Voltage-Gated Intrinsic Conductances Shape the Input-Output Relationship of Cortical Neurons in Behaving Primate V1

Li, Baowang; Routh, Brandy N.; Johnston, Daniel; Seidemann, Eyal; Priebe, Nicholas J.

doi:10.1016/j.neuron.2020.04.001

Cited by 16 publications

(33 citation statements)

References 61 publications

(80 reference statements)

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“…A larger synaptic input could also naturally result from an overall increase in neuronal firing too, since increased neuronal excitability would result in increase in neurotransmitter release. This agrees with reports that reveal that synaptic inputs can be enhanced by intrinsic membrane mechanisms (Hu et al, 2002; Li et al, 2020; Manabe et al, 1992; Marder and Goaillard, 2006; Narayanan and Johnston, 2008; Turrigiano et al, 1998). For all these reasons, we suspect synaptic plasticity can be explained by the effects of the stimulation protocols in the ready releasable pool and intrinsic changes in excitability in the stimulated neurons and the network.…”

Section: Discussionsupporting

confidence: 93%

“…Changes in intrinsic properties are known to modulate EPSP amplitude, for example, by the modification of membrane conductance induced by postsynaptic stimulation (Hu et al, 2002; Li et al, 2020; Manabe et al, 1992; Marder and Goaillard, 2006; Narayanan and Johnston, 2008; Turrigiano et al, 1998). These changes might be due to modification in intrinsic membrane conductances (Li et al, 2020; Manabe et al, 1992; Narayanan and Johnston, 2008), and they could amplify synaptic inputs and improve the accuracy of synaptic integration of neurons (Desai et al, 1999; Mahon and Charpier, 2012; Malik and Chattarji, 2012; Sjöström et al, 2008; Turrigiano et al, 1994).…”

Section: Discussionmentioning

confidence: 99%

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Intrinsic excitability mechanisms of neuronal ensemble formation

Alejandre-García

Kim

Pérez-Ortega

et al. 2020

Preprint

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Neuronal ensembles are coactive groups of cortical neurons, found in both spontaneous and evoked activity, which can mediate perception and behavior (Cossart et al., 2003; Buzsáki, 2010; Carrillo-Reid et al., 2019; Marshel et al., 2019). To understand the mechanism that lead to the formation of neuronal ensembles, we generated optogenetically artificial photo-ensembles in layer 2/3 pyramidal neurons in brain slices of mouse visual cortex from both sexes, replicating an optogenetic protocol to generate ensembles in vivo by simultaneous coactivation of neurons (Carrillo-Reid et al. 2016). Using whole-cell voltage-clamp recordings from individual neurons and connected pairs, we find that synaptic properties of photostimulated were surprisingly unaffected, without any signs of Hebbian plasticity. However, extracellular recordings revealed that photostimulation induced strong increases in spontaneous action potential activity. Using perforated patch clamp recordings, we find increases in neuronal excitability, accompanied by increases in membrane resistance and a reduction in spike threshold. We conclude that the formation of neuronal ensemble by photostimulation is mediated by cell-intrinsic changes in excitability, rather than by Hebbian synaptic plasticity or changes in local synaptic connectivity. We propose an “iceberg” model, by which increased neuronal excitability makes subthreshold connections become suprathreshold, increasing the functional effect of already existing synapses and generating a new neuronal ensemble.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Intrinsic excitability mechanisms of neuronal ensemble formation

Alejandre-García

Kim

Pérez-Ortega

et al. 2020

Preprint

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“…The effective time constants of the principal cells in our simulations ranged from 6 to 60 ms, which is within a reasonable range for in vivo cortical neurons, but the values of the intrinsic time constants (1 to 2 ms) were ∼10 times shorter than experimental measurements of intrinsic time constants (88,89). Increasing the values of the time constant parameters would make the responses sluggish and decrease the oscillation frequencies (Fig.…”

Section: Discussionsupporting

confidence: 52%

A recurrent circuit implements normalization, simulating the dynamics of V1 activity

Heeger

Zemlianova

2020

Proc. Natl. Acad. Sci. U.S.A.

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The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model’s defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. Despite the success of the normalization model, there are three unresolved issues. 1) Experimental evidence supports the hypothesis that normalization in V1 operates via recurrent amplification, i.e., amplifying weak inputs more than strong inputs. It is unknown how normalization arises from recurrent amplification. 2) Experiments have demonstrated that normalization is weighted such that each weight specifies how one neuron contributes to another’s normalization pool. It is unknown how weighted normalization arises from a recurrent circuit. 3) Neural activity in V1 exhibits complex dynamics, including gamma oscillations, linked to normalization. It is unknown how these dynamics emerge from normalization. Here, a family of recurrent circuit models is reported, each of which comprises coupled neural integrators to implement normalization via recurrent amplification with arbitrary normalization weights, some of which can recapitulate key experimental observations of the dynamics of neural activity in V1.

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“…Fig. 6), inputs within this region generate firing rates that are in the experimentally observed range in cortical networks [23,[35][36][37][38][39][40][41][42][43]. The analysis of SI VI also shows that, when τ S /τ ∼ 1, i.e.…”

Section: Resultsmentioning

confidence: 82%

“…However, these results have all been derived assuming that the firing of a presynaptic neuron produces a fixed amount of synaptic current, hence neglecting the dependence of synaptic current on the membrane potential, a key aspect of neuronal biophysics. Large synaptic conductances has been shown to have major effects on the stationary [11] and dynamical [12] response of single cells, and form the basis of the 'high-conductance state' [13][14][15][16][17][18][19] that has been argued to describe well in vivo data [20][21][22] (but see [23] and Discussion). At the network level, conductance modulation plays a role in controlling signal propagation [24], input summation [25], and firing statistics [26].…”

mentioning

confidence: 99%

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

Sanzeni¹,

Histed²,

Brunel³

2020

Preprint

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Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with currentbased synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order 1/ √ K. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synapses are of order 1/ log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a driftdiffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.

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Voltage-Gated Intrinsic Conductances Shape the Input-Output Relationship of Cortical Neurons in Behaving Primate V1

Cited by 16 publications

References 61 publications

Intrinsic excitability mechanisms of neuronal ensemble formation

Intrinsic excitability mechanisms of neuronal ensemble formation

A recurrent circuit implements normalization, simulating the dynamics of V1 activity

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

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