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1.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(6): 902-910, 2019 Dec 25.
Artigo em Chinês | MEDLINE | ID: mdl-31875362

RESUMO

Biological neural networks have dual properties of small-world attributes and scale-free attributes. Most of the current researches on neural networks are based on small-world networks or scale-free networks with lower clustering coefficient, however, the real brain network is a scale-free network with small-world attributes. In this paper, a scale-free spiking neural network with high clustering coefficient and small-world attribute was constructed. The dynamic evolution process was analyzed from three aspects: synaptic regulation process, firing characteristics and complex network characteristics. The experimental results show that, as time goes by, the synaptic strength gradually decreases and tends to be stable. As a result, the connection strength of the network decreases and tends to be stable; the firing rate of neurons gradually decreases and tends to be stable, and the synchronization becomes worse; the local information transmission efficiency is stable, the global information transmission efficiency is reduced and tends to be stable, and the small-world attributes are relatively stable. The dynamic characteristics vary with time and interact with each other. The regulation of synapses is based on the firing time of neurons, and the regulation of synapses will affect the firing of neurons and complex characteristics of networks. In this paper, a scale-free spiking neural network was constructed, which has biological authenticity. It lays a foundation for the research of artificial neural network and its engineering application.


Assuntos
Modelos Neurológicos , Plasticidade Neuronal , Potenciais de Ação , Sinapses
2.
Behav Brain Sci ; 42: e241, 2019 11 28.
Artigo em Inglês | MEDLINE | ID: mdl-31775929

RESUMO

Brains are information processing systems whose operational principles ultimately cannot be understood without resource to information theory. We suggest that understanding how external signals are represented in the brain is a necessary step towards employing further engineering tools (such as control theory) to understand the information processing performed by brain circuits during behaviour.


Assuntos
Modelos Neurológicos , Fenômenos Fisiológicos do Sistema Nervoso , Encéfalo , Metáfora
3.
Nat Neurosci ; 22(12): 2060-2065, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31686023

RESUMO

Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed 'inception loops', a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli-most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.


Assuntos
Modelos Neurológicos , Neurônios/fisiologia , Córtex Visual/fisiologia , Animais , Simulação por Computador , Movimentos Oculares/fisiologia , Feminino , Masculino , Camundongos , Camundongos Transgênicos , Dinâmica não Linear , Estimulação Luminosa/métodos , Percepção Visual/fisiologia
4.
Phys Rev Lett ; 123(17): 178103, 2019 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-31702278

RESUMO

We develop a phenomenological coarse-graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse-grained variables seem to approach a fixed non-Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a nontrivial fixed point.


Assuntos
Hipocampo/fisiologia , Modelos Neurológicos , Neurônios/fisiologia , Animais , Hipocampo/citologia , Humanos , Camundongos , Rede Nervosa/citologia , Rede Nervosa/fisiologia , Neurônios/citologia
6.
Nat Neurosci ; 22(12): 2066-2077, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31659343

RESUMO

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions that fail to maximize expected value. We reasoned that these 'non-greedy' decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here using reinforcement learning models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stem from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by blood oxygen level-dependent responses to obtained rewards in the dorsal anterior cingulate cortex and by phasic pupillary dilation, suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine system. Together, these findings indicate that most behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.


Assuntos
Tomada de Decisões/fisiologia , Aprendizagem/fisiologia , Recompensa , Adulto , Comportamento de Escolha/fisiologia , Feminino , Lobo Frontal/fisiologia , Humanos , Imagem por Ressonância Magnética , Masculino , Modelos Neurológicos , Neuroimagem , Pupila/fisiologia , Adulto Jovem
7.
Nat Commun ; 10(1): 4745, 2019 10 18.
Artigo em Inglês | MEDLINE | ID: mdl-31628322

RESUMO

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an 'internal tuning-curve' that characterizes its activity relative to the network activity, rather than relative to any predefined external variable, revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the 'trajectory-phase'. The internal structure was conserved across mice, allowing using one animal's data to decode another animal's behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.


