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Dwijesha AS, Eswaran A, Berry JA, Phan A. Diverse memory paradigms in Drosophila reveal diverse neural mechanisms. Learn Mem 2024; 31:a053810. [PMID: 38862165 DOI: 10.1101/lm.053810.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/12/2024] [Indexed: 06/13/2024]
Abstract
In this review, we aggregated the different types of learning and memory paradigms developed in adult Drosophila and attempted to assess the similarities and differences in the neural mechanisms supporting diverse types of memory. The simplest association memory assays are conditioning paradigms (olfactory, visual, and gustatory). A great deal of work has been done on these memories, revealing hundreds of genes and neural circuits supporting this memory. Variations of conditioning assays (reversal learning, trace conditioning, latent inhibition, and extinction) also reveal interesting memory mechanisms, whereas mechanisms supporting spatial memory (thermal maze, orientation memory, and heat box) and the conditioned suppression of innate behaviors (phototaxis, negative geotaxis, anemotaxis, and locomotion) remain largely unexplored. In recent years, there has been an increased interest in multisensory and multicomponent memories (context-dependent and cross-modal memory) and higher-order memory (sensory preconditioning and second-order conditioning). Some of this work has revealed how the intricate mushroom body (MB) neural circuitry can support more complex memories. Finally, the most complex memories are arguably those involving social memory: courtship conditioning and social learning (mate-copying and egg-laying behaviors). Currently, very little is known about the mechanisms supporting social memories. Overall, the MBs are important for association memories of multiple sensory modalities and multisensory integration, whereas the central complex is important for place, orientation, and navigation memories. Interestingly, several different types of memory appear to use similar or variants of the olfactory conditioning neural circuitry, which are repurposed in different ways.
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Affiliation(s)
- Amoolya Sai Dwijesha
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Akhila Eswaran
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Jacob A Berry
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Anna Phan
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Women and Children's Health Research Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
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2
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Fesce R. The emergence of identity, agency and consciousness from the temporal dynamics of neural elaboration. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1292388. [PMID: 38628469 PMCID: PMC11018992 DOI: 10.3389/fnetp.2024.1292388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/13/2024] [Indexed: 04/19/2024]
Abstract
Identity-differentiating self from external reality-and agency-being the author of one's acts-are generally considered intrinsic properties of awareness and looked at as mental constructs generated by consciousness. Here a different view is proposed. All physiological systems display complex time-dependent regulations to adapt or anticipate external changes. To interact with rapid changes, an animal needs a nervous system capable of modelling and predicting (not simply representing) it. Different algorithms must be employed to predict the momentary location of an object based on sensory information (received with a delay), or to design in advance and direct the trajectory of movement. Thus, the temporal dynamics of external events and action must be handled in differential ways, thereby generating the distinction between self and non-self ("identity") as an intrinsic computational construct in neuronal elaboration. Handling time is not what neurons are designed for. Neuronal circuits are based on parallel processing: each bit of information diverges on many neurons, each of which combines it with many other data. Spike firing reports the likelihood that the specific pattern the neuron is designed to respond to is present in the incoming data. This organization seems designed to process synchronous datasets. However, since neural networks can introduce delays in processing, time sequences can be transformed into simultaneous patterns and analysed as such. This way predictive algorithms can be implemented, and continually improved through neuronal plasticity. To successfully interact with the external reality, the nervous system must model and predict, but also differentially handle perceptual functions or motor activity, by putting in register information that becomes available at different time moments. Also, to learn through positive/negative reinforcement, modelling must establish a causal relation between motor control and its consequences: the contrast between phase lag in perception and phase lead (and control) in motor programming produces the emergence of identity (discerning self from surrounding) and agency (control on actions) as necessary computational constructs to model reality. This does not require any form of awareness. In a brain, capable of producing awareness, these constructs may evolve from mere computational requirements into mental (conscious) constructs.
