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Masoli S, Rizza MF, Tognolina M, Prestori F, D’Angelo E. Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation. Front Comput Neurosci 2022; 16:1006989. [PMID: 36387305 PMCID: PMC9649760 DOI: 10.3389/fncom.2022.1006989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | | | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- *Correspondence: Francesca Prestori,
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
- Egidio D’Angelo,
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2
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Cruz NC, González-Redondo Á, Redondo JL, Garrido JA, Ortigosa EM, Ortigosa PM. Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity. Front Neuroinform 2022; 16:1017222. [PMID: 36338942 PMCID: PMC9630480 DOI: 10.3389/fninf.2022.1017222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/28/2022] [Indexed: 11/25/2022] Open
Abstract
The basal ganglia (BG) is a brain structure that has long been proposed to play an essential role in action selection, and theoretical models of spiking neurons have tried to explain how the BG solves this problem. A recently proposed functional and biologically inspired network model of the striatum (an important nucleus of the BG) is based on spike-timing-dependent eligibility (STDE) and captured important experimental features of this nucleus. The model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensory inputs. However, model tuning is challenging due to two main reasons. The first is the expert knowledge required, resulting in tedious and potentially biased trial-and-error procedures. The second is the computational cost of assessing model configurations (approximately 1.78 h per evaluation). This study addresses the model tuning problem through numerical optimization. Considering the cost of assessing solutions, the selected methods stand out due to their low requirements for solution evaluations and compatibility with high-performance computing. They are the SurrogateOpt solver of Matlab and the RBFOpt library, both based on radial basis function approximations, and DIRECT-GL, an enhanced version of the widespread black-box optimizer DIRECT. Besides, a parallel random search serves as a baseline reference of the outcome of opting for sophisticated methods. SurrogateOpt turns out to be the best option for tuning this kind of model. It outperforms, on average, the quality of the configuration found by an expert and works significantly faster and autonomously. RBFOpt and the random search share the second position, but their average results are below the option found by hand. Finally, DIRECT-GL follows this line becoming the worst-performing method.
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Affiliation(s)
- Nicolás C. Cruz
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | - Álvaro González-Redondo
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
- *Correspondence: Álvaro González-Redondo
| | - Juana L. Redondo
- Department of Informatics, University of Almería, ceiA3 Excellence Agri-food Campus, Almeria, Spain
| | - Jesús A. Garrido
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | - Eva M. Ortigosa
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | - Pilar M. Ortigosa
- Department of Informatics, University of Almería, ceiA3 Excellence Agri-food Campus, Almeria, Spain
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3
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Computational epidemiology study of homeostatic compensation during sensorimotor aging. Neural Netw 2021; 146:316-333. [PMID: 34923219 DOI: 10.1016/j.neunet.2021.11.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/26/2021] [Accepted: 11/24/2021] [Indexed: 11/20/2022]
Abstract
The vestibulo-ocular reflex (VOR) stabilizes vision during head motion. Age-related changes of vestibular neuroanatomical properties predict a linear decay of VOR function. Nonetheless, human epidemiological data show a stable VOR function across the life span. In this study, we model cerebellum-dependent VOR adaptation to relate structural and functional changes throughout aging. We consider three neurosynaptic factors that may codetermine VOR adaptation during aging: the electrical coupling of inferior olive neurons, the long-term spike timing-dependent plasticity at parallel fiber - Purkinje cell synapses and mossy fiber - medial vestibular nuclei synapses, and the intrinsic plasticity of Purkinje cell synapses Our cross-sectional aging analyses suggest that long-term plasticity acts as a global homeostatic mechanism that underpins the stable temporal profile of VOR function. The results also suggest that the intrinsic plasticity of Purkinje cell synapses operates as a local homeostatic mechanism that further sustains the VOR at older ages. Importantly, the computational epidemiology approach presented in this study allows discrepancies among human cross-sectional studies to be understood in terms of interindividual variability in older individuals. Finally, our longitudinal aging simulations show that the amount of residual fibers coding for the peak and trough of the VOR cycle constitutes a predictive hallmark of VOR trajectories over a lifetime.
