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Webb B. Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body. Learn Mem 2024; 31:a053824. [PMID: 38862164 PMCID: PMC11199945 DOI: 10.1101/lm.053824.123] [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: 11/03/2023] [Accepted: 03/01/2024] [Indexed: 06/13/2024]
Abstract
The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.
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Affiliation(s)
- Barbara Webb
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
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2
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Jürgensen AM, Sakagiannis P, Schleyer M, Gerber B, Nawrot MP. Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience 2024; 27:108640. [PMID: 38292165 PMCID: PMC10824792 DOI: 10.1016/j.isci.2023.108640] [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] [Received: 03/01/2023] [Revised: 11/10/2023] [Accepted: 12/01/2023] [Indexed: 02/01/2024] Open
Abstract
Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Panagiotis Sakagiannis
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for the Advancement of Higher Education, Faculty of Science, Hokkaido University, Sapporo 060-08080, Japan
| | - Bertram Gerber
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Brain and Behavioral Sciences (CBBS), Otto-von-Guericke University, 39118 Magdeburg, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
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3
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Gkanias E, McCurdy LY, Nitabach MN, Webb B. An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster. eLife 2022; 11:e75611. [PMID: 35363138 PMCID: PMC8975552 DOI: 10.7554/elife.75611] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/07/2022] [Indexed: 11/30/2022] Open
Abstract
Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.
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Affiliation(s)
- Evripidis Gkanias
- Institute of Perception Action and Behaviour, School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | - Li Yan McCurdy
- Department of Cellular and Molecular Physiology, Yale UniversityNew HavenUnited States
| | - Michael N Nitabach
- Department of Cellular and Molecular Physiology, Yale UniversityNew HavenUnited States
- Department of Genetics, Yale UniversityNew HavenUnited States
- Department of Neuroscience, Yale UniversityNew HavenUnited States
| | - Barbara Webb
- Institute of Perception Action and Behaviour, School of Informatics, University of EdinburghEdinburghUnited Kingdom
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4
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Manoonpong P, Patanè L, Xiong X, Brodoline I, Dupeyroux J, Viollet S, Arena P, Serres JR. Insect-Inspired Robots: Bridging Biological and Artificial Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:7609. [PMID: 34833685 PMCID: PMC8623770 DOI: 10.3390/s21227609] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 12/18/2022]
Abstract
This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.
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Affiliation(s)
- Poramate Manoonpong
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark;
- Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong 21210, Thailand
| | - Luca Patanè
- Department of Engineering, University of Messina, 98100 Messina, Italy
| | - Xiaofeng Xiong
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark;
| | - Ilya Brodoline
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
| | - Julien Dupeyroux
- Faculty of Aerospace Engineering, Delft University of Technology, 52600 Delft, The Netherlands;
| | - Stéphane Viollet
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
| | - Paolo Arena
- Department of Electrical, Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy
| | - Julien R. Serres
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
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5
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Hulse BK, Haberkern H, Franconville R, Turner-Evans D, Takemura SY, Wolff T, Noorman M, Dreher M, Dan C, Parekh R, Hermundstad AM, Rubin GM, Jayaraman V. A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. eLife 2021; 10:e66039. [PMID: 34696823 PMCID: PMC9477501 DOI: 10.7554/elife.66039] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.
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Affiliation(s)
- Brad K Hulse
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hannah Haberkern
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Romain Franconville
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daniel Turner-Evans
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Shin-ya Takemura
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tanya Wolff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marcella Noorman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Chuntao Dan
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ann M Hermundstad
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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6
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Schwarz S, Mangan M, Webb B, Wystrach A. Route-following ants respond to alterations of the view sequence. J Exp Biol 2020; 223:jeb218701. [PMID: 32487668 DOI: 10.1242/jeb.218701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/21/2020] [Indexed: 08/26/2023]
Abstract
Ants can navigate by comparing the currently perceived view with memorised views along a familiar foraging route. Models regarding route-following suggest that the views are stored and recalled independently of the sequence in which they occur. Hence, the ant only needs to evaluate the instantaneous familiarity of the current view to obtain a heading direction. This study investigates whether ant homing behaviour is influenced by alterations in the sequence of views experienced along a familiar route, using the frequency of stop-and-scan behaviour as an indicator of the ant's navigational uncertainty. Ants were trained to forage between their nest and a feeder which they exited through a short channel before proceeding along the homeward route. In tests, ants were collected before entering the nest and released again in the channel, which was placed either in its original location or halfway along the route. Ants exiting the familiar channel in the middle of the route would thus experience familiar views in a novel sequence. Results show that ants exiting the channel scan significantly more when they find themselves in the middle of the route, compared with when emerging at the expected location near the feeder. This behaviour suggests that previously encountered views influence the recognition of current views, even when these views are highly familiar, revealing a sequence component to route memory. How information about view sequences could be implemented in the insect brain, as well as potential alternative explanations to our results, are discussed.