Assuntos
Movimentos da Cabeça/fisiologia , Rede Nervosa/fisiologia , Neurônios/fisiologia , Córtex Pré-Frontal/fisiologia , Percepção Espacial/fisiologia , Potenciais de Ação/fisiologia , Algoritmos , Animais , Cognição/fisiologia , Hipocampo/citologia , Hipocampo/fisiologia , Masculino , Camundongos Endogâmicos C57BL , Modelos Neurológicos , Rede Nervosa/citologia , Orientação/fisiologia , Córtex Pré-Frontal/citologia
8.
Nat Commun ; 10(1): 4747, 2019 10 18.
Artigo em Inglês | MEDLINE | ID: mdl-31628329

RESUMO

The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dependencies between functional signals in directly connected areas can be expected higher. However, the degree to which brain function is bound by the underlying wiring diagram remains a complex question that has been only partially answered. Here, we introduce the structural-decoupling index to quantify the coupling strength between structure and function, and we reveal a macroscale gradient from brain regions more strongly coupled, to regions more strongly decoupled, than expected by realistic surrogate data. This gradient spans behavioral domains from lower-level sensory function to high-level cognitive ones and shows for the first time that the strength of structure-function coupling is spatially varying in line with evidence derived from other modalities, such as functional connectivity, gene expression, microstructural properties and temporal hierarchy.


Assuntos
Encéfalo/fisiologia , Córtex Cerebral/fisiologia , Rede Nervosa/fisiologia , Neurônios/fisiologia , Algoritmos , Encéfalo/anatomia & histologia , Encéfalo/citologia , Mapeamento Encefálico/métodos , Córtex Cerebral/anatomia & histologia , Córtex Cerebral/citologia , Humanos , Imagem por Ressonância Magnética , Modelos Neurológicos , Rede Nervosa/anatomia & histologia , Rede Nervosa/citologia , Vias Neurais/anatomia & histologia , Vias Neurais/citologia , Vias Neurais/fisiologia
10.
EMBO J ; 38(21): e103331, 2019 10 04.
Artigo em Inglês | MEDLINE | ID: mdl-31602659

RESUMO

Research that uses stem cell-based chimeras promises to advance our understanding of human developmental biology, as well as new medical interventions, such as generating transplantable human organs in livestock. However, along with these exciting research possibilities come moral concerns about the moral humanization of animals, especially when it comes to the potential effects of human cells in the brains of experimental animals. Recent work involving neurologically chimeric mice may suggest that such worries are reasonable. However, this overlooks the crucial social and neurological conditions for enabling the development of conscious self-awareness, the absence of which leaves us only with animal welfare to monitor and consider.


Assuntos
Quimera/metabolismo , Modelos Animais , Modelos Neurológicos , Pesquisa com Células-Tronco/ética , Bem-Estar do Animal , Animais , Humanos , Camundongos
11.
Nat Neurosci ; 22(11): 1871-1882, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31591558

RESUMO

Sensorimotor control during overt movements is characterized in terms of three building blocks: a controller, a simulator and a state estimator. We asked whether the same framework could explain the control of internal states in the absence of movements. Recently, it was shown that the brain controls the timing of future movements by adjusting an internal speed command. We trained monkeys in a novel task in which the speed command had to be dynamically controlled based on the timing of a sequence of flashes. Recordings from the frontal cortex provided evidence that the brain updates the internal speed command after each flash based on the error between the timing of the flash and the anticipated timing of the flash derived from a simulated motor plan. These findings suggest that cognitive control of internal states may be understood in terms of the same computational principles as motor control.


Assuntos
Lobo Frontal/fisiologia , Modelos Neurológicos , Movimento/fisiologia , Percepção do Tempo/fisiologia , Animais , Macaca mulatta , Masculino , Desempenho Psicomotor/fisiologia
12.
Nat Commun ; 10(1): 4468, 2019 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-31578320

RESUMO

State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.