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4
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Martinez-Murcia FJ, Ortiz A, Ramírez J, Górriz JM, Cruz R. Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.04.148] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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5
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Abadia I, Naveros F, Garrido JA, Ros E, Luque NR. On Robot Compliance: A Cerebellar Control Approach. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2476-2489. [PMID: 31647453 DOI: 10.1109/tcyb.2019.2945498] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.
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6
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Calcium Channel-Dependent Induction of Long-Term Synaptic Plasticity at Excitatory Golgi Cell Synapses of Cerebellum. J Neurosci 2021; 41:3307-3319. [PMID: 33500277 DOI: 10.1523/jneurosci.3013-19.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 12/15/2020] [Accepted: 12/18/2020] [Indexed: 11/21/2022] Open
Abstract
Golgi cells, together with granule cells and mossy fibers, form a neuronal microcircuit regulating information transfer at the cerebellum input stage. Despite theoretical predictions, little was known about long-term synaptic plasticity at Golgi cell synapses. Here, we have used whole-cell patch-clamp recordings and calcium imaging to investigate long-term synaptic plasticity at excitatory synapses impinging on Golgi cells. In acute mouse cerebellar slices, mossy fiber theta-burst stimulation (TBS) could induce either long-term potentiation (LTP) or long-term depression (LTD) at mossy fiber-Golgi cell and granule cell-Golgi cell synapses. This synaptic plasticity showed a peculiar voltage dependence, with LTD or LTP being favored when TBS induction occurred at depolarized or hyperpolarized potentials, respectively. LTP required, in addition to NMDA channels, activation of T-type Ca2+ channels, while LTD required uniquely activation of L-type Ca2+ channels. Notably, the voltage dependence of plasticity at the mossy fiber-Golgi cell synapses was inverted with respect to pure NMDA receptor-dependent plasticity at the neighboring mossy fiber-granule cell synapse, implying that the mossy fiber presynaptic terminal can activate different induction mechanisms depending on the target cell. In aggregate, this result shows that Golgi cells show cell-specific forms of long-term plasticity at their excitatory synapses, that could play a crucial role in sculpting the response patterns of the cerebellar granular layer.SIGNIFICANCE STATEMENT This article shows for the first time a novel form of Ca2+ channel-dependent synaptic plasticity at the excitatory synapses impinging on cerebellar Golgi cells. This plasticity is bidirectional and inverted with respect to NMDA receptor-dependent paradigms, with long-term depression (LTD) and long-term potentiation (LTP) being favored at depolarized and hyperpolarized potentials, respectively. Furthermore, LTP and LTD induction requires differential involvement of T-type and L-type voltage-gated Ca2+ channels rather than the NMDA receptors alone. These results, along with recent computational predictions, support the idea that Golgi cell plasticity could play a crucial role in controlling information flow through the granular layer along with cerebellar learning and memory.
