested whether these regenerated fibers establish functional synapses again. Intracellular recordings from AL projection neurons revealed that the first few odor stimulus evoked postsynaptic responses from regenerated ORNs from day 4-7 post crush on. On average, synaptic connections of regenerated afferents appeared faster in younger locusts operated as fifth instar nymphs than in adults. The proportions of response categories (excitatory vs. inhibitory) changed during regeneration, but were back to normal within 21 days. Odor-evoked oscillating extracellular local field potentials (LFP) were recorded in the mushroom body. These responses, absent after antennal nerve crush, reappeared, in a few animals as soon as 4 days post crush. Odor-induced oscillation patterns were restored within 7 days post crush. Both intra- and extracellular recordings indicate the capability of the locust olfactory system to re-establish synaptic contacts in the antennal lobe after antennal nerve lesion.
Collapse
|
48
|
Kunert-Graf JM, Shlizerman E, Walker A, Kutz JN. Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics. Front Comput Neurosci 2017; 11:53. [PMID: 28659783 PMCID: PMC5468412 DOI: 10.3389/fncom.2017.00053] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 05/31/2017] [Indexed: 11/13/2022] Open
Abstract
The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous modeling work has found that dynamic models of the worm's full neuronal network are capable of generating reasonable dynamic responses to certain inputs, even when all neurons are treated as identical save for their connectivity. This study investigates such a model of C. elegans neuronal dynamics, finding that a wide variety of multistable responses are generated in response to varied inputs. Specifically, we generate bifurcation diagrams for all possible single-neuron inputs, showing the existence of fixed points and limit cycles for different input regimes. The nature of the dynamical response is seen to vary according to the type of neuron receiving input; for example, input into sensory neurons is more likely to drive a bifurcation in the system than input into motor neurons. As a specific example we consider compound input into the neuron pairs PLM and ASK, discovering bistability of a limit cycle and a fixed point. The transient timescales in approaching each of these states are much longer than any intrinsic timescales of the system. This suggests consistency of our model with the characterization of dynamics in neural systems as long-timescale transitions between discrete, low-dimensional attractors corresponding to behavioral states.
Collapse
Affiliation(s)
| | - Eli Shlizerman
- Department of Applied Mathematics, University of WashingtonSeattle, WA, United States.,Department of Electrical Engineering, University of WashingtonSeattle, WA, United States
| | - Andrew Walker
- Department of Applied Mathematics, University of WashingtonSeattle, WA, United States
| | - J Nathan Kutz
- Department of Physics, University of WashingtonSeattle, WA, United States.,Department of Applied Mathematics, University of WashingtonSeattle, WA, United States
| |
Collapse
|
49
|
Chalk M, Masset P, Deneve S, Gutkin B. Sensory noise predicts divisive reshaping of receptive fields. PLoS Comput Biol 2017; 13:e1005582. [PMID: 28622330 PMCID: PMC5509365 DOI: 10.1371/journal.pcbi.1005582] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 07/13/2017] [Accepted: 05/10/2017] [Indexed: 11/18/2022] Open
Abstract
In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.
Collapse
Affiliation(s)
- Matthew Chalk
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Paul Masset
- Department of Neuroscience, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
- Watson School of Biological Sciences, Cold Spring Harbor, New York, United States of America
| | - Sophie Deneve
- National Research University Higher School of Economics, Center for Cognition and Decision Making, Moscow, Russia
| | - Boris Gutkin
- National Research University Higher School of Economics, Center for Cognition and Decision Making, Moscow, Russia
- Group for Neural Theory, LNC INSERM U960, Departement d’Etudes Cognitive, Ecole Normale Superieure PSL* University, Paris, France
| |
Collapse
|
50
|
Heald SLM, Van Hedger SC, Nusbaum HC. Perceptual Plasticity for Auditory Object Recognition. Front Psychol 2017; 8:781. [PMID: 28588524 PMCID: PMC5440584 DOI: 10.3389/fpsyg.2017.00781] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 04/26/2017] [Indexed: 01/25/2023] Open
Abstract
In our auditory environment, we rarely experience the exact acoustic waveform twice. This is especially true for communicative signals that have meaning for listeners. In speech and music, the acoustic signal changes as a function of the talker (or instrument), speaking (or playing) rate, and room acoustics, to name a few factors. Yet, despite this acoustic variability, we are able to recognize a sentence or melody as the same across various kinds of acoustic inputs and determine meaning based on listening goals, expectations, context, and experience. The recognition process relates acoustic signals to prior experience despite variability in signal-relevant and signal-irrelevant acoustic properties, some of which could be considered as "noise" in service of a recognition goal. However, some acoustic variability, if systematic, is lawful and can be exploited by listeners to aid in recognition. Perceivable changes in systematic variability can herald a need for listeners to reorganize perception and reorient their attention to more immediately signal-relevant cues. This view is not incorporated currently in many extant theories of auditory perception, which traditionally reduce psychological or neural representations of perceptual objects and the processes that act on them to static entities. While this reduction is likely done for the sake of empirical tractability, such a reduction may seriously distort the perceptual process to be modeled. We argue that perceptual representations, as well as the processes underlying perception, are dynamically determined by an interaction between the uncertainty of the auditory signal and constraints of context. This suggests that the process of auditory recognition is highly context-dependent in that the identity of a given auditory object may be intrinsically tied to its preceding context. To argue for the flexible neural and psychological updating of sound-to-meaning mappings across speech and music, we draw upon examples of perceptual categories that are thought to be highly stable. This framework suggests that the process of auditory recognition cannot be divorced from the short-term context in which an auditory object is presented. Implications for auditory category acquisition and extant models of auditory perception, both cognitive and neural, are discussed.
Collapse
|