Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that simultaneously trains synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher accuracies on various static and neuromorphic datasets than SNNs trained with two single-learning models of the synaptic learning (SL) and the threshold learning (TL). During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework (JDF). Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.Comment: 13 pages, 9 figures, submitted for publicatio

소뇌 퍼킨지 세포 내재적 흥분성의 활동-의존적 조절

학위논문 (박사)-- 서울대학교 대학원 : 의과대학 의과학과, 2019. 2. 김상정.Learning rule has been thought to be implemented by activity-dependent modifications of synaptic function and neuronal excitability which contributing to maximization the information flow in the neural network. Since the sensory information is conveyed by forms of action potential (AP) firing, the plasticity of the intrinsic excitability (intrinsic plasticity) has been highlighted the computational feature of the brain. Given the cerebellar Purkinje cells (PCs) is the sole output neurons in the cerebellar cortex, coordination of the synaptic plasticity at the parallel fiber (PF) to PC synapses including long-term depression (LTD) and long-term potentiation (LTP) but also the intrinsic plasticity may play a essential role in information processing in the cerebellum. In this Dissertation, I have investigated several features of intrinsic plasticity in the cerebellar PCs in an activity-dependent manner and their cellular mechanism. Furthermore, the functional implications of the intrinsic plasticity in the cerebellum-dependent behavioral output are discussed. Firstly, I first cover the ion channels regulating the spiking activity of the cerebellar PCs and the cellular mechanisms of the plastic changes in excitability. Various ion channels indeed harmonize the cellular activity and shaping the optimal ranges of the neuronal excitability. Among the ion channels expressed in the cerebellar PCs, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels contribute to the non-Hebbian homeostatic intrinsic plasticity in the cerebellar PCs. Chronic activity-deprivation of PC activity caused the upregulation of agonist-independent activity of type 1 metabotropic glutamate receptor (mGluR1). The increased mGluR1 activity consequently enhanced the HCN channel current density through protein kinase A (PKA) pathway thereby downregulation of intrinsic excitability in PCs. In addition, the intrinsic excitability of PCs is found to be modulated by synaptic activity. Of interest, I investigated that the PF-PC LTD is accompanied by LTD of intrinsic excitability (LTD-IE). The LTD-IE indeed shared intracellular signal cascade for governing the synaptic LTD such as large amount of Ca2+ influx, mGluR1, protein kinase C (PKC) and