Assuntos
Potenciais de Ação/fisiologia , Hipocampo/fisiologia , Vias Neurais/fisiologia , Neurônios/fisiologia , Potenciais Sinápticos/fisiologia , Algoritmos , Animais , Hipocampo/anatomia & histologia , Hipocampo/citologia , Modelos Lineares , Modelos Neurológicos , Neurônios/citologia , Ratos
13.
Nat Commun ; 10(1): 4441, 2019 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-31570719

RESUMO

What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent fluctuations in synaptic strengths. How memories can survive these fluctuations and the accompanying stabilizing homeostatic mechanisms is a fundamental open question. Here we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate. We find that homeostatic stabilization of fluctuations differentially affects different aspects of network connectivity. Specifically, memories stored as time-varying attractors of neural dynamics are more resilient to erosion than fixed-points. Such dynamic attractors can be learned by biologically plausible learning-rules and support associative retrieval. Our results suggest a link between the properties of learning-rules and those of network-level memory representations, and point at experimentally measurable signatures.


Assuntos
Memória/fisiologia , Modelos Neurológicos , Rede Nervosa/fisiologia , Sinapses/fisiologia , Algoritmos , Simulação por Computador , Homeostase , Aprendizagem , Memória de Longo Prazo/fisiologia , Plasticidade Neuronal/fisiologia , Neurônios/fisiologia , Dinâmica não Linear , Software
14.
Nat Neurosci ; 22(11): 1751-1760, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31611705

RESUMO

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.


Assuntos
Encéfalo/fisiologia , Neuroimagem Funcional/métodos , Modelos Neurológicos , Vias Neurais/fisiologia , Animais , Humanos , Estudos de Validação como Assunto
15.
PLoS Comput Biol ; 15(9): e1006698, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31557151

RESUMO

Although information theoretic approaches have been used extensively in the analysis of the neural code, they have yet to be used to describe how information is accumulated in time while sensory systems are categorizing dynamic sensory stimuli such as speech sounds or visual objects. Here, we present a novel method to estimate the cumulative information for stimuli or categories. We further define a time-varying categorical information index that, by comparing the information obtained for stimuli versus categories of these same stimuli, quantifies invariant neural representations. We use these methods to investigate the dynamic properties of avian cortical auditory neurons recorded in zebra finches that were listening to a large set of call stimuli sampled from the complete vocal repertoire of this species. We found that the time-varying rates carry 5 times more information than the mean firing rates even in the first 100 ms. We also found that cumulative information has slow time constants (100-600 ms) relative to the typical integration time of single neurons, reflecting the fact that the behaviorally informative features of auditory objects are time-varying sound patterns. When we correlated firing rates and information values, we found that average information correlates with average firing rate but that higher-rates found at the onset response yielded similar information values as the lower-rates found in the sustained response: the onset and sustained response of avian cortical auditory neurons provide similar levels of independent information about call identity and call-type. Finally, our information measures allowed us to rigorously define categorical neurons; these categorical neurons show a high degree of invariance for vocalizations within a call-type. Peak invariance is found around 150 ms after stimulus onset. Surprisingly, call-type invariant neurons were found in both primary and secondary avian auditory areas.


Assuntos
Córtex Auditivo , Modelos Neurológicos , Neurônios/fisiologia , Vocalização Animal/fisiologia , Estimulação Acústica , Animais , Córtex Auditivo/citologia , Córtex Auditivo/fisiologia , Biologia Computacional , Feminino , Tentilhões/fisiologia , Masculino
16.
Nat Commun ; 10(1): 4289, 2019 09 19.
Artigo em Inglês | MEDLINE | ID: mdl-31537787

RESUMO

Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.


Assuntos
Encéfalo/fisiologia , Comunicação Celular/fisiologia , Rede Nervosa/fisiologia , Vias Neurais/fisiologia , Adulto , Animais , Conectoma , Drosophila/fisiologia , Feminino , Humanos , Macaca/fisiologia , Masculino , Camundongos , Modelos Neurológicos , Transdução de Sinais/fisiologia , Adulto Jovem
18.
PLoS Comput Biol ; 15(9): e1007275, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31513570

