Rapid homeostatic plasticity of excitation-inhibition balance in thalamocortical microcircuits
Rapid homeostatic plasticity of excitation-inhibition balance in thalamocortical microcircuits
批准号:
9467780
负责人:
Brian Joseph Lane
金额:
$6.04万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31
关键词:
AffectAnimalsAutistic DisorderAxonBrainCellsChronicCircadian RhythmsDataDevelopmentDisinhibitionEnsureEpilepsyEquilibriumExcitatory SynapseFaceFeedbackFinancial compensationFunctional disorderHomeostasisHourImplanted ElectrodesIn VitroIndividualInterneuronsMeasurementMethodsMonitorNeuronsPathologyPathway interactionsPatternPhysiologyProcessPropertyRegulationResearchSchizophreniaSensorySensory DeprivationSliceSynapsesTestingThalamic structureTimeVisualVisual CortexWithdrawalarea striatacell typecomputer studiesdesigner receptors exclusively activated by designer drugsexperienceexperimental studyhippocampal pyramidal neuronin vivomonocularneocorticalneural circuitoptogeneticssensory systemvisual deprivation
中文摘要
大脑中神经元之间的兴奋性和抑制性连接通过
经验。为了避免过度或过度兴奋,稳态过程确保神经元的放电率是
在扰动后返回适当的设置点。保存适当神经元的一个重要方法
放电频率是通过回路内兴奋-抑制平衡(E/I平衡)的动态平衡调节来实现的。
在体内只发现了缓慢的细胞自主的动态平衡过程,但研究表明,更快的
面对Hebbian的快速变化,补偿机制是必要的,以保持稳定的发射
可塑性。理论和实验证据表明,E/I动态平衡对于
网络维持稳定,但这一过程从未在体内得到证明。
丘脑皮质(TC)轴突将视觉感觉信息发送到初级视皮层,主要
终止于新皮质第四层(L4)。TC轴突直接兴奋星状锥体(SP)神经元和PV+快速放电
(FS)中间神经元。然后,FS中间神经元对SP神经元提供双突触前馈抑制。
来自特里加诺实验室的初步数据表明,丘脑皮质前馈E/I比率与SP
神经元是动态维持的。有趣的是,L4 SP神经元的平均放电频率(几分钟到几小时)
视觉皮质在不同的环境或昼夜节律状态下没有什么不同。我假设SP神经元放电
通过调整激励强度以匹配前馈强度来动态稳定速率
对不同程度的感觉驱动的抑制。我将使用两个范例在体内测试这一点:i)
单眼视觉剥夺(MD)和ii)使用DREADD调制PV+FS的放电频率。对于每一个
这些范例,我将使用慢性电极植入记录活体L4中FS和SP神经元的放电率,
并准备皮质切片,以探测每种突触类型的突触属性的变化。
我希望这些实验能阐明神经元放电频率的基本原理。
在体内受到调节。如果成功,这将使我们对神经元动态平衡的理解超越缓慢。
细胞自主过程,揭示了稳定神经元的快速、潜在的网络级机制
在较短的时间尺度上的射击率。
英文摘要
The excitatory and inhibitory connections between neurons in the brain are continually refined through
experience. To avoid hyper- or hypo excitability, homeostatic processes ensure that neuronal firing rates are
returned an appropriate set-point after perturbation. One important way to preserve appropriate neuronal
firing rates is through homeostatic regulation of the excitation-inhibition balance (E/I balance) within a circuit.
Only slow cell-autonomous homeostatic processes been identified in vivo, but studies suggest that more rapid
compensatory mechanisms are necessary to maintain stable firing in the face of rapid changes from Hebbian
plasticity. Theoretical and experimental evidence suggests that dynamic balancing of E/I is necessary for
networks to maintain stability, but this process has never been demonstrated in vivo.
Thalamocortical (TC) axons send visual sensory information to the primary visual cortex, which mainly
terminate in neocortical layer 4 (L4). TC axons directly excite star pyramidal (SP) neurons and PV+ fast-spiking
(FS) interneurons. FS interneurons then provide disynaptic feedforward inhibition onto SP neurons.
Preliminary data from the Turrigiano lab indicates that the thalamocortical feedforward E/I ratio to SP
neurons is dynamically maintained. Interestingly, average firing rates (minutes to hours) of L4 SP neurons in
visual cortex are not different across environmental or circadian states. I hypothesize that SP neuron firing
rates are dynamically stabilized by scaling the strength of excitation to match the strength of feedforward
inhibition across vastly different levels of sensory drive. I will test this in vivo using two paradigms: i)
monocular visual deprivation (MD) and ii) modulation of PV+ FS firing rates using DREADDs. For each of
these paradigms, I will record firing rates of FS and SP neurons in L4 in vivo using chronic electrode implants,
and prepare cortical slices to probe for changes in synaptic properties at each synapse type.
I expect that these experiments will elucidate basic principles about how neuronal firing rates are
regulated in vivo. If successful, this would extend our understanding of neuronal homeostasis beyond slow
cell-autonomous processes and reveal rapid, potentially network-level mechanisms that stabilize neuronal
firing rates on shorter timescales.
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