The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

Giugliano, Michèle; Camera, Giancarlo La; Fusi, Stefano; Senn, Walter

doi:10.1007/s00422-008-0270-9

Cited by 26 publications

(26 citation statements)

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“…8), adaptation removes the singularity and transforms the response function in thresholdlinear (Ermentrout 1998). Firing rate adaptation also plays a variety of roles in the response to time-varying input current (reviewed in Giugliano et al 2008). The theory developed so far is extended in this section to include the effect of firing rate adaptation.…”

Section: Firing Rate Adaptationmentioning

confidence: 97%

“…A similar approximation can be used in the presence of short-term (Tsodyks and Markram 1997;Tsodyks et al 1998;Mongillo et al 2008) or long-term synaptic plasticity (Del Giudice et al 2003;Amit and Mongillo 2003). For faster inputs a different approach is required and is reviewed in Giugliano et al (2008).…”

Section: Network Response To Time-varying Inputsmentioning

confidence: 98%

“…The same approach can be extended to the case of time-varying statistics of the input current, with occasional modifications customized to work for the relevant time-scale under consideration. Some of these extensions are reviewed in the companion paper (Giugliano et al 2008), whereas in this article we consider the response of cortical neurons in a regime of stationary or quasi-stationary activity. In the next subsections, we consider its quantitative development in the framework of networks of IF neurons.…”

Section: Neuronal Mean Field Approachmentioning

confidence: 99%

“…The network dynamics can be studied in the framework of mean field theory also when the input statistics are not stationary. Some of these extensions are reviewed in Giugliano et al (2008); here we mention briefly a few applications of the stationary response function to the characterization of the transient behavior of the network and its response to time-varying inputs.…”

Section: Network Response To Time-varying Inputsmentioning

confidence: 99%

“…A generalization to the case of more realistic conductance-based inputs, and its comparison with the current-based approximation, is presented at a theoretical level only, and limited to the case of stationary inputs. The extension to the case of time-varying statistics, responses on longer time-scales and to the nonlinearities due to backpropagating action potentials are addressed in a companion article (Giugliano et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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The response of cortical neurons to in vivo-like input current: theory and experiment

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The study of several aspects of the collective dynamics of interacting neurons can be highly simplified if one assumes that the statistics of the synaptic input is the same for a large population of similarly behaving neurons (mean field approach). In particular, under such an assumption, it is possible to determine and study all the equilibrium points of the network dynamics when the neuronal response to noisy, in vivo-like, synaptic currents is known. The response function can be computed analytically for simple integrate-and-fire neuron models and it can be measured directly in experiments in vitro. Here we review theoretical and experimental results about the neural response to noisy inputs with stationary statistics. These response functions are important to characterize the collective neural dynamics that are proposed to be the neural substrate of working memory, decision making and other cognitive functions. Applications to the case of time-varying inputs are reviewed in a companion paper (Giugliano et al. in Biol Cybern, 2008). We conclude that modified integrate-and-fire neuron models are good enough to reproduce faithfully many of the relevant dynamical aspects of the neuronal response measured in experiments on real neurons in vitro.

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Section: Firing Rate Adaptationmentioning

confidence: 97%

Section: Network Response To Time-varying Inputsmentioning

confidence: 98%

Section: Neuronal Mean Field Approachmentioning

confidence: 99%

Section: Network Response To Time-varying Inputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The response of cortical neurons to in vivo-like input current: theory and experiment

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Special issue on quantitative neuron modeling

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Single neurons are the fundamental constituents of all nervous systems. Understanding the properties and dynamics of single neurons is therefore one of the key challenges in modern neuroscience. Since the work of Lapicque (1907) [see also the recent translation by Brunel and van Rossum (2007)], single neuron models have enjoyed great popularity and have been the subject of many theoretical studies. Two broad categories of spiking neuron models have been extensively studied and used: Hodgkin-Huxley-type neuron models and simplified phenomenological neuron models, of which the integrate-and-fire model is the most famous representative. Each model type presents its own set of advantages and drawbacks. The Hodgkin-Huxley framework permits the design of detailed, biophysically realistic models, which can be used to study the effects of different types of ion channels including their distribution along dendrites. However, these models are computationally expensive and their analysis is usually difficult. Simple phenomenological neuron models, on the other hand, are analytically tractable and computationally cheap, but their realism is questionable. Both types of models have been used in countless studies of single neurons and network models, with the ultimate aim of understanding how information processing in single neurons and neural circuits gives rise to behaviour.Despite the tremendous popularity both types of models enjoy, the question of their quantitative accuracy in reproducing experimentally measured neuronal dynamics has been barely addressed until recently. It was implicitly assumed that Hodgkin-Huxley-type neuron models are the most realistic, while simplified phenomenological neuron models were overlooked in that regard but extensively used because of their simplicity. However, are either of these models able to predict the rate and timing of output spikes of a real neuron, given arbitrary patterns of synaptic input? Can they correctly predict the result of the interactions between dendrites and soma? And can they quantitatively predict the effect of various intrinsic neuronal mechanisms, for example those underlying adaptation or bursting? With recent initiatives such as the Blue Brain Project (Markram 2006) or the recent large-scale model by Izhikevich and Edelman (2008), it is now time to try to answer these questions. In order to provide the community with a benchmark to address these questions, we set up an international scholarly challenge on quantitative single neuron modeling 1 . The present Special Issue comprises a selection of papers that address precisely these questions, and extensively describe the challenge: can Hodgkin-Huxley-type neuron models or simplified phenomenological neuron models predict the activity of real neurons? What is the minimal level of description required to achieve a reasonable accuracy of predictions made by 1

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Cortical computations via metastable activity

Camera

Fontanini

Mazzucato

2019

Current Opinion in Neurobiology

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Metastable brain dynamics are characterized by abrupt, jump-like modulations so that the neural activity in single trials appears to unfold as a sequence of discrete, quasi-stationary 'states.' Evidence that cortical neural activity unfolds as a sequence of metastable states is accumulating at fast pace. Metastable activity occurs both in response to an external stimulus and during ongoing, self-generated activity. These spontaneous metastable states are increasingly found to subserve internal representations that are not locked to external triggers, including states of deliberations, attention and expectation. Moreover, decoding stimuli or decisions via metastable states can be carried out trial-by-trial. Focusing on metastability will allow us to shift our perspective on neural coding from traditional concepts based on trial-averaging to models based on dynamic ensemble representations. Recent theoretical work has started to characterize the mechanistic origin and potential roles of metastable representations. In this article we review recent findings on metastable activity, how it may arise in biologically realistic models, and its potential role for representing internal states as well as relevant task variables. Highlights• Metastable activity is increasingly being observed in many cortical areas and in a variety of tasks.• Metastable activity is correlated with sensory and cognitive processes and may subserve state-dependent computations.• Metastable activity can emerge spontaneously in spiking networks with clustered architecture.• Metastable activity is a candidate neural substrate for coding both external events and internal representations.

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The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

Cited by 26 publications

References 90 publications

The response of cortical neurons to in vivo-like input current: theory and experiment

The response of cortical neurons to in vivo-like input current: theory and experiment

Special issue on quantitative neuron modeling

Cortical computations via metastable activity

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