RESUMO

In many brain areas, sensory responses are heavily modulated by factors including attentional state, context, reward history, motor preparation, learned associations, and other cognitive variables. Modelling the effect of these modulatory factors on sensory responses has proven challenging, mostly due to the time-varying and nonlinear nature of the underlying computations. Here we present a computational model capable of capturing and dissociating multiple time-varying modulatory effects on neuronal responses on the order of milliseconds. The model's performance is tested on extrastriate perisaccadic visual responses in nonhuman primates. Visual neurons respond to stimuli presented around the time of saccades differently than during fixation. These perisaccadic changes include sensitivity to the stimuli presented at locations outside the neuron's receptive field, which suggests a contribution of multiple sources to perisaccadic response generation. Current computational approaches cannot quantitatively characterize the contribution of each modulatory source in response generation, mainly due to the very short timescale on which the saccade takes place. In this study, we use a high spatiotemporal resolution experimental paradigm along with a novel extension of the generalized linear model framework (GLM), termed the sparse-variable GLM, to allow for time-varying model parameters representing the temporal evolution of the system with a resolution on the order of milliseconds. We used this model framework to precisely map the temporal evolution of the spatiotemporal receptive field of visual neurons in the middle temporal area during the execution of a saccade. Moreover, an extended model based on a factorization of the sparse-variable GLM allowed us to disassociate and quantify the contribution of individual sources to the perisaccadic response. Our results show that our novel framework can precisely capture the changes in sensitivity of neurons around the time of saccades, and provide a general framework to quantitatively track the role of multiple modulatory sources over time.


Assuntos
Modelos Neurológicos , Neurônios/fisiologia , Algoritmos , Animais , Biologia Computacional/métodos , Macaca mulatta , Masculino , Estimulação Luminosa , Movimentos Sacádicos/fisiologia
19.
PLoS Comput Biol ; 15(9): e1007375, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31545787

RESUMO

Dopaminergic neurons (DAs) of the rodent substantia nigra pars compacta (SNc) display varied electrophysiological properties in vitro. Despite this, projection patterns and functional inputs from DAs to other structures are conserved, so in vivo delivery of consistent, well-timed dopamine modulation to downstream circuits must be coordinated. Here we show robust coordination by linear parameter controllers, discovered through powerful mathematical analyses of data and models, and from which consistent control of DA subthreshold oscillations (STOs) and spontaneous firing emerges. These units of control represent coordinated intracellular variables, sufficient to regulate complex cellular properties with radical simplicity. Using an evolutionary algorithm and dimensionality reduction, we discovered metaparameters, which when regressed against STO features, revealed a 2-dimensional control plane for the neuron's 22-dimensional parameter space that fully maps the natural range of DA subthreshold electrophysiology. This plane provided a basis for spiking currents to reproduce a large range of the naturally occurring spontaneous firing characteristics of SNc DAs. From it we easily produced a unique population of models, derived using unbiased parameter search, that show good generalization to channel blockade and compensatory intracellular mechanisms. From this population of models, we then discovered low-dimensional controllers for regulating spontaneous firing properties, and gain insight into how currents active in different voltage regimes interact to produce the emergent activity of SNc DAs. Our methods therefore reveal simple regulators of neuronal function lurking in the complexity of combined ion channel dynamics.


Assuntos
Potenciais de Ação/fisiologia , Neurônios Dopaminérgicos/metabolismo , Neurônios Dopaminérgicos/fisiologia , Modelos Neurológicos , Algoritmos , Animais , Biologia Computacional , Ratos , Substância Negra/citologia , Substância Negra/metabolismo
20.
Neural Netw ; 119: 332-340, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31499357

RESUMO

In recent years, spiking neural networks (SNNs) have demonstrated great success in completing various machine learning tasks. We introduce a method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via inhibitory interactions to learn features from different locations of the input space. These locally-connected SNNs (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore a biologically inspired n-gram classification approach allowing parallel processing over various patches of the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which respectively match state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large numbers of synapses and neurons. Our results have been obtained using the BindsNET library, which allows efficient machine learning implementations of spiking neural networks.


Assuntos
Aprendizado de Máquina , Plasticidade Neuronal/fisiologia , Neurônios/fisiologia , Modelos Neurológicos
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