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1.
Cell Rep ; 43(2): 113785, 2024 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-38363673

RESUMO

Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.


Assuntos
Cristalino , Lentes , Animais , Humanos , Camundongos , Animais Geneticamente Modificados , Encéfalo , Análise por Conglomerados
2.
Nat Comput Sci ; 4(1): 19-28, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38177495

RESUMO

The brain is an intricate assembly of intercommunicating neurons whose input-output function is only partially understood. The role of active dendrites in shaping spiking responses, in particular, is unclear. Although existing models account for active dendrites and spiking responses, they are too complex to analyze analytically and demand long stochastic simulations. Here we combine cable and renewal theory to describe how input fluctuations shape the response of neuronal ensembles with active dendrites. We found that dendritic input readily and potently controls interspike interval dispersion. This phenomenon can be understood by considering that neurons display three fundamental operating regimes: one mean-driven regime and two fluctuation-driven regimes. We show that these results are expected to appear for a wide range of dendritic properties and verify predictions of the model in experimental data. These findings have implications for the role of interspike interval dispersion in learning and for theories of attractor states.


Assuntos
Dendritos , Sinapses , Dendritos/fisiologia , Sinapses/fisiologia , Neurônios/fisiologia , Algoritmos
3.
J Physiol ; 602(3): 417-420, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38071740
4.
J Physiol ; 601(23): 5165-5193, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-37889516

RESUMO

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.


Assuntos
Neurônios , Transmissão Sináptica , Neurônios/fisiologia , Transmissão Sináptica/fisiologia , Potenciais de Ação/fisiologia , Plasticidade Neuronal/fisiologia
5.
Elife ; 122023 01 19.
Artigo em Inglês | MEDLINE | ID: mdl-36655738

RESUMO

By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.


Assuntos
Núcleo Dorsal da Rafe , Serotonina , Camundongos , Animais , Núcleo Dorsal da Rafe/fisiologia , Serotonina/fisiologia , Neurônios/fisiologia , Redes Neurais de Computação
6.
J Neurosci ; 42(45): 8460-8467, 2022 11 09.
Artigo em Inglês | MEDLINE | ID: mdl-36351832

RESUMO

Dendrites receive the vast majority of a single neuron's inputs, and coordinate the transformation of these signals into neuronal output. Ex vivo and theoretical evidence has shown that dendrites possess powerful processing capabilities, yet little is known about how these mechanisms are engaged in the intact brain or how they influence circuit dynamics. New experimental and computational technologies have led to a surge in interest to unravel and harness their computational potential. This review highlights recent and emerging work that combines established and cutting-edge technologies to identify the role of dendrites in brain function. We discuss active dendritic mediation of sensory perception and learning in neocortical and hippocampal pyramidal neurons. Complementing these physiological findings, we present theoretical work that provides new insights into the underlying computations of single neurons and networks by using biologically plausible implementations of dendritic processes. Finally, we present a novel brain-computer interface task, which assays somatodendritic coupling to study the mechanisms of biological credit assignment. Together, these findings present exciting progress in understanding how dendrites are critical for in vivo learning and behavior, and highlight how subcellular processes can contribute to our understanding of both biological and artificial neural computation.


Assuntos
Dendritos , Células Piramidais , Dendritos/fisiologia , Células Piramidais/fisiologia , Neurônios/fisiologia , Hipocampo , Aprendizagem , Modelos Neurológicos , Potenciais de Ação/fisiologia
7.
Adv Exp Med Biol ; 1359: 69-86, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35471535

RESUMO

The generalized integrate-and-fire (GIF) neuron model accounts for some of the most fundamental behaviours of neurons within a compact and extensible mathematical framework. Here, we introduce the main concepts behind the design of the GIF model in terms that will be familiar to electrophysiologists, and show why its simple design makes this model particularly well suited to mimicking behaviours observed in experimental data. Along the way, we will build an intuition for how specific neuronal behaviours, such as spike-frequency adaptation, or electrical properties, such as ionic currents, can be formulated mathematically and used to extend integrate-and-fire models to overcome their limitations. This chapter will provide readers with no previous exposure to modelling a clear understanding of the strengths and limitations of GIF models, along with the mathematical intuitions required to digest more detailed and technical treatments of this topic.