Ca2+-calmodulin-dependent protein kinase II (CaMKII) activation. Interestingly, the LTD-IE reduced PC spike output without changes in patterns of synaptic integration and spike generation, suggesting that the intrinsic plasticity alters the quantity of information rather than the quality of information processing. In consistent, the LTD-IE was shown in the floccular PCs when the PF-PC LTD occurs. Notably, not only the synaptic LTD but also LTD-IE was found to be formed at the conditioned dendritic branch. Thus, synaptic plasticity could significantly affect to the neuronal net output through the synergistic coordination of synaptic and intrinsic plasticity in the dendrosomatic axis of the cerebellar PCs. In conclusion, the activity-dependent modulation of intrinsic excitability may contribute to dynamic tuning of the cerebellar PC output for appropriate signal transduction into the downstream neurons of the cerebellar PCs.생명체는 끊임없이 주변환경에 반응하여 행동을 수정하며 이러한 적응은 변화하는 환경에서 생존에 필수적이다. 소뇌-운동 학습은 대표적인 적응 행동의 예이다. 다양한 감각 신호들이 소뇌로 전달되어 처리된 후 소뇌 출력을 통해 운동 협응이 이루어진다. 이러한 소뇌-운동 학습 및 소뇌 기능 조절의 세포 생리학적 기전으로 소뇌 퍼킨지 세포의 시냅스 장기저하가 오랫동안 주목받았다. 퍼킨지 세포의 시냅스 장기저하가 나타나지 않는 유전자 변형 동물 모델들에서 소뇌-운동 학습이 정상적으로 일어나지 않는 현상이 관찰되었기 때문에 시냅스 장기저하 이론은 오랜 시간 소뇌-운동 학습의 기전으로 지지 받았다. 하지만 최근 10년 동안의 연구결과는 시냅스 장기저하만으로 소뇌-운동 학습 및 기능 조절을 설명할 수 없다고 반박한다. 특히 소뇌 퍼킨지 세포는 소뇌 피질로 전달된 감각신호를 처리하여 출력을 담당하는 유일한 신경세포이므로 운동 학습 상황에서 소뇌의 출력이 어떻게 조절되는지를 이해하는 것이 중요하게 인식되었다. 감각 신호가 신경 회로 내에서 전달될 때 활동 전압의 형태로 전달되기 때문에 활동 전압의 발생 빈도 및 패턴 조절 양상에 대한 이해는 소뇌 운동 학습의 기전을 밝히는 데에 중요하다. 본 박사학위 논문에서는 먼저 소뇌 퍼킨지 세포의 내재적 흥분성을 조절하는 여러가지 이온 통로들의 특성에 대해 정리하고 더 나아가 내재적 흥분성 가소성의 기전 및 생리학적 의의를 제시하였다. 소뇌 퍼킨지 세포의 흥분성은 활동-의존적 가소성을 보이는데, 시냅스의 활동이 아닌 소뇌 회로 활동성의 장기적인 변화에 대응하여 나타날 수 있다. 소뇌 회로의 활동을 2일 간의 tetrodotoxin (TTX, 1µM) 처리를 통해 저해하였을 때 과분극에 의해 발생하는 내향전류 (Ih) 증가를 통한 소뇌 퍼킨지 세포의 흥분성이 감소되는 것을 전기생리학적 기록을 통해 관찰하였다. 이러한 장기적인 소뇌 회로의 활동성 변화에 의한 퍼킨지 세포의 내재적 흥분성 감소의 세포생리학적 기전으로서 대사성 글루타메이트 수용체의 길항제-비의존적인 활동성 증가 및 그로 인한 PKA의 증가에 의해 발생함을 생화학 및 전기생리학적 방법을 통해 규명하였다. 이처럼 소뇌 퍼킨지 세포의 내재적 흥분성은 소뇌 회로 내에서 역동적으로 조절되어 소뇌 기능을 조절한다. 더 나아가 퍼킨지 세포의 흥분성 조절과 소뇌-기억형성과의 관계성을 검증하기 위해 소뇌-학습의 세포생리학적 기전으로 알려져있는 퍼킨지 세포 시냅스 장기저하 유도 후 흥분성의 변화를 관찰하였다. 흥미롭게도 퍼킨지 세포의 내재적 흥분성 역시 시냅스 가소성과 마찬가지로 평행섬유와 등반섬유의 활성을 통해 가소성을 보이는데 이 흥분성의 가소성은 대사성 글루타메이트 수용체, PKC 그리고 CaMKII와 같은 시냅스 장기 저하를 야기하는 세포 내 신호전달기전을 필요로 한다. 이러한 실험결과를 통해 시냅스 장기저하가 발생할 때 소뇌 퍼킨지 세포의 내재적 흥분성 역시 같이 감소하여 소뇌 운동 시 소뇌 피질의 출력이 크게 감소함을 예상할 수 있다. 실제로 소뇌 퍼킨지 세포의 신경가소성을 유도한 후 평행섬유를 자극하여 나타나는 퍼킨지 세포의 활동 전압 발생 빈도를 측정해 본 결과, 시냅스 장기저하와 흥분성의 장기저하가 함께 발생했을 때에만 소뇌 퍼킨지 세포의 출력이 유의미하게 감소하는 것을 관찰하였다. 특히 퍼킨지 세포의 활동-의존적 흥분성의 가소성은 시냅스 가소성과 마찬가지로 특정 수상돌기 가지 특이적으로 발생함을 관찰하였다. 이를 통해 퍼킨지 세포의 시냅스 가소성과 흥분성 가소성의 유기적인 연합을 통해 소뇌 퍼킨지 세포의 출력신호가 조절되어 소뇌-운동학습을 조절함을 알 수 있다. 