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Masoli S, Ottaviani A, Casali S, D’Angelo E. Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity. PLoS Comput Biol 2020; 16:e1007937. [PMID: 33378395 PMCID: PMC7837495 DOI: 10.1371/journal.pcbi.1007937] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 01/26/2021] [Accepted: 11/13/2020] [Indexed: 02/06/2023] Open
Abstract
The Golgi cells are the main inhibitory interneurons of the cerebellar granular layer. Although recent works have highlighted the complexity of their dendritic organization and synaptic inputs, the mechanisms through which these neurons integrate complex input patterns remained unknown. Here we have used 8 detailed morphological reconstructions to develop multicompartmental models of Golgi cells, in which Na, Ca, and K channels were distributed along dendrites, soma, axonal initial segment and axon. The models faithfully reproduced a rich pattern of electrophysiological and pharmacological properties and predicted the operating mechanisms of these neurons. Basal dendrites turned out to be more tightly electrically coupled to the axon initial segment than apical dendrites. During synaptic transmission, parallel fibers caused slow Ca-dependent depolarizations in apical dendrites that boosted the axon initial segment encoder and Na-spike backpropagation into basal dendrites, while inhibitory synapses effectively shunted backpropagating currents. This oriented dendritic processing set up a coincidence detector controlling voltage-dependent NMDA receptor unblock in basal dendrites, which, by regulating local calcium influx, may provide the basis for spike-timing dependent plasticity anticipated by theory.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Stefano Casali
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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8
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Galindo SE, Toharia P, Robles ÓD, Ros E, Pastor L, Garrido JA. Simulation, visualization and analysis tools for pattern recognition assessment with spiking neuronal networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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9
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Song X, Valencia-Cabrera L, Peng H, Wang J, Pérez-Jiménez MJ. Spiking Neural P Systems with Delay on Synapses. Int J Neural Syst 2020; 31:2050042. [PMID: 32701003 DOI: 10.1142/s0129065720500422] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapses, SN P systems with delay on synapses (SNP-DS systems) are proposed in this work. Unlike the traditional SN P systems, where all the postsynaptic neurons receive spikes at the same instant from their presynaptic neuron, the postsynaptic neurons in SNP-DS systems would receive spikes at different instants, depending on the delay time on the synapses connecting them. It is proved that the SNP-DS systems are universal as number generators. Two small universal SNP-DS systems, with standard or extended rules, are constructed to compute functions, using 56 and 36 neurons, respectively. Moreover, a simulator has been provided, in order to check the correctness of these two SNP-DS systems, thus providing an experimental validation of the universality of the systems designed.
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Affiliation(s)
- Xiaoxiao Song
- School of Electrical Engineering and Electronic Information and Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Luis Valencia-Cabrera
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Andalucía 41004, Spain
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information and Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Mario J Pérez-Jiménez
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Andalucía 41004, Spain
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10
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Marín M, Sáez-Lara MJ, Ros E, Garrido JA. Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell. Front Cell Neurosci 2020; 14:161. [PMID: 32765220 PMCID: PMC7381211 DOI: 10.3389/fncel.2020.00161] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 05/13/2020] [Indexed: 11/17/2022] Open
Abstract
Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensity-frequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.
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Affiliation(s)
- Milagros Marín
- Department of Computer Architecture and Technology-CITIC, University of Granada, Granada, Spain.,Department of Biochemistry and Molecular Biology I, University of Granada, Granada, Spain
| | - María José Sáez-Lara
- Department of Biochemistry and Molecular Biology I, University of Granada, Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology-CITIC, University of Granada, Granada, Spain
| | - Jesús A Garrido
- Department of Computer Architecture and Technology-CITIC, University of Granada, Granada, Spain
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11
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Hu R, Huang Q, Wang H, He J, Chang S. Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns. Int J Neural Syst 2019; 29:1950006. [DOI: 10.1142/s0129065719500060] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neural networks are powerful computation tools for mimicking the human brain to solve realistic problems. Since spiking neural networks are a type of brain-inspired network, called the novel spiking system, Monitor-based Spiking Recurrent network (MbSRN), is derived to learn and represent patterns in this paper. This network provides a computational framework for memorizing the targets using a simple dynamic model that maintains biological plasticity. Based on a recurrent reservoir, the MbSRN presents a mechanism called a ‘monitor’ to track the components of the state space in the training stage online and to self-sustain the complex dynamics in the testing stage. The network firing spikes are optimized to represent the target dynamics according to the accumulation of the membrane potentials of the units. Stability analysis of the monitor conducted by limiting the coefficient penalty in the loss function verifies that our network has good anti-interference performance under neuron loss and noise. The results of solving some realistic tasks show that the MbSRN not only achieves a high goodness-of-fit of the target patterns but also maintains good spiking efficiency and storage capacity.
