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Granato G, Baldassarre G. Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory. Neural Netw 2024; 176:106292. [PMID: 38657422 DOI: 10.1016/j.neunet.2024.106292] [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: 10/27/2023] [Revised: 03/27/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Goal-directed manipulation of internal representations is a key element of human flexible behaviour, while consciousness is commonly associated with higher-order cognition and human flexibility. Current perspectives have only partially linked these processes, thus preventing a clear understanding of how they jointly generate flexible cognition and behaviour. Moreover, these limitations prevent an effective exploitation of this knowledge for technological scopes. We propose a new theoretical perspective that extends our 'three-component theory of flexible cognition' toward higher-order cognition and consciousness, based on the systematic integration of key concepts from Cognitive Neuroscience and AI/Robotics. The theory proposes that the function of conscious processes is to support the alignment of representations with multi-level goals. This higher alignment leads to more flexible and effective behaviours. We analyse here our previous model of goal-directed flexible cognition (validated with more than 20 human populations) as a starting GARIM-inspired model. By bridging the main theories of consciousness and goal-directed behaviour, the theory has relevant implications for scientific and technological fields. In particular, it contributes to developing new experimental tasks and interpreting clinical evidence. Finally, it indicates directions for improving machine learning and robotics systems and for informing real-world applications (e.g., in digital-twin healthcare and roboethics).
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Affiliation(s)
- Giovanni Granato
- Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.
| | - Gianluca Baldassarre
- Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.
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2
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Liu W, Liu X. Pre-stimulus network responses affect information coding in neural variability quenching. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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3
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The effects of distractors on brightness perception based on a spiking network. Sci Rep 2023; 13:1517. [PMID: 36707550 PMCID: PMC9883501 DOI: 10.1038/s41598-023-28326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 01/17/2023] [Indexed: 01/28/2023] Open
Abstract
Visual perception can be modified by the surrounding context. Particularly, experimental observations have demonstrated that visual perception and primary visual cortical responses could be modified by properties of surrounding distractors. However, the underlying mechanism remains unclear. To simulate primary visual cortical activities in this paper, we design a k-winner-take-all (k-WTA) spiking network whose responses are generated through probabilistic inference. In simulations, images with the same target and various surrounding distractors perform as stimuli. Distractors are designed with multiple varying properties, including the luminance, the sizes and the distances to the target. Simulations for each varying property are performed with other properties fixed. Each property could modify second-layer neural responses and interactions in the network. To the same target in the designed images, the modified network responses could simulate distinguishing brightness perception consistent with experimental observations. Our model provides a possible explanation of how the surrounding distractors modify primary visual cortical responses to induce various brightness perception of the given target.
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4
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Jia S, Zhang T, Zuo R, Xu B. Explaining cocktail party effect and McGurk effect with a spiking neural network improved by Motif-topology. Front Neurosci 2023; 17:1132269. [PMID: 37021133 PMCID: PMC10067589 DOI: 10.3389/fnins.2023.1132269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/03/2023] [Indexed: 04/07/2023] Open
Abstract
Network architectures and learning principles have been critical in developing complex cognitive capabilities in artificial neural networks (ANNs). Spiking neural networks (SNNs) are a subset of ANNs that incorporate additional biological features such as dynamic spiking neurons, biologically specified architectures, and efficient and useful paradigms. Here we focus more on network architectures in SNNs, such as the meta operator called 3-node network motifs, which is borrowed from the biological network. We proposed a Motif-topology improved SNN (M-SNN), which is further verified efficient in explaining key cognitive phenomenon such as the cocktail party effect (a typical noise-robust speech-recognition task) and McGurk effect (a typical multi-sensory integration task). For M-SNN, the Motif topology is obtained by integrating the spatial and temporal motifs. These spatial and temporal motifs are first generated from the pre-training of spatial (e.g., MNIST) and temporal (e.g., TIDigits) datasets, respectively, and then applied to the previously introduced two cognitive effect tasks. The experimental results showed a lower computational cost and higher accuracy and a better explanation of some key phenomena of these two effects, such as new concept generation and anti-background noise. This mesoscale network motifs topology has much room for the future.
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Affiliation(s)
- Shuncheng Jia
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tielin Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Tielin Zhang
| | - Ruichen Zuo
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Bo Xu
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5
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Kulvicius T, Herzog S, Tamosiunaite M, Worgotter F. Finding Optimal Paths Using Networks Without Learning-Unifying Classical Approaches. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:7877-7887. [PMID: 34170833 DOI: 10.1109/tnnls.2021.3089023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Trajectory or path planning is a fundamental issue in a wide variety of applications. In this article, we show that it is possible to solve path planning on a maze for multiple start point and endpoint highly efficiently with a novel configuration of multilayer networks that use only weighted pooling operations, for which no network training is needed. These networks create solutions, which are identical to those from classical algorithms such as breadth-first search (BFS), Dijkstra's algorithm, or TD(0). Different from competing approaches, very large mazes containing almost one billion nodes with dense obstacle configuration and several thousand importance-weighted path endpoints can this way be solved quickly in a single pass on parallel hardware.