결론적으로 본 박사학위 논문의 연구결과들은 소뇌 퍼킨지 세포의 출력은 퍼킨지 세포의 시냅스 가소성 혹은 흥분성의 조절과 비선형관계를 보이며 이러한 시냅스 가소성과 내재적 가소성의 시너지는 소뇌 정보 저장 능력을 극대화하여 소뇌 기능 조절 및 정보저장에 중요한 역할을 담당하고 있음을 제시한다.Preface Abstract General introduction Chapter 1. Summary of the previous literatures and further implication for physiological significance of the intrinsic plasticity in the cerebellar Purkinje cells Summary. 1.1 Ion channels and spiking activity of the cerebellar Purkinje cells 1.1.1 Voltage-gated Na+ channels 1.1.2 Voltage-gated K+ channels and Ca2+-activated K+ channels 1.2 Activity-dependent plasticity of intrinsic excitability through ion channel modulation 1.2.1 Activity-dependent plasticity of intrinsic. excitability through ion channel 1.2.2 Possible mechanisms for LTD-IE. 1.2.3 Upside down: to what extent does bidirectional intrinsic plasticity in. the cerebellar dependent-motor learning do? 1.3 The further implication of intrinsic plasticity in the memory circuits. Chapter 2. Type 1 metabotropic glutamate receptor mediates homeostatic control of intrinsic excitability through hyperpolarization-activated current in cerebellar Purkinje cells Introduction Material and Method Results 2.1 Chronic activity-deprivation reduces intrinsic excitability of the cerebellar. Purkinje cells 35 2.2 Homeostatic intrinsic plasticity of the cerebellar Purkinje cells is mediated activity-dependent modulation of Ih 2.3 Homeostatic intrinsic plasticity of the cerebellar Purkinje cells requires agonist-independent action of mGluR1 2.4 Homeostatic intrinsic plasticity of the cerebellar Purkinje cells is mediated. PKA activity Discussion Chapter 3. Long-Term Depression of Intrinsic Excitability Accompanied by Synaptic Depression in Cerebellar Purkinje Cells Introduction Material and Method Results 3.1 LTD of intrinsic excitability of PC accompanied by PF-PC LTD 3.2 LTD-IE has different developing kinetics from synaptic LTD 3.3 LTD-IE was not reversed by subsequent LTP-IE induction 3.4 The number of recruited synapses were not correlated to the magnitude of the neuronal 3.5 Information processing after LTD induction LTD-IE was not. reversed by subsequent LTP-IE induction 3.6 LTD-IE required the Ca2+-signal but not depended on the Ca2+-activated K+ channels Discussion Chapter 4. Synergies between synaptic depression and intrinsic plasticity of the cerebellar Purkinje cells determining the Purkinje cell output Introduction Material and Method Restuls 4.1 Timing rules of intrinsic plasticity of floccular PCs 87 4.2 Intrinsic plasticity shares intracellular signaling for PF-PC LTD 4.3 Conditioned PF branches contributing to robust reduction of spike output of the PCs 4.4 Sufficient changes in spiking output require both of plasticity, synaptic and. intrinsic plasticity 4.5 Supralinearity of spiking output coordination after induction of PC plasticity Discussion Bibliography Abstract in Korean AcknowledgementDocto