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Affiliation(s)
- Ruihan Hu
- School of Physics and Technology, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Qijun Huang
- School of Physics and Technology, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Hao Wang
- School of Physics and Technology, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Jin He
- School of Physics and Technology, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Sheng Chang
- School of Physics and Technology, Wuhan University, Wuhan 430072 Hubei, P. R. China
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12
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Naveros F, Luque NR, Ros E, Arleo A. VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model. IEEE TRANSACTIONS ON CYBERNETICS 2019; 50:4744-4757. [PMID: 30835236 DOI: 10.1109/tcyb.2019.2899246] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex (VOR) tasks. The spiking neural network computation, including event- and time-driven neural dynamics, neural activity, and spike-timing dependent plasticity (STDP) mechanisms, leads to a nondeterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This nondeterministic computation time motivates the integration of an RT supervisor module that is able to ensure a well-orchestrated neural computation time and robot operation. Actually, our neurorobotic experimental setup (VOR) benefits from the biological sensory motor delay between the cerebellum and the body to buffer the computational overloads as well as providing flexibility in adjusting the neural computation time and RT operation. The RT supervisor module provides for incremental countermeasures that dynamically slow down or speed up the cerebellar simulation by either halting the simulation or disabling certain neural computation features (i.e., STDP mechanisms, spike propagation, and neural updates) to cope with the RT constraints imposed by the real robot operation. This neurorobotic experimental setup is applied to different horizontal and vertical VOR adaptive tasks that are widely used by the neuroscientific community to address cerebellar functioning. We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation. This paper underlies the need for a two-stage learning process to facilitate VOR acquisition.
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13
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Carrillo RR, Naveros F, Ros E, Luque NR. A Metric for Evaluating Neural Input Representation in Supervised Learning Networks. Front Neurosci 2019; 12:913. [PMID: 30618549 PMCID: PMC6302114 DOI: 10.3389/fnins.2018.00913] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 11/20/2018] [Indexed: 11/13/2022] Open
Abstract
Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation.
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Affiliation(s)
- Richard R Carrillo
- Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Centro de Investigación en Tecnologías de la Información y de las Comunicaciones (CITIC-UGR), University of Granada, Granada, Spain
| | - Francisco Naveros
- Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Centro de Investigación en Tecnologías de la Información y de las Comunicaciones (CITIC-UGR), University of Granada, Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Centro de Investigación en Tecnologías de la Información y de las Comunicaciones (CITIC-UGR), University of Granada, Granada, Spain
| | - Niceto R Luque
- Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Centro de Investigación en Tecnologías de la Información y de las Comunicaciones (CITIC-UGR), University of Granada, Granada, Spain.,Aging in Vision and Action, Institut de la Vision, Inserm-UPMC-CNRS, Paris, France
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14
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Wu T, Bîlbîe FD, Păun A, Pan L, Neri F. Simplified and Yet Turing Universal Spiking Neural P Systems with Communication on Request. Int J Neural Syst 2018; 28:1850013. [DOI: 10.1142/s0129065718500132] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike. It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems. Theoretical results are shown in the cases of the SNQ P system used in both generating and accepting modes. Furthermore, the influence of the number of unbounded neurons (the number of spikes in a neuron is not bounded) on the computation power of SNQ P systems with one type of spike is investigated. It is found that SNQ P systems functioning as number generating devices with one type of spike and four unbounded neurons are Turing universal.
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Affiliation(s)
- Tingfang Wu
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Florin-Daniel Bîlbîe
- Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei Nr. 14, Sector 1, C.P. 010014, Bucharest, Romania
| | - Andrei Păun
- Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei Nr. 14, Sector 1, C.P. 010014, Bucharest, Romania
- Department of Bioinformatics, National Institute of Research and Development for Biological Sciences, Splaiul Independenţei, Nr. 296, Sector 6, Bucharest, Romania
| | - Linqiang Pan
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
- School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, P. R. China
| | - Ferrante Neri
- Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK
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15
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Rongala UB, Spanne A, Mazzoni A, Bengtsson F, Oddo CM, Jörntell H. Intracellular Dynamics in Cuneate Nucleus Neurons Support Self-Stabilizing Learning of Generalizable Tactile Representations. Front Cell Neurosci 2018; 12:210. [PMID: 30108485 PMCID: PMC6079306 DOI: 10.3389/fncel.2018.00210] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 06/26/2018] [Indexed: 12/12/2022] Open
Abstract
How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.