Assuntos
Modelos Neurológicos , Neurônios , Potenciais de Ação/fisiologia , Adaptação Fisiológica , Simulação por Computador , Neurônios/fisiologia
8.
Elife ; 112022 02 03.
Artigo em Inglês | MEDLINE | ID: mdl-35113017

RESUMO

The primary motor cortex (M1) is known to be a critical site for movement initiation and motor learning. Surprisingly, it has also been shown to possess reward-related activity, presumably to facilitate reward-based learning of new movements. However, whether reward-related signals are represented among different cell types in M1, and whether their response properties change after cue-reward conditioning remains unclear. Here, we performed longitudinal in vivo two-photon Ca2+ imaging to monitor the activity of different neuronal cell types in M1 while mice engaged in a classical conditioning task. Our results demonstrate that most of the major neuronal cell types in M1 showed robust but differential responses to both the conditioned cue stimulus (CS) and reward, and their response properties undergo cell-type-specific modifications after associative learning. PV-INs' responses became more reliable to the CS, while VIP-INs' responses became more reliable to reward. Pyramidal neurons only showed robust responses to novel reward, and they habituated to it after associative learning. Lastly, SOM-INs' responses emerged and became more reliable to both the CS and reward after conditioning. These observations suggest that cue- and reward-related signals are preferentially represented among different neuronal cell types in M1, and the distinct modifications they undergo during associative learning could be essential in triggering different aspects of local circuit reorganization in M1 during reward-based motor skill learning.


Assuntos
Aprendizagem/fisiologia , Córtex Motor/citologia , Córtex Motor/fisiologia , Animais , Feminino , Masculino , Camundongos , Neurônios/classificação , Neurônios/fisiologia
9.
Elife ; 112022 01 20.
Artigo em Inglês | MEDLINE | ID: mdl-35048910

RESUMO

Triggered activity bursts in place cells can increase and decrease the strength of some inputs.


Assuntos
Dendritos , Células de Lugar , Plasticidade Neuronal
10.
Neuroscience ; 489: 200-215, 2022 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-34358629

RESUMO

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.


Assuntos
Inteligência Artificial , Dendritos , Biofísica , Dendritos/fisiologia , Modelos Neurológicos , Redes Neurais de Computação , Neurônios/fisiologia
11.
PLoS Comput Biol ; 17(11): e1009478, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34748532

RESUMO

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.


Assuntos
Potenciais de Ação/fisiologia , Modelos Neurológicos , Rede Nervosa/fisiologia , Células Piramidais/fisiologia , Animais , Biologia Computacional , Simulação por Computador , Dendritos/fisiologia , Homeostase , Interneurônios/fisiologia , Rede Nervosa/citologia , Plasticidade Neuronal/fisiologia , Ratos
13.
Sci Rep ; 11(1): 15910, 2021 08 05.
Artigo em Inglês | MEDLINE | ID: mdl-34354118

RESUMO

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

14.
Nat Neurosci ; 24(7): 1010-1019, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33986551

RESUMO

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.


Assuntos
Aprendizado Profundo , Aprendizagem/fisiologia , Modelos Neurológicos , Plasticidade Neuronal/fisiologia , Células Piramidais/fisiologia , Animais , Humanos
15.
PLoS Comput Biol ; 17(3): e1008013, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33720935

RESUMO

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.


Assuntos
Modelos Lineares , Dinâmica não Linear , Sinapses/fisiologia , Transmissão Sináptica/fisiologia , Potenciais de Ação , Algoritmos , Funções Verossimilhança , Modelos Neurológicos , Rede Nervosa , Plasticidade Neuronal , Reprodutibilidade dos Testes , Processos Estocásticos
16.
Neuron ; 109(4): 571-575, 2021 02 17.
Artigo em Inglês | MEDLINE | ID: mdl-33600754

RESUMO

Recent research resolves the challenging problem of building biophysically plausible spiking neural models that are also capable of complex information processing. This advance creates new opportunities in neuroscience and neuromorphic engineering, which we discussed at an online focus meeting.