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Affiliation(s)
- Sebastian Schwarz
- Centre de Recherches sur la Cognition Animale, CNRS, Université Paul Sabatier, Toulouse, 31062 Cedex 09, France
| | - Michael Mangan
- Sheffield Robotics, Department of Computer Science, University of Sheffield, Western Bank, Sheffield S10 2TN, UK
| | - Barbara Webb
- School of Informatics, University of Edinburgh, Crichton Street, Edinburgh EH8 9AB, UK
| | - Antoine Wystrach
- Centre de Recherches sur la Cognition Animale, CNRS, Université Paul Sabatier, Toulouse, 31062 Cedex 09, France
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7
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Endress AD. A Simple, Biologically Plausible Feature Detector for Language Acquisition. J Cogn Neurosci 2020; 32:435-445. [DOI: 10.1162/jocn_a_01494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Abstract
Language has a complex grammatical system we still have to understand computationally and biologically. However, some evolutionarily ancient mechanisms have been repurposed for grammar so that we can use insight from other taxa into possible circuit-level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions, I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world's languages: grammatical rules based on sameness/difference relations. This circuit acts as a sameness detector. “Different” items are suppressed through inhibition, but presenting two “identical” items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms.
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8
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Delahunt CB, Riffell JA, Kutz JN. Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets. Front Comput Neurosci 2018; 12:102. [PMID: 30618694 PMCID: PMC6306094 DOI: 10.3389/fncom.2018.00102] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 12/03/2018] [Indexed: 11/23/2022] Open
Abstract
The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its structural features, which are widespread in biological neural systems, process olfactory stimuli through a cascade of networks where large dimension shifts occur from stage to stage and where sparsity and randomness play a critical role in coding. Learning is partly enabled by a neuromodulatory reward mechanism of octopamine stimulation of the AL, whose increased activity induces synaptic weight updates in the MB through Hebbian plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that are the most important for the representation of the learned odor. Based upon current biophysical knowledge, we have constructed an end-to-end computational firing-rate model of the Manduca sexta moth olfactory system which includes the interaction of the AL and MB under octopamine stimulation. Our model is able to robustly learn new odors, and neural firing rates in our simulations match the statistical features of in vivo firing rate data. From a biological perspective, the model provides a valuable tool for examining the role of neuromodulators, like octopamine, in learning, and gives insight into critical interactions between sparsity, Hebbian growth, and stimulation during learning. Our simulations also inform predictions about structural details of the olfactory system that are not currently well-characterized. From a machine learning perspective, the model yields bio-inspired mechanisms that are potentially useful in constructing neural nets for rapid learning from very few samples. These mechanisms include high-noise layers, sparse layers as noise filters, and a biologically-plausible optimization method to train the network based on octopamine stimulation, sparse layers, and Hebbian growth.
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Affiliation(s)
- Charles B. Delahunt
- Department of Electrical Engineering, University of Washington, Seattle, WA, United States
- Computational Neuroscience Center, University of Washington, Seattle, WA, United States
| | - Jeffrey A. Riffell
- Department of Biology, University of Washington, Seattle, WA, United States
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States
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9
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Sanguanini M, Cattaneo A. A continuous model of physiological prion aggregation suggests a role for Orb2 in gating long-term synaptic information. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180336. [PMID: 30662713 PMCID: PMC6304141 DOI: 10.1098/rsos.180336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 11/02/2018] [Indexed: 06/09/2023]
Abstract
The regulation of mRNA translation at the level of the synapse is believed to be fundamental in memory and learning at the cellular level. The family of cytoplasmic polyadenylation element binding (CPEB) proteins emerged as an important RNA-binding protein family during development and in adult neurons. Drosophila Orb2 (homologue of mouse CPEB3 protein and of the neural isoform of Aplysia CPEB) has been found to be involved in the translation of plasticity-dependent mRNAs and has been associated with long-term memory. Orb2 protein presents two main isoforms, Orb2A and Orb2B, which form an activity-induced amyloid-like functional aggregate, thought to be the translation-inducing state of the RNA-binding protein. Here we present a first two-states continuous differential model for Orb2A-Orb2B aggregation. This model provides new working hypotheses for studying the role of prion-like CPEB proteins in long-term synaptic plasticity. Moreover, this model can be used as a first step to integrate translation- and protein aggregation-dependent phenomena in synaptic facilitation rules.