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Affiliation(s)
- Udaya B Rongala
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Anton Spanne
- Section for Neurobiology, Department of Experimental Medical Sciences, Biomedical Center, Lund University, Lund, Sweden
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Fredrik Bengtsson
- Section for Neurobiology, Department of Experimental Medical Sciences, Biomedical Center, Lund University, Lund, Sweden
| | - Calogero M Oddo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Henrik Jörntell
- Section for Neurobiology, Department of Experimental Medical Sciences, Biomedical Center, Lund University, Lund, Sweden
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16
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Martinez-Murcia FJ, Górriz JM, Ramírez J, Ortiz A. Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed? Int J Neural Syst 2018; 28:1850035. [PMID: 30215285 DOI: 10.1142/s0129065718500351] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional neural networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analyzing nuclear brain imaging? Are spatial and intensity normalizations still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization - and its type - is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.
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Affiliation(s)
| | - Juan M Górriz
- * Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,† Department of Psychiatry, University of Cambridge, Cambridge CB2 OSZ, UK
| | - Javier Ramírez
- * Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Andres Ortiz
- ‡ Department of Communications Engineering, University of Malaga, Malaga, Spain
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17
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Osinski BL, Kim A, Xiao W, Mehta NM, Kay LM. Pharmacological manipulation of the olfactory bulb modulates beta oscillations: testing model predictions. J Neurophysiol 2018; 120:1090-1106. [PMID: 29847235 DOI: 10.1152/jn.00090.2018] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The mammalian olfactory bulb (OB) generates gamma (40-100 Hz) and beta (15-30 Hz) local field potential (LFP) oscillations. Gamma oscillations arise at the peak of inhalation supported by dendrodendritic interactions between glutamatergic mitral cells (MCs) and GABAergic granule cells (GCs). Beta oscillations are induced by odorants in learning or odor sensitization paradigms, but their mechanism and function are still poorly understood. When centrifugal OB inputs are blocked, beta oscillations disappear, but gamma oscillations persist. Centrifugal inputs target primarily GABAergic interneurons in the GC layer (GCL) and regulate GC excitability, suggesting a causal link between beta oscillations and GC excitability. Our previous modeling work predicted that convergence of excitatory/inhibitory inputs onto MCs and centrifugal inputs onto GCs increase GC excitability sufficiently to produce beta oscillations primarily through voltage dependent calcium channel-mediated GABA release, independently of NMDA channels. We test some of the predictions of this model by examining the influence of NMDA and muscarinic acetylcholine (ACh) receptors, which affect GC excitability in different ways, on beta oscillations. A few minutes after intrabulbar infusion, scopolamine (muscarinic antagonist) suppressed odor-evoked beta in response to a strong stimulus but increased beta power in response to a weak stimulus, as predicted by our model. Pyriform cortex (PC) beta power was unchanged. Oxotremorine (muscarinic agonist) suppressed all oscillations, likely from overinhibition. APV, an NMDA receptor antagonist, suppressed gamma oscillations selectively (in OB and PC), lending support to the model's prediction that beta oscillations can be supported independently of NMDA receptors. NEW & NOTEWORTHY Olfactory bulb local field potential beta oscillations appear to be gated by GABAergic granule cell excitability. Reducing excitability with scopolamine reduces beta induced by strong odors but increases beta induced by weak odors. Beta oscillations rely on the same synapse as gamma oscillations but, unlike gamma, can persist in the absence of NMDA receptor activation. Pyriform cortex beta oscillations maintain power when olfactory bulb beta power is low, and the system maintains beta band coherence.