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6
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7
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Pezzulo G, LaPalme J, Durant F, Levin M. Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190765. [PMID: 33550952 PMCID: PMC7935058 DOI: 10.1098/rstb.2019.0765] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2020] [Indexed: 02/06/2023] Open
Abstract
Nervous systems' computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states-in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Joshua LaPalme
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Fallon Durant
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA
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8
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Kulvicius T, Herzog S, Lüddecke T, Tamosiunaite M, Wörgötter F. One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms. Front Neurorobot 2021; 14:600984. [PMID: 33584239 PMCID: PMC7874085 DOI: 10.3389/fnbot.2020.600984] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 12/10/2020] [Indexed: 11/30/2022] Open
Abstract
Path planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases <10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (<22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms.
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Affiliation(s)
- Tomas Kulvicius
- Third Institute of Physics - Biophysics, Department for Computational Neuroscience, University of Göttingen, Göttingen, Germany
| | - Sebastian Herzog
- Third Institute of Physics - Biophysics, Department for Computational Neuroscience, University of Göttingen, Göttingen, Germany
| | - Timo Lüddecke
- Third Institute of Physics - Biophysics, Department for Computational Neuroscience, University of Göttingen, Göttingen, Germany
| | - Minija Tamosiunaite
- Third Institute of Physics - Biophysics, Department for Computational Neuroscience, University of Göttingen, Göttingen, Germany.,Faculty of Computer Science, Vytautas Mangnus University, Kaunas, Lithuania
| | - Florentin Wörgötter
- Third Institute of Physics - Biophysics, Department for Computational Neuroscience, University of Göttingen, Göttingen, Germany
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9
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A generative spiking neural-network model of goal-directed behaviour and one-step planning. PLoS Comput Biol 2020; 16:e1007579. [PMID: 33290414 PMCID: PMC7748287 DOI: 10.1371/journal.pcbi.1007579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 12/18/2020] [Accepted: 10/01/2020] [Indexed: 11/21/2022] Open
Abstract
In mammals, goal-directed and planning processes support flexible behaviour used to face new situations that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, models have been proposed to perform planning as probabilistic inference but this approach encounters a crucial problem: explaining how such inference might be implemented in brain spiking networks. Recently, the literature has proposed some models that face this problem through recurrent spiking neural networks able to internally simulate state trajectories, the core function at the basis of planning. However, the proposed models have relevant limitations that make them biologically implausible, namely their world model is trained ‘off-line’ before solving the target tasks, and they are trained with supervised learning procedures that are biologically and ecologically not plausible. Here we propose two novel hypotheses on how brain might overcome these problems, and operationalise them in a novel architecture pivoting on a spiking recurrent neural network. The first hypothesis allows the architecture to learn the world model in parallel with its use for planning: to this purpose, a new arbitration mechanism decides when to explore, for learning the world model, or when to exploit it, for planning, based on the entropy of the world model itself. The second hypothesis allows the architecture to use an unsupervised learning process to learn the world model by observing the effects of actions. The architecture is validated by reproducing and accounting for the learning profiles and reaction times of human participants learning to solve a visuomotor learning task that is new for them. Overall, the architecture represents the first instance of a model bridging probabilistic planning and spiking-processes that has a degree of autonomy analogous to the one of real organisms. Goal-directed behaviour relies on brain processes supporting planning of actions based on their expected consequences before performing them in the environment. An important computational modelling approach proposes that the brain performs goal-directed processes on the basis of probability distributions and computations on them. A key challenge of this approach is to explain how these probabilistic processes can rely on the spiking processes of the brain. The literature has recently proposed some models that do so by ‘thinking ahead’ alternative possible action-outcomes based on low-level neuronal stochastic events. However, these models have a limited autonomy as they require to learn how the environment works (‘world model’) before solving the tasks, and use a biologically implausible learning process requiring an ‘external teacher’ to tell how their internal units should respond. Here we present a novel architecture proposing how organisms might overcome these challenging problems. First, the architecture can decide if exploring, to learn the world model, or planning, using such model, by evaluating how confident it is on the model knowledge. Second, the architecture can autonomously learn the world model based on experience. The architecture represents a first fully autonomous planning model relying on a spiking neural network.