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Affiliation(s)
- Riccardo Fesce
- Department of Biomedical Sciences, Humanitas University, Medical School, Pieve Emanuele, Italy
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3
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Baetu TM. Extrapolating animal consciousness. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2024; 104:150-159. [PMID: 38520882 DOI: 10.1016/j.shpsa.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 03/25/2024]
Abstract
I argue that the question of animal consciousness is an extrapolation problem and, as such, is best tackled by deploying currently accepted methodology for validating experimental models of a phenomenon of interest. This methodology relies on an assessment of similarities and dissimilarities between experimental models, the partial replication of findings across complementary models, and evidence from the successes and failures of explanations, technologies and medical applications developed by extrapolating and aggregating findings from multiple models. Crucially important, this methodology does not require a commitment to any particular theory or construct of consciousness, thus avoiding theory-biased reinterpretations of empirical findings rampant in the literature.
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Affiliation(s)
- Tudor M Baetu
- Université du Québec à Trois-Rivières, Département de philosophie et des arts, 3351, boul. des Forges, Trois-Rivières, Québec, G8Z 4M3, Canada.
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4
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Stupski SD, van Breugel F. Wind Gates Search States in Free Flight. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.30.569086. [PMID: 38076971 PMCID: PMC10705368 DOI: 10.1101/2023.11.30.569086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
For any organism tracking a chemical cue to its source, the motion of its surrounding fluid provides crucial information for success. For both swimming and flying animals engaged in olfaction driven search, turning into the direction of oncoming wind or water current is often a critical first step 1, 2 . However, in nature, wind and water currents may not always provide a reliable directional cue 3, 4, 5 . It is unclear how organisms adjust their search strategies accordingly due to the challenges of separately controlling flow and chemical encounters. Here, we use the genetic toolkit of Drosophila melanogaster , a model organism for olfaction 6 , to develop an optogenetic paradigm to deliver temporally precise "virtual" olfactory experiences in free-flying animals while independently manipulating the wind conditions. We show that in free flight, Drosophila melanogaster adopt distinct search routines that are gated by whether they are flying in laminar wind or in still air. We first confirm that in laminar wind flies turn upwind, and further, we show that they achieve this using a rapid turn. In still air, flies adopt remarkably stereotyped "sink and circle" search state characterized by ∼60°turns at 3-4 Hz, biased in a consistent direction. In both laminar wind and still air, immediately after odor onset, flies decelerate and often perform a rapid turn. Both maneuvers are consistent with predictions from recent control theoretic analyses for how insects may estimate properties of wind while in flight 7, 8 . We suggest that flies may use their deceleration and "anemometric" turn as active sensing maneuvers to rapidly gauge properties of their wind environment before initiating a proximal or upwind search routine.
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5
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Arizanovska D, Emodogo JA, Lally AP, Palavicino-Maggio CB, Liebl DJ, Folorunso OO. Cross species review of the physiological role of D-serine in translationally relevant behaviors. Amino Acids 2023; 55:1501-1517. [PMID: 37833512 PMCID: PMC10689556 DOI: 10.1007/s00726-023-03338-6] [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: 06/01/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023]
Abstract
Bridging the gap between preclinical models of neurological and psychiatric disorders with their human manifestations is necessary to understand their underlying mechanisms, identify biomarkers, and develop novel therapeutics. Cognitive and social impairments underlie multiple neuropsychiatric and neurological disorders and are often comorbid with sleep disturbances, which can exacerbate poor outcomes. Importantly, many symptoms are conserved between vertebrates and invertebrates, although they may have subtle differences. Therefore, it is essential to determine the molecular mechanisms underlying these behaviors across different species and their translatability to humans. Genome-wide association studies have indicated an association between glutamatergic gene variants and both the risk and frequency of psychiatric disorders such as schizophrenia, bipolar disorder, and autism spectrum disorder. For example, changes in glutamatergic neurotransmission, such as glutamate receptor subtype N-methyl-D-aspartate receptor (NMDAR) hypofunction, have been shown to contribute to the pathophysiology of schizophrenia. Furthermore, in neurological disorders, such as traumatic brain injury and Alzheimer's disease, hyperactivation of NMDARs leads to synaptic damage. In addition to glutamate binding, NMDARs require the binding of a co-agonist D-serine or glycine to the GluN1 subunit to open. D-serine, which is racemized from L-serine by the neuronal enzyme serine racemase (SRR), and both SRR and D-serine are enriched in cortico-limbic brain regions. D-serine is critical for complex behaviors, such as cognition and social behavior, where dysregulation of its synthesis and release has been implicated in many pathological conditions. In this review, we explore the role of D-serine in behaviors that are translationally relevant to multiple psychiatric and neurological disorders in different models across species.
