Noise correlations improve response fidelity and stimulus encoding

Cafaro, Jon; Rieke, Fred

doi:10.1038/nature09570

Cited by 119 publications

(114 citation statements)

References 32 publications

(43 reference statements)

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“…The balance of excitatory and inhibitory membrane currents a neuron experiences during stimulated and ongoing activity has been the topic of many recent studies (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). This balance, first defined as equal average amounts of de-and hyperpolarizing membrane currents (from hereon referred to as "global balance") is thought to be essential for maintaining stability of cortical networks (1, 2).…”

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confidence: 99%

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Vogels

Sprekeler

Zenke

et al. 2011

Science

659

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."A Hebbian form of synaptic plasticity at inhibitory synapses generates balanced input currents and sparse neuronal responses that stabilize memory traces in neuronal networks" Cortical neurons receive balanced excitatory and inhibitory membrane currents.Here, we show that such a balance can be established and maintained in an experiencedependent manner by synaptic plasticity at inhibitory synapses. The mechanism we put forward provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. We show that the introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state, but can be re-activated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry. 1The balance of excitatory and inhibitory membrane currents a neuron experiences during stimulated and ongoing activity has been the topic of many recent studies (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). This balance, first defined as equal average amounts of de-and hyperpolarizing membrane currents (from hereon referred to as "global balance") is thought to be essential for maintaining stability of cortical networks (1, 2). In the balanced state networks display asynchronous irregular (AI) dynamics that mimic activity patterns observed in cortical neurons. Such asynchronous network states facilitate rapid responses to small changes in the input (2-4), providing an ideal substrate for cortical signal processing (5,15,16). Pathologies that disrupt the balance of excitation and inhibition have often been implicated in neurological diseases such as epilepsy or schizophrenia (17, 18).Moreover, the input currents to a given cortical neuron are not merely globally balanced. Excitatory and inhibitory inputs are coupled also in time (6-8) and co-tuned for different stimulus features (9,10). The tight coupling of excitation and inhibition suggests a more precise, detailed balance, in which each excitatory input arrives at the cell together with an inhibitory counterpart, supposedly supplied through feedforward inhibition (Fig. 1 A). These observations fit well with models of cortical processing in which balanced sensory inputs are left unattended, but can be transiently (11), or persistently turned on by targeted disruptions of the balance (12-14).Although it is widely thought that the excitatory-inhibitory balance plays an important role for stability and information processing in cortical networks, it is still not understood by which mechanisms this balance is established and maintained in the presence of ongoing sensory experiences. Inspired by recent experimental results (9), we investigate the hypothesis that synaptic plastic...

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confidence: 99%

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Vogels

Sprekeler

Zenke

et al. 2011

Science

659

1,046

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“…The technique of in vivo intracellular recording holds the potential to shed a great deal of light on the biophysics of neural computation, and in particular on the dynamic balance between excitation and inhibition underlying sensory information processing (BorgGraham et al 1996;Peña and Konishi 2000;Anderson et al 2000;Wehr and Zador 2003;Priebe and Ferster 2005;Murphy and Rieke 2006;Wang et al 2007;Xie et al 2007;Cafaro and Rieke 2010).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Pospischil et al (2007) fit average conductance quantities given a long observed voltage trace, while a number of other papers (e.g., Borg-Graham et al 1996;Wehr and Zador 2003;Priebe and Ferster 2005;Murphy and Rieke 2006) rely on voltage-clamping the cell at a number of different holding potentials and then averaging over a few trials in order to infer the average timecourse of synaptic inputs given a single repeated stimulus. Two important exceptions are Wang et al (2007), where the excitatory retinogeniculate input conductances are large and distinct enough to be inferred via direct thresholding techniques, and Cafaro and Rieke (2010), where the stimulus changed slowly enough that an alternating voltageclamp and current sub-sampling strategy allowed for effectively simultaneous measurements of excitatory and inhibitory conductances.…”