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10
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Seoane LF. Fate of Duplicated Neural Structures. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E928. [PMID: 33286697 PMCID: PMC7597184 DOI: 10.3390/e22090928] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/18/2020] [Accepted: 08/20/2020] [Indexed: 01/25/2023]
Abstract
Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.
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Affiliation(s)
- Luís F. Seoane
- Departamento de Biología de Sistemas, Centro Nacional de Biotecnología (CNB), CSIC, C/Darwin 3, 28049 Madrid, Spain;
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), CSIC-UIB, 07122 Palma de Mallorca, Spain
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11
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Pezzulo G, Donnarumma F, Maisto D, Stoianov I. Planning at decision time and in the background during spatial navigation. Curr Opin Behav Sci 2019. [DOI: 10.1016/j.cobeha.2019.04.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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12
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Jordan J, Weidel P, Morrison A. A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents. Front Comput Neurosci 2019; 13:46. [PMID: 31427939 PMCID: PMC6687756 DOI: 10.3389/fncom.2019.00046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 06/25/2019] [Indexed: 11/17/2022] Open
Abstract
Neural network simulation is an important tool for generating and evaluating hypotheses on the structure, dynamics, and function of neural circuits. For scientific questions addressing organisms operating autonomously in their environments, in particular where learning is involved, it is crucial to be able to operate such simulations in a closed-loop fashion. In such a set-up, the neural agent continuously receives sensory stimuli from the environment and provides motor signals that manipulate the environment or move the agent within it. So far, most studies requiring such functionality have been conducted with custom simulation scripts and manually implemented tasks. This makes it difficult for other researchers to reproduce and build upon previous work and nearly impossible to compare the performance of different learning architectures. In this work, we present a novel approach to solve this problem, connecting benchmark tools from the field of machine learning and state-of-the-art neural network simulators from computational neuroscience. The resulting toolchain enables researchers in both fields to make use of well-tested high-performance simulation software supporting biologically plausible neuron, synapse and network models and allows them to evaluate and compare their approach on the basis of standardized environments with various levels of complexity. We demonstrate the functionality of the toolchain by implementing a neuronal actor-critic architecture for reinforcement learning in the NEST simulator and successfully training it on two different environments from the OpenAI Gym. We compare its performance to a previously suggested neural network model of reinforcement learning in the basal ganglia and a generic Q-learning algorithm.
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Affiliation(s)
- Jakob Jordan
- Department of Physiology, University of Bern, Bern, Switzerland
- Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA-Institute Brain Structure Function Relationship (JBI 1/INM-10), Research Centre Jülich, Jülich, Germany
| | - Philipp Weidel
- Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA-Institute Brain Structure Function Relationship (JBI 1/INM-10), Research Centre Jülich, Jülich, Germany
- aiCTX, Zurich, Switzerland
- Department of Computer Science, RWTH Aachen University, Aachen, Germany
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA-Institute Brain Structure Function Relationship (JBI 1/INM-10), Research Centre Jülich, Jülich, Germany
- Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
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13
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Tanneberg D, Peters J, Rueckert E. Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks. Neural Netw 2018; 109:67-80. [PMID: 30408695 DOI: 10.1016/j.neunet.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 08/22/2018] [Accepted: 10/09/2018] [Indexed: 11/29/2022]
Abstract
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signal cognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.
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Affiliation(s)
- Daniel Tanneberg
- Intelligent Autonomous Systems, Technische Universität Darmstadt, Hochschulstr. 10, 64289 Darmstadt, Germany.
| | - Jan Peters
- Intelligent Autonomous Systems, Technische Universität Darmstadt, Hochschulstr. 10, 64289 Darmstadt, Germany; Robot Learning Group, Max-Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany.
| | - Elmar Rueckert
- Institute for Robotics and Cognitive Systems, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Intelligent Autonomous Systems, Technische Universität Darmstadt, Hochschulstr. 10, 64289 Darmstadt, Germany.
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14
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Bing Z, Meschede C, Röhrbein F, Huang K, Knoll AC. A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks. Front Neurorobot 2018; 12:35. [PMID: 30034334 PMCID: PMC6043678 DOI: 10.3389/fnbot.2018.00035] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 06/14/2018] [Indexed: 11/30/2022] Open
Abstract
Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.
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Affiliation(s)
- Zhenshan Bing
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Claus Meschede
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Florian Röhrbein
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Kai Huang
- Department of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Alois C. Knoll
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
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15
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Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task. PLoS One 2017; 12:e0186959. [PMID: 29077735 PMCID: PMC5659652 DOI: 10.1371/journal.pone.0186959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 10/10/2017] [Indexed: 11/19/2022] Open
Abstract
Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.