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Affiliation(s)
- Dena Arizanovska
- The Miami Project to Cure Paralysis, Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jada A Emodogo
- Translational Psychiatry Laboratory, McLean Hospital, Belmont, MA, USA
| | - Anna P Lally
- Translational Neuroscience Laboratory, McLean Hospital, Belmont, MA, USA
| | - Caroline B Palavicino-Maggio
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Neurobiological Mechanisms of Aggression Laboratory, McLean Hospital, Belmont, MA, USA
| | - Daniel J Liebl
- The Miami Project to Cure Paralysis, Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Oluwarotimi O Folorunso
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Translational Psychiatry Laboratory, McLean Hospital, Belmont, MA, USA.
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6
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Zacks O, Jablonka E. The evolutionary origins of the Global Neuronal Workspace in vertebrates. Neurosci Conscious 2023; 2023:niad020. [PMID: 37711313 PMCID: PMC10499063 DOI: 10.1093/nc/niad020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/01/2023] [Accepted: 08/24/2023] [Indexed: 09/16/2023] Open
Abstract
The Global Neuronal Workspace theory of consciousness offers an explicit functional architecture that relates consciousness to cognitive abilities such as perception, attention, memory, and evaluation. We show that the functional architecture of the Global Neuronal Workspace, which is based mainly on human studies, corresponds to the cognitive-affective architecture proposed by the Unlimited Associative Learning theory that describes minimal consciousness. However, we suggest that when applied to basal vertebrates, both models require important modifications to accommodate what has been learned about the evolution of the vertebrate brain. Most importantly, comparative studies suggest that in basal vertebrates, the Global Neuronal Workspace is instantiated by the event memory system found in the hippocampal homolog. This proposal has testable predictions and implications for understanding hippocampal and cortical functions, the evolutionary relations between memory and consciousness, and the evolution of unified perception.
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Affiliation(s)
- Oryan Zacks
- The Cohn Institute for the History and Philosophy of Science and Ideas, Tel Aviv University, Ramat Aviv 6934525, Israel
| | - Eva Jablonka
- The Cohn Institute for the History and Philosophy of Science and Ideas, Tel Aviv University, Ramat Aviv 6934525, Israel
- CPNSS, London School of Economics, Houghton St., London WC2A 2AE, United Kingdom
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7
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Wilson RI. Neural Networks for Navigation: From Connections to Computations. Annu Rev Neurosci 2023; 46:403-423. [PMID: 37428603 DOI: 10.1146/annurev-neuro-110920-032645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Cambridge, Massachusetts, USA;
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8
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Paoli M, Macri C, Giurfa M. A cognitive account of trace conditioning in insects. CURRENT OPINION IN INSECT SCIENCE 2023; 57:101034. [PMID: 37044245 DOI: 10.1016/j.cois.2023.101034] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Abstract
Trace conditioning is a form of Pavlovian learning in which the conditioned stimulus (CS) and the unconditioned stimulus (US) are separated by a temporal gap. Insects learn trace associations of variable nature (appetitive, aversive) and involving CSs of different sensory modalities (olfactory, visual). The accessibility of the insect neural system in behaving animals allowed identifying neural processes driving trace conditioning: the existence of prolonged neural responses to the CS after stimulus offset and the anticipation of US responses during the free-stimulus gap. Specific brain structures, such as the mushroom bodies seem to be allocated to this learning form. Here, we posit that a further component facilitating trace conditioning in insects relates to neuromodulatory mechanisms underlying enhanced attention. We thus propose a model based on different types of mushroom-body neurons, which provides a cognitive account of trace conditioning in insects.