Section: Introductionmentioning

confidence: 99%

Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods

et al. 2011

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We discuss methods for optimally inferring the synaptic inputs to an electrotonically compact neuron, given intracellular voltage-clamp or current-clamp recordings from the postsynaptic cell. These methods are based on sequential Monte Carlo techniques ("particle filtering"). We demonstrate, on model data, that these methods can recover the time course of excitatory and inhibitory synaptic inputs accurately on a single trial. Depending on the observation noise level, no averaging over multiple trials may be required. However, excitatory inputs are consistently inferred more accurately than inhibitory inputs at physiological resting potentials, due to the stronger driving force associated with excitatory conductances. Once these synaptic input time courses are recovered, it becomes possible to fit (via tractable convex optimization techniques) models describing the relationship between the sensory stimulus and the observed synaptic input. We develop both parametric and nonparametric expectation-maximization (EM) algorithms that consist of alternating iterations between these synaptic recovery and model estimation steps. We employ a fast, robust convex optimization-based method to effectively initialize the filter; these fast methods may be of independent interest. The proposed methods could be applied to better understand the balance between excitation and inhibition in sensory processing in vivo.

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“…Finally, neurons sharing presynaptic partners exhibit correlations in their synaptic input. While network models typically assume sparse connectivity for which correlations are negligible, recent reports suggest important functional roles for this type of "noise" correlation [14,15]. For IF neurons, obtaining the input-output relationship essentially involves computing moments of the first-passage-time (FPT) to threshold, but analytical solutions are rarely possible.…”

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Firing statistics and correlations in spiking neurons: A level-crossing approach

Badel

2011

Phys. Rev. E

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We present a time-dependent level-crossing theory for linear dynamical systems perturbed by colored Gaussian noise. We apply these results to approximate the firing statistics of conductancebased integrate-and-fire neurons receiving excitatory and inhibitory Poissonian inputs. Analytical expressions are obtained for three key quantities characterizing the neuronal response to time-varying inputs: the mean firing rate, the linear response to sinusoidally-modulated inputs, and the pairwise spike-correlation for neurons receiving correlated inputs. The theory yields tractable results that are shown to accurately match numerical simulations, and provides useful tools for the analysis of interconnected neuronal populations.PACS numbers: 05.10. Gg,87.19.lj,87.19.ll,87.19.lm Understanding the dynamics of interconnected networks of neurons is of fundamental importance in theoretical neuroscience. An essential step in solving this problem is to determine the input-output relationship of individual neurons given an underlying biophysical model. For white-noise-driven integrate-and-fire (IF) neurons this problem is generally tractable (e.g., [1][2][3][4]), but efforts to integrate key aspects of neuronal signaling into the IF formalism have added to its complexity. First, synaptic input consists of discrete action potentials, which results in non-Gaussian voltage distributions and affects firing statistics [5][6][7][8]. Second, synaptic communication is mediated by two separate (excitatory and inhibitory) systems with distinct kinetics. The majority of theoretical studies include only one synaptic type and assume either fast (e.g., [9-11]) or slow [11,12] kinetics compared with the membrane integration time, but experimental evidence suggests that these timescales are often comparable [13]. Finally, neurons sharing presynaptic partners exhibit correlations in their synaptic input. While network models typically assume sparse connectivity for which correlations are negligible, recent reports suggest important functional roles for this type of "noise" correlation [14,15]. For IF neurons, obtaining the input-output relationship essentially involves computing moments of the first-passage-time (FPT) to threshold, but analytical solutions are rarely possible. Here, we show that for membrane-to-synaptic time constant ratios of the order of unity, level-crossing statistics provide good approximations to the FPT while retaining sufficient tractability to incorporate additional biological detail. Analytical expressions are given for the mean firing rate, pairwise spike-correlation and linear response to sinusoidally-modulated inputs, which compare favorably with numerical simulations of conductance-based IF neurons. The method thus provides a complete set of input- * Present address: Laboratory for Circuit Mechanisms of Sensory Perception, RIKEN Brain Science Institute, Wako, Saitama, Japan output properties needed for an analysis at the network level. Although the focus of the present paper is on neuroscience applications, the ...

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Noise correlations improve response fidelity and stimulus encoding

Cited by 119 publications

References 32 publications

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods

Firing statistics and correlations in spiking neurons: A level-crossing approach

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