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16
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Pezzulo G, Kemere C, van der Meer MAA. Internally generated hippocampal sequences as a vantage point to probe future-oriented cognition. Ann N Y Acad Sci 2017; 1396:144-165. [PMID: 28548460 DOI: 10.1111/nyas.13329] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/31/2017] [Accepted: 02/07/2017] [Indexed: 12/22/2022]
Abstract
Information processing in the rodent hippocampus is fundamentally shaped by internally generated sequences (IGSs), expressed during two different network states: theta sequences, which repeat and reset at the ∼8 Hz theta rhythm associated with active behavior, and punctate sharp wave-ripple (SWR) sequences associated with wakeful rest or slow-wave sleep. A potpourri of diverse functional roles has been proposed for these IGSs, resulting in a fragmented conceptual landscape. Here, we advance a unitary view of IGSs, proposing that they reflect an inferential process that samples a policy from the animal's generative model, supported by hippocampus-specific priors. The same inference affords different cognitive functions when the animal is in distinct dynamical modes, associated with specific functional networks. Theta sequences arise when inference is coupled to the animal's action-perception cycle, supporting online spatial decisions, predictive processing, and episode encoding. SWR sequences arise when the animal is decoupled from the action-perception cycle and may support offline cognitive processing, such as memory consolidation, the prospective simulation of spatial trajectories, and imagination. We discuss the empirical bases of this proposal in relation to rodent studies and highlight how the proposed computational principles can shed light on the mechanisms of future-oriented cognition in humans.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Caleb Kemere
- Electrical and Computer Engineering, Rice University, Houston, Texas
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17
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Palmer J, Keane A, Gong P. Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. PLoS Comput Biol 2017; 13:e1005669. [PMID: 28759562 PMCID: PMC5552356 DOI: 10.1371/journal.pcbi.1005669] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 08/10/2017] [Accepted: 07/06/2017] [Indexed: 02/03/2023] Open
Abstract
Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes, we develop and investigate a spiking neural circuit model endowed with a combination of two synaptic plasticity mechanisms including spike-timing dependent plasticity (STDP) and synaptic scaling. In this model, the interplay of the combined synaptic plasticity mechanisms and network dynamics gives rise to neural sequences which propagate ahead of the animals’ decision point to reach goal locations. The dynamical properties of these forward-sweeping sequences and the rates of correct binary choices executed by these sequences are quantitatively consistent with experimental observations; this consistency, however, is lost in our model when only one of STDP or synaptic scaling is included. We further demonstrate that such sequence-based decision making in our network model can adaptively respond to time-varying and probabilistic associations of cues and goal locations, and that our model performs as well as an optimal Kalman filter model. Our results thus suggest that the combination of plasticity phenomena on different timescales provides a candidate mechanism for forming internally generated neural sequences and for implementing adaptive spatial decision making. Adaptive goal-directed decision making is critical for animals, robots and humans to navigate through space. In this study, we propose a novel neural mechanism for implementing spatial decision making in cued-choice tasks. We show that in a spiking neural circuit model, the interplay of network dynamics and a combination of two synaptic plasticity rules, STDP and synaptic scaling, gives rise to neural sequences. When a model rat pauses around a decision point, these sequences propagate ahead of the animal’s current location and travel towards a goal location. The dynamical properties of these forward-sweeping sequences and the rate of correct responses made by them are consistent with experimental data. In addition, we demonstrate that STDP when complemented by slower synaptic scaling enables neural sequences to make adaptive choices under probabilistic and time-varying cue-goal associations. The adaptive performance of our sequence-based network is comparable to a mathematical model, namely the Kalman filter, which is optimal for this adaptive task. Our results thus shed new light on our understanding of neural mechanisms underlying goal-directed decision making.
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Affiliation(s)
- John Palmer
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Adam Keane
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
- * E-mail:
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Pitti A, Gaussier P, Quoy M. Iterative free-energy optimization for recurrent neural networks (INFERNO). PLoS One 2017; 12:e0173684. [PMID: 28282439 PMCID: PMC5345841 DOI: 10.1371/journal.pone.0173684] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2016] [Accepted: 02/24/2017] [Indexed: 11/19/2022] Open
Abstract
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.
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Affiliation(s)
- Alexandre Pitti
- ETIS Laboratory, CNRS UMR 8051, University of Cergy-Pontoise, ENSEA, Paris-Seine, Cergy-Pontoise, France
| | - Philippe Gaussier
- ETIS Laboratory, CNRS UMR 8051, University of Cergy-Pontoise, ENSEA, Paris-Seine, Cergy-Pontoise, France
| | - Mathias Quoy
- ETIS Laboratory, CNRS UMR 8051, University of Cergy-Pontoise, ENSEA, Paris-Seine, Cergy-Pontoise, France
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