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Affiliation(s)
- Bolesław L Osinski
- Graduate Program in Biophysical Sciences, The University of Chicago , Chicago, Illinois.,Institute for Mind and Biology, The University of Chicago , Chicago, Illinois
| | - Alex Kim
- The College, The University of Chicago , Chicago, Illinois
| | - Wenxi Xiao
- Masters Program in Computational Social Sciences, The University of Chicago , Chicago, Illinois
| | - Nisarg M Mehta
- Institute for Mind and Biology, The University of Chicago , Chicago, Illinois
| | - Leslie M Kay
- Institute for Mind and Biology, The University of Chicago , Chicago, Illinois.,Department of Psychology, The University of Chicago , Chicago, Illinois
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18
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Antonietti A, Monaco J, D'Angelo E, Pedrocchi A, Casellato C. Dynamic Redistribution of Plasticity in a Cerebellar Spiking Neural Network Reproducing an Associative Learning Task Perturbed by TMS. Int J Neural Syst 2018; 28:1850020. [PMID: 29914314 DOI: 10.1142/s012906571850020x] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
During natural learning, synaptic plasticity is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks evolving under behavioral feedback and should involve changes distributed across multiple synaptic sites. In eyeblink classical conditioning (EBCC), the cerebellum learns to predict the precise timing between two stimuli, hence EBCC represents an elementary yet meaningful paradigm to investigate the cerebellar network functioning. We have simulated EBCC mechanisms by reconstructing a realistic cerebellar microcircuit model and embedding multiple plasticity rules imitating those revealed experimentally. The model was tuned to fit experimental EBCC human data, estimating the underlying learning time-constants. Learning started rapidly with plastic changes in the cerebellar cortex followed by slower changes in the deep cerebellar nuclei. This process was characterized by differential development of long-term potentiation and depression at individual synapses, with a progressive accumulation of plasticity distributed over the whole network. The experimental data included two EBCC sessions interleaved by a trans-cranial magnetic stimulation (TMS). The experimental and the model response data were not significantly different in each learning phase, and the model goodness-of-fit was [Formula: see text] for all the experimental conditions. The models fitted on TMS data revealed a slowed down re-acquisition (sessions-2) compared to the control condition ([Formula: see text]). The plasticity parameters characterizing each model significantly differ among conditions, and thus mechanistically explain these response changes. Importantly, the model was able to capture the alteration in EBCC consolidation caused by TMS and showed that TMS affected plasticity at cortical synapses thereby altering the fast learning phase. This, secondarily, also affected plasticity in deep cerebellar nuclei altering learning dynamics in the entire sensory-motor loop. This observation reveals dynamic redistribution of changes over the entire network and suggests how TMS affects local circuit computation and memory processing in the cerebellum.
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Affiliation(s)
- Alberto Antonietti
- 1 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy
| | - Jessica Monaco
- 2 Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Italy.,3 Brain Connectivity Center, Istituto Neurologico IRCCS Fondazione C. Mondino, Via Mondino 2, 1-27100 Pavia, Italy
| | - Egidio D'Angelo
- 2 Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Italy.,3 Brain Connectivity Center, Istituto Neurologico IRCCS Fondazione C. Mondino, Via Mondino 2, 1-27100 Pavia, Italy
| | - Alessandra Pedrocchi
- 1 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy
| | - Claudia Casellato
- 2 Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Italy
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19
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Antunes G, Faria da Silva SF, Simoes de Souza FM. Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder. Int J Neural Syst 2018; 28:1750058. [DOI: 10.1142/s0129065717500587] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.