Assuntos
Engenharia Biomédica/tendências , Modelos Neurológicos , Redes Neurais de Computação , Neurociências/tendências , Engenharia Biomédica/métodos , Previsões , Humanos , Neurônios/fisiologia , Neurociências/métodos
17.
Science ; 370(6523)2020 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-33335033

RESUMO

Hippocampal output influences memory formation in the neocortex, but this process is poorly understood because the precise anatomical location and the underlying cellular mechanisms remain elusive. Here, we show that perirhinal input, predominantly to sensory cortical layer 1 (L1), controls hippocampal-dependent associative learning in rodents. This process was marked by the emergence of distinct firing responses in defined subpopulations of layer 5 (L5) pyramidal neurons whose tuft dendrites receive perirhinal inputs in L1. Learning correlated with burst firing and the enhancement of dendritic excitability, and it was suppressed by disruption of dendritic activity. Furthermore, bursts, but not regular spike trains, were sufficient to retrieve learned behavior. We conclude that hippocampal information arriving at L5 tuft dendrites in neocortical L1 mediates memory formation in the neocortex.


Assuntos
Dendritos/fisiologia , Hipocampo/fisiologia , Aprendizagem/fisiologia , Neocórtex/fisiologia , Córtex Perirrinal/fisiologia , Células Piramidais/fisiologia , Animais , Hipocampo/citologia , Masculino , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Neocórtex/citologia , Optogenética , Córtex Perirrinal/citologia , Ratos Wistar
18.
STAR Protoc ; 1(3): 100176, 2020 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-33377070

RESUMO

The proportion of silent (AMPAR-lacking) synapses is thought to be related to the plasticity potential of neural networks. We created a maximum-likelihood estimator of silent synapse fraction based on simulations of the underlying experimental methodology. Here, we provide a set of guidelines for running a Python package on compatible experimental synaptic data. Compared with traditional failure-rate approaches, this synthetic likelihood estimator improves the validity and accuracy of the estimates of the silent synapse fraction. For complete details on the use and execution of this protocol, please refer to Lynn et al. (2020).


Assuntos
Simulação por Computador , Fenômenos Eletrofisiológicos , Software , Sinapses/fisiologia , Funções Verossimilhança , Probabilidade
19.
Cell Rep ; 32(3): 107916, 2020 07 21.
Artigo em Inglês | MEDLINE | ID: mdl-32697998

RESUMO

Functional features of synaptic populations are typically inferred from random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we develop a biophysically constrained statistical framework to address this question and apply it to assess the performance of a widely used method based on a failure-rate analysis to quantify the occurrence of silent (AMPAR-lacking) synapses. We simulate this method in silico and find that it is characterized by strong and systematic biases, poor reliability, and weak statistical power. Key conclusions are validated by whole-cell recordings from hippocampal neurons. To address these shortcomings, we develop a simulator of the experimental protocol and use it to compute a synthetic likelihood. By maximizing the likelihood, we infer silent synapse fraction with no bias, low variance, and superior statistical power over alternatives. Together, this generalizable approach highlights how a simulator of experimental methodologies can substantially improve the estimation of physiological properties.


Assuntos
Sinapses/fisiologia , Animais , Região CA1 Hipocampal/fisiologia , Região CA3 Hipocampal/fisiologia , Simulação por Computador , Estimulação Elétrica , Fenômenos Eletrofisiológicos , Funções Verossimilhança , Masculino , Camundongos Endogâmicos C57BL
20.
J Math Neurosci ; 10(1): 6, 2020 Apr 20.
Artigo em Inglês | MEDLINE | ID: mdl-32314104

RESUMO

Following publication of the original article (Naud and Longtin in J Math Neurosci 9:3, 2019), the authors noticed a mistake in the first paragraph within "Altered propagation".

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