Intrinsic adaptation in autonomous recurrent neural networks

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depends crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting or chaotic activity patterns. We study the influence of non-synaptic plasticity on the default dynamical state of recurrent neural networks. The non-synaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes, a regular synchronized, an overall chaotic and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interseeded by chaotic bursts which respond sensitively to input signals. We discuss these finding in the context of self-organized information processing and critical brain dynamics.Comment: 24 pages, 8 figure

Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules

Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence. We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The adaption rules are Hebbian, self-limiting, and result from the minimization of the Fisher information with respect to the synaptic flux. We perform a series of simulations examining the behavior of the new learning rules in various circumstances. The vector of synaptic weights aligns with the principal direction of input activities, whenever one is present. A linear discrimination is performed when there are two or more principal directions; directions having bimodal firing-rate distributions, being characterized by a negative excess kurtosis, are preferred. We find robust performance and full homeostatic adaption of the synaptic weights results as a by-product of the synaptic flux minimization. This self-limiting behavior allows for stable online learning for arbitrary durations. The neuron acquires new information when the statistics of input activities is changed at a certain point of the simulation, showing however, a distinct resilience to unlearn previously acquired knowledge. Learning is fast when starting with randomly drawn synaptic weights and substantially slower when the synaptic weights are already fully adapted

Excitatory postsynaptic potentials in rat neocortical neurons in vitro. III. Effects of a quinoxalinedione non-NMDA receptor antagonist

1. Intracellular microelectrodes were used to obtain recordings from neurons in layer II/III of rat frontal cortex. A bipolar electrode positioned in layer IV of the neocortex was used to evoke postsynaptic potentials. Graded series of stimulation were employed to selectively activate different classes of postsynaptic responses. The sensitivity of postsynaptic potentials and iontophoretically applied neurotransmitters to the non-N-methyl-D-asparate (NMDA) antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) was examined. 2. As reported previously, low-intensity electrical stimulation of cortical layer IV evoked short-latency early excitatory postsynaptic potentials (eEPSPs) in layer II/III neurons. CNQX reversibly antagonized eEPSPs in a dose-dependent manner. Stimulation at intensities just subthreshold for activation of inhibitory postsynaptic potentials (IPSPs) produced long-latency (10 to 40-ms) EPSPs (late EPSPs or 1EPSPs). CNQX was effective in blocking 1EPSPs. 3. With the use of stimulus intensities at or just below threshold for evoking an action potential, complex synaptic potentials consisting of EPSP-IPSP sequences were observed. Both early, Cl(-)-dependent and late, K(+)-dependent IPSPs were reduced by CNQX. This effect was reversible on washing. This disinhibition could lead to enhanced excitability in the presence of CNQX. 4. Iontophoretic application of quisqualate produced a membrane depolarization with superimposed action potentials, whereas NMDA depolarized the membrane potential and evoked bursts of action potentials. At concentrations up to 5 microM, CNQX selectively antagonized quisqualate responses. NMDA responses were reduced by 10 microM CNQX. D-Serine (0.5-2 mM), an agonist at the glycine regulatory site on the NMDA receptor, reversed the CNQX depression of NMDA responses

Neuroplasticity of Ipsilateral Cortical Motor Representations, Training Effects and Role in Stroke Recovery

This thesis examines the contribution of the ipsilateral hemisphere to motor control with the aim of evaluating the potential of the contralesional hemisphere to contribute to motor recovery after stroke. Predictive algorithms based on neurobiological principles emphasize integrity of the ipsilesional corticospinal tract as the strongest prognostic indicator of good motor recovery. In contrast, extensive lesions placing reliance on alternative contralesional ipsilateral motor pathways are associated with poor recovery. Within the predictive algorithms are elements of motor control that rely on contributions from ipsilateral motor pathways, suggesting that balanced, parallel contralesional contributions can be beneficial. Current therapeutic approaches have focussed on the maladaptive potential of the contralesional hemisphere and sought to inhibit its activity with neuromodulation. Using Transcranial Magnetic Stimulation I seek examples of beneficial plasticity in ipsilateral cortical motor representations of expert performers, who have accumulated vast amounts of deliberate practise training skilled bilateral activation of muscles habitually under ipsilateral control. I demonstrate that ipsilateral cortical motor representations reorganize in response to training to acquisition of skilled motor performance. Features of this reorganization are compatible with evidence suggesting ipsilateral importance in synergy representations, controlled through corticoreticulopropriospinal pathways. I demonstrate that ipsilateral plasticity can associate positively with motor recovery after stroke. Features of plastic change in ipsilateral cortical representations are shown in response to robotic training of chronic stroke patients. These findings have implications for the individualization of motor rehabilitation after stroke, and prompt reappraisal of the approach to therapeutic intervention in the chronic phase of stroke