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Affiliation(s)
- Marco Paoli
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), University of Toulouse, CNRS, UPS, 31062 Toulouse cedex 9, France
| | - Catherine Macri
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), University of Toulouse, CNRS, UPS, 31062 Toulouse cedex 9, France
| | - Martin Giurfa
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), University of Toulouse, CNRS, UPS, 31062 Toulouse cedex 9, France; Institut Universitaire de France (IUF), Paris, France.
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9
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Zeng J, Li X, Zhang R, Lv M, Wang Y, Tan K, Xia X, Wan J, Jing M, Zhang X, Li Y, Yang Y, Wang L, Chu J, Li Y, Li Y. Local 5-HT signaling bi-directionally regulates the coincidence time window for associative learning. Neuron 2023; 111:1118-1135.e5. [PMID: 36706757 PMCID: PMC11152601 DOI: 10.1016/j.neuron.2022.12.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/03/2022] [Accepted: 12/30/2022] [Indexed: 01/27/2023]
Abstract
The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration of this temporal window remains unclear. Here, we found that serotonin (5-HT) bi-directionally regulates the coincidence time window of olfactory learning in Drosophila and affects synaptic plasticity of Kenyon cells (KCs) in the mushroom body (MB). Utilizing GPCR-activation-based (GRAB) neurotransmitter sensors, we found that KC-released acetylcholine (ACh) activates a serotonergic dorsal paired medial (DPM) neuron, which in turn provides inhibitory feedback to KCs. Physiological stimuli induce spatially heterogeneous 5-HT signals, which proportionally gate the intrinsic coincidence time windows of different MB compartments. Artificially reducing or increasing the DPM neuron-released 5-HT shortens or prolongs the coincidence window, respectively. In a sequential trace conditioning paradigm, this serotonergic neuromodulation helps to bridge the CS-US temporal gap. Altogether, we report a model circuitry for perceiving the temporal coincidence and determining the causal relationship between environmental events.
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Affiliation(s)
- Jianzhi Zeng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Hefei National Laboratory for Physical Sciences at the Microscale, CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, Anhui, China.
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Renzimo Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China
| | - Mingyue Lv
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Ke Tan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China
| | - Jinxia Wan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Miao Jing
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiuning Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yu Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yang Yang
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Wang
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jun Chu
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yan Li
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China; Chinese Institute for Brain Research, Beijing 102206, China.
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10
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Kandimalla P, Omoto JJ, Hong EJ, Hartenstein V. Lineages to circuits: the developmental and evolutionary architecture of information channels into the central complex. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2023:10.1007/s00359-023-01616-y. [PMID: 36932234 DOI: 10.1007/s00359-023-01616-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/27/2023] [Accepted: 01/28/2023] [Indexed: 03/19/2023]
Abstract
The representation and integration of internal and external cues is crucial for any organism to execute appropriate behaviors. In insects, a highly conserved region of the brain, the central complex (CX), functions in the representation of spatial information and behavioral states, as well as the transformation of this information into desired navigational commands. How does this relatively invariant structure enable the incorporation of information from the diversity of anatomical, behavioral, and ecological niches occupied by insects? Here, we examine the input channels to the CX in the context of their development and evolution. Insect brains develop from ~ 100 neuroblasts per hemisphere that divide systematically to form "lineages" of sister neurons, that project to their target neuropils along anatomically characteristic tracts. Overlaying this developmental tract information onto the recently generated Drosophila "hemibrain" connectome and integrating this information with the anatomical and physiological recording of neurons in other species, we observe neuropil and lineage-specific innervation, connectivity, and activity profiles in CX input channels. We posit that the proliferative potential of neuroblasts and the lineage-based architecture of information channels enable the modification of neural networks across existing, novel, and deprecated modalities in a species-specific manner, thus forming the substrate for the evolution and diversification of insect navigational circuits.