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Affiliation(s)
- Gabriela Antunes
- Department of Physics, Faculdade de Filosofia, Ciencias e Letras de Ribeirao Preto, Universidade de Sao Paulo, Ribeirao Preto, SP, Brazil
| | | | - Fabio M. Simoes de Souza
- Center for Mathematics, Computation and Cognition, Federal University of ABC, Sao Bernardo do Campo, SP, Brazil
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20
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Abstract
The cerebellum is a central brain structure deeply integrated into major loops with the cerebral cortex, brainstem, and spinal cord. The cerebellum shows a complex regional organization consisting of modules with sagittal orientation. The cerebellum takes part in motor control and its lesions cause a movement incoordination syndrome called ataxia. Recent observations also imply involvement of the cerebellum in cognition and executive control, with an impact on pathologies like dyslexia and autism. The cerebellum operates as a forward controller learning to predict the precise timing of correlated events. The physiologic mechanisms of cerebellar functioning are still the object of intense research. The signals entering the cerebellum through the mossy fibers are processed in the granular layer and transmitted to Purkinje cells, while a collateral pathway activates the deep cerebellar nuclei (DCN). Purkinje cells in turn inhibit DCN, so that the cerebellar cortex operates as a side loop controlling the DCN. Learning is now known to occur through synaptic plasticity at multiple synapses in the granular layer, molecular layer, and DCN, extending the original concept of the Motor Learning Theory that predicted a single form of plasticity at the synapse between parallel fibers and Purkinje cells under the supervision of climbing fibers deriving from the inferior olive. Coordination derives from the precise regulation of timing and gain in the different cerebellar modules. The investigation of cerebellar dynamics using advanced physiologic recordings and computational models is now providing new clues on how the cerebellar network performs its internal computations.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
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21
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Weissenberger F, Meier F, Lengler J, Einarsson H, Steger A. Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population Activity. Int J Neural Syst 2017; 27:1750044. [DOI: 10.1142/s0129065717500447] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity.
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Affiliation(s)
- Felix Weissenberger
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Florian Meier
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Johannes Lengler
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Hafsteinn Einarsson
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Angelika Steger
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
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22
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Pan L, Păun G, Zhang G, Neri F. Spiking Neural P Systems with Communication on Request. Int J Neural Syst 2017; 27:1750042. [DOI: 10.1142/s0129065717500423] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.
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Affiliation(s)
- Linqiang Pan
- Key Laboratory of Image Information Processing, and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
- and Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, P. R. China
| | - Gheorghe Păun
- Institute of Mathematics of the Romanian Academy, P. O. Box 1-764, RO-014700 Bucharest, Romania
| | - Gexiang Zhang
- Robotics Research Center, Xihua University, Chengdu 610039, P. R. China
- Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education, Chengdu 610039, P. R. China
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Ferrante Neri
- Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, England, UK
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23
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Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 2017; 100:270-278. [PMID: 28974302 DOI: 10.1016/j.compbiomed.2017.09.017] [Citation(s) in RCA: 554] [Impact Index Per Article: 79.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 08/29/2017] [Accepted: 09/22/2017] [Indexed: 10/18/2022]
Abstract
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
| | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Hojjat Adeli
- Departments of Neuroscience, Neurology, Biomedical Engineering, Biomedical Informatics, and Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, United States
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24
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Guo L, Wang Z, Cabrerizo M, Adjouadi M. A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy. Int J Neural Syst 2017; 27:1750002. [DOI: 10.1142/s0129065717500022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.
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Affiliation(s)
- Lilin Guo
- Center for Advanced Technology and Education, Florida International University, 10555 W Flagler St, Miami, FL 33174, USA
| | - Zhenzhong Wang
- Center for Advanced Technology and Education, Florida International University, 10555 W Flagler St, Miami, FL 33174, USA
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education, Florida International University, 10555 W Flagler St, Miami, FL 33174, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, 10555 W Flagler St, Miami, FL 33174, USA
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25
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Naveros F, Garrido JA, Carrillo RR, Ros E, Luque NR. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks. Front Neuroinform 2017; 11:7. [PMID: 28223930 PMCID: PMC5293783 DOI: 10.3389/fninf.2017.00007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 01/18/2017] [Indexed: 12/12/2022] Open
Abstract
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.