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Affiliation(s)
| | - Antonino Cattaneo
- Scuola Normale Superiore, Piazza dei Cavalieri, 7, 56126 Pisa, Italy
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10
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Cope AJ, Vasilaki E, Minors D, Sabo C, Marshall JAR, Barron AB. Abstract concept learning in a simple neural network inspired by the insect brain. PLoS Comput Biol 2018; 14:e1006435. [PMID: 30222735 PMCID: PMC6160224 DOI: 10.1371/journal.pcbi.1006435] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 09/27/2018] [Accepted: 08/15/2018] [Indexed: 12/24/2022] Open
Abstract
The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of 'sameness' and 'difference' that is compatible with the insect brain, and is not dependent on top-down or executive control processing.
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Affiliation(s)
- Alex J. Cope
- Department of Computer Science, University of Sheffield, Sheffield, UK
- Sheffield Robotics, University of Sheffield, Sheffield, UK
| | - Eleni Vasilaki
- Department of Computer Science, University of Sheffield, Sheffield, UK
- Sheffield Robotics, University of Sheffield, Sheffield, UK
| | - Dorian Minors
- Department of Biological Sciences, Macquarie University, Sydney, Australia
| | - Chelsea Sabo
- Department of Computer Science, University of Sheffield, Sheffield, UK
- Sheffield Robotics, University of Sheffield, Sheffield, UK
| | - James A. R. Marshall
- Department of Computer Science, University of Sheffield, Sheffield, UK
- Sheffield Robotics, University of Sheffield, Sheffield, UK
| | - Andrew B. Barron
- Department of Biological Sciences, Macquarie University, Sydney, Australia
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11
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Arena E, Arena P, Strauss R, Patané L. Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System. Front Neurorobot 2017; 11:12. [PMID: 28337138 PMCID: PMC5340754 DOI: 10.3389/fnbot.2017.00012] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 02/20/2017] [Indexed: 11/13/2022] Open
Abstract
In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster.
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Affiliation(s)
- Eleonora Arena
- Dipartimento di Ingegneria Elettrica, Elettronica, e Informatica, University of Catania Catania, Italy
| | - Paolo Arena
- Dipartimento di Ingegneria Elettrica, Elettronica, e Informatica, University of CataniaCatania, Italy; National Institute of Biostructures and BiosystemsRome, Italy
| | - Roland Strauss
- Institut für Zoologie III (Neurobiologie), University of Mainz Mainz, Germany
| | - Luca Patané
- Dipartimento di Ingegneria Elettrica, Elettronica, e Informatica, University of Catania Catania, Italy
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12
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Faghihi F, Moustafa AA, Heinrich R, Wörgötter F. A computational model of conditioning inspired by Drosophila olfactory system. Neural Netw 2017; 87:96-108. [DOI: 10.1016/j.neunet.2016.11.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Revised: 11/07/2016] [Accepted: 11/11/2016] [Indexed: 11/15/2022]
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13
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Arena P, Calí M, Patané L, Portera A, Strauss R. A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning. Int J Neural Syst 2016; 26:1650035. [DOI: 10.1142/s0129065716500350] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.
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Affiliation(s)
- Paolo Arena
- Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy
- National Institute of Biostructures and Biosystems (INBB), Viale delle Medaglie d’Oro 305, 00136 Rome, Italy
| | - Marco Calí
- Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy
| | - Luca Patané
- Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy
| | - Agnese Portera
- Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy
| | - Roland Strauss
- Institut für Zoologie III (Neurobiologie), University of Mainz, Mainz, Germany
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14
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Modelling the insect Mushroom Bodies: Application to sequence learning. Neural Netw 2015; 67:37-53. [DOI: 10.1016/j.neunet.2015.03.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Revised: 03/11/2015] [Accepted: 03/12/2015] [Indexed: 11/18/2022]
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