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Affiliation(s)
- Pratyush Kandimalla
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. .,Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA.
| | - Jaison Jiro Omoto
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Elizabeth J Hong
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Volker Hartenstein
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
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11
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Pfeiffer K. The neuronal building blocks of the navigational toolkit in the central complex of insects. CURRENT OPINION IN INSECT SCIENCE 2023; 55:100972. [PMID: 36126877 DOI: 10.1016/j.cois.2022.100972] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/03/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
The central complex in the brain of insects is a group of midline-spanning neuropils at the interface between sensory and premotor tasks of the brain. It is involved in sleep control, decision-making and most prominently in goal-directed locomotion behaviors. The recently published connectome of the central complex of Drosophila melanogaster is a milestone in understanding the intricacies of the central-complex circuits and will provide inspiration for testable hypotheses for the coming years. Here, I provide a basic neuroanatomical description of the central complex of Drosophila and other species and discuss some recent advancements, some of which, such as the discovery of coordinate transformation through vector math, have been predicted from connectomics data.
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Affiliation(s)
- Keram Pfeiffer
- Behavioural Physiology and Sociobiology (Zoology II), Biocenter, University of Würzburg, 97074 Würzburg, Germany.
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12
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Multilevel development of cognitive abilities in an artificial neural network. Proc Natl Acad Sci U S A 2022; 119:e2201304119. [PMID: 36122214 PMCID: PMC9522351 DOI: 10.1073/pnas.2201304119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels, and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious, manner. The third and cognitively highest level handles the information globally and consciously. It is based on the global neuronal workspace (GNW) theory and is referred to as the conscious level. We use the trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through the selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory/inhibitory ratio increases performance. We discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
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13
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Giurfa M, Macri C. Neuroscience: Mechanisms for bridging stimuli in Pavlovian trace conditioning in flies. Curr Biol 2022; 32:R532-R535. [PMID: 35671730 DOI: 10.1016/j.cub.2022.04.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A recent study revealed neural mechanisms underlying visual trace conditioning in flies. To associate visual stimuli with heat punishment, the activity of visual- and heat-processing circuits was extended into the gap between them. Distractors delivered during the gap disrupted learning, raising the question of the cognitive processes at play.
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Affiliation(s)
- Martin Giurfa
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), University of Toulouse, CNRS, UPS, 31062 Toulouse cedex 9, France; Institut Universitaire de France (IUF), Paris, France.
| | - Catherine Macri
- Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), University of Toulouse, CNRS, UPS, 31062 Toulouse cedex 9, France
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14
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Fisher YE, Marquis M, D’Alessandro I, Wilson RI. Dopamine promotes head direction plasticity during orienting movements. Nature 2022; 612:316-322. [PMID: 36450986 PMCID: PMC9729112 DOI: 10.1038/s41586-022-05485-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 10/25/2022] [Indexed: 12/05/2022]
Abstract
In neural networks that store information in their connection weights, there is a tradeoff between sensitivity and stability1,2. Connections must be plastic to incorporate new information, but if they are too plastic, stored information can be corrupted. A potential solution is to allow plasticity only during epochs when task-specific information is rich, on the basis of a 'when-to-learn' signal3. We reasoned that dopamine provides a when-to-learn signal that allows the brain's spatial maps to update when new spatial information is available-that is, when an animal is moving. Here we show that the dopamine neurons innervating the Drosophila head direction network are specifically active when the fly turns to change its head direction. Moreover, their activity scales with moment-to-moment fluctuations in rotational speed. Pairing dopamine release with a visual cue persistently strengthens the cue's influence on head direction cells. Conversely, inhibiting these dopamine neurons decreases the influence of the cue. This mechanism should accelerate learning during moments when orienting movements are providing a rich stream of head direction information, allowing learning rates to be low at other times to protect stored information. Our results show how spatial learning in the brain can be compressed into discrete epochs in which high learning rates are matched to high rates of information intake.
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Affiliation(s)
- Yvette E. Fisher
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA ,grid.47840.3f0000 0001 2181 7878Present Address: Department of Molecular and Cellular Biology, University of California Berkeley, Berkeley, CA USA ,grid.499295.a0000 0004 9234 0175Present Address: Chan Zuckerberg Biohub, San Francisco, CA USA
| | - Michael Marquis
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | - Isabel D’Alessandro
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | - Rachel I. Wilson
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
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