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Affiliation(s)
- Francisco Naveros
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Jesus A Garrido
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Richard R Carrillo
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Niceto R Luque
- Vision Institute, Aging in Vision and Action LabParis, France; CNRS, INSERM, Pierre and Marie Curie UniversityParis, France
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26
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Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage. J Neurosci 2017; 37:2809-2823. [PMID: 28188217 DOI: 10.1523/jneurosci.2079-16.2016] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 11/10/2016] [Accepted: 12/28/2016] [Indexed: 11/21/2022] Open
Abstract
Spike-timing-dependent plasticity (STDP) is a form of long-term synaptic plasticity exploiting the time relationship between postsynaptic action potentials (APs) and EPSPs. Surprisingly enough, very little was known about STDP in the cerebellum, although it is thought to play a critical role for learning appropriate timing of actions. We speculated that low-frequency oscillations observed in the granular layer may provide a reference for repetitive EPSP/AP phase coupling. Here we show that EPSP-spike pairing at 6 Hz can optimally induce STDP at the mossy fiber-granule cell synapse in rats. Spike timing-dependent long-term potentiation and depression (st-LTP and st-LTD) were confined to a ±25 ms time-window. Because EPSPs led APs in st-LTP while APs led EPSPs in st-LTD, STDP was Hebbian in nature. STDP occurred at 6-10 Hz but vanished >50 Hz or <1 Hz (where only LTP or LTD occurred). STDP disappeared with randomized EPSP/AP pairing or high intracellular Ca2+ buffering, and its sign was inverted by GABA-A receptor activation. Both st-LTP and st-LTD required NMDA receptors, but st-LTP also required reinforcing signals mediated by mGluRs and intracellular calcium stores. Importantly, st-LTP and st-LTD were significantly larger than LTP and LTD obtained by modulating the frequency and duration of mossy fiber bursts, probably because STDP expression involved postsynaptic in addition to presynaptic mechanisms. These results thus show that a Hebbian form of STDP occurs at the cerebellum input stage, providing the substrate for phase-dependent binding of mossy fiber spikes to repetitive theta-frequency cycles of granule cell activity.SIGNIFICANCE STATEMENT Long-term synaptic plasticity is a fundamental property of the brain, causing persistent modifications of neuronal communication thought to provide the cellular basis of learning and memory. The cerebellum is critical for learning the appropriate timing of sensorimotor behaviors, but whether and how appropriate spike patterns could drive long-term synaptic plasticity remained unknown. Here, we show that this can actually occur through a form of spike-timing-dependent plasticity (STDP) at the cerebellar inputs stage. Pairing presynaptic and postsynaptic spikes at 6-10 Hz reliably induced STDP at the mossy fiber-granule cell synapse, with potentiation and depression symmetrically distributed within a ±25 ms time window. Thus, STDP can bind plasticity to the mossy fiber burst phase with high temporal precision.
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27
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D'Angelo E, Antonietti A, Casali S, Casellato C, Garrido JA, Luque NR, Mapelli L, Masoli S, Pedrocchi A, Prestori F, Rizza MF, Ros E. Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue. Front Cell Neurosci 2016; 10:176. [PMID: 27458345 PMCID: PMC4937064 DOI: 10.3389/fncel.2016.00176] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 06/23/2016] [Indexed: 11/13/2022] Open
Abstract
The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Alberto Antonietti
- NearLab - NeuroEngineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy
| | - Stefano Casali
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Claudia Casellato
- NearLab - NeuroEngineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy
| | - Jesus A Garrido
- Department of Computer Architecture and Technology, University of Granada Granada, Spain
| | - Niceto Rafael Luque
- Department of Computer Architecture and Technology, University of Granada Granada, Spain
| | - Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Alessandra Pedrocchi
- NearLab - NeuroEngineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy
| | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Martina Francesca Rizza
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-BicoccaMilan, Italy
| | - Eduardo Ros
- Department of Computer Architecture and Technology, University of Granada Granada, Spain
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