Robust neuronal dynamics in premotor cortex during motor planning

Li, Nuo; Daie, Kayvon; Svoboda, Karel; Druckmann, Shaul

doi:10.1038/nature17643

Cited by 422 publications

(530 citation statements)

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“…Druckmann and Chklovskii (11) found that stable subspace models can incorporate neurobiological constraints such as sparse connectivity and that unsupervised Hebbian learning of recurrent connections can produce a stable coding subspace. Our empirical findings are in line with this theoretical framework and suggest that WM activity in PFC may be supported by such stablesubspace network mechanisms (27). Another direction for future circuit modeling is to compare empirical population data to activity in trained recurrent neural networks, which can lie at an intermediate stage of random and structured connectivity (10).…”

Section: Discussionsupporting

confidence: 66%

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

Murray

Bernacchia

Roy

et al. 2016

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

312

455

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Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.working memory | prefrontal cortex | population coding T he neuronal basis of working memory (WM) in prefrontal cortex (PFC) has been studied for decades through singleneuron recordings from monkeys performing tasks in which a transient sensory stimulus must be held in WM across a secondslong delay to guide a future response. These studies discovered that a key neural correlate of WM in PFC is stimulus-selective persistent activity, i.e., stable elevated firing rates in a subset of neurons, that spans the delay (1). These neurophysiological findings have grounded a leading hypothesis that WM is supported by stable persistent activity patterns in PFC that bridge the gap between stimulus and response epochs. Because the timescales of WM maintenance (several seconds) are longer than typical timescales of neuronal and synaptic integration (∼10-100 ms), mechanisms at the level of neural circuits may be critical for generating WM activity in PFC (2). A leading theoretical framework proposes that PFC circuits subserve WM maintenance through dynamical attractors, i.e., stable fixed points in network activity, generated by strong recurrent connectivity (3, 4).Recent neurophysiologi...

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Section: Discussionsupporting

confidence: 66%

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

Murray

Bernacchia

Roy

et al. 2016

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

312

455

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“…To causally test the role of delay period activity in WM, recent work has focused on single-trial analyses of neural activity and behavior and on the impact of optogenetic perturbations on the behavioral reports at the end of the delay period; as argued by Panzeri et al (2017), these two techniques provide powerful tests of the causal role of neural activities in driving behavior. This work shows that, even over trials in which the animal had to remember the same presented stimulus, differences in the delay period activities are predictive of subsequent differences in the behavior (Kopec et al 2015, Li et al 2016, Vergara et al 2016, Wimmer et al 2014). Moreover, optogenetic perturbations to the neural activities during the delay period cause predictable changes in the subsequent behavior (Kopec et al 2015, Li et al 2016, Liu et al 2014).…”

Section: Overviewmentioning

confidence: 73%

“…Recent work has expanded to rodents, in which genetic tools allow neural activities to be manipulated as well as recorded. That work has focused on rodent homologs of the primate brain areas involved in memory and movement planning, including the parietal cortex, PFC, anterior lateral motor cortex (ALM), frontal orienting fields (FOF), and superior colliculus (SC) (Harvey et al 2012, Kopec et al 2015, Li et al 2016, Liu et al 2014). In rodents, elevated delay period firing is observed as in the monkey, but relatively few cells are active for the entire delay period.…”

Section: Overviewmentioning

confidence: 99%

Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory

Zylberberg

Strowbridge

2017

Annu. Rev. Neurosci.

162

165

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A commonly observed neural correlate of working memory is firing that persists after the triggering stimulus disappears. Substantial effort has been devoted to understanding the many potential mechanisms that may underlie memory-associated persistent activity. These rely either on the intrinsic properties of individual neurons or on the connectivity within neural circuits to maintain the persistent activity. Nevertheless, it remains unclear which mechanisms are at play in the many brain areas involved in working memory. Herein, we first summarize the palette of different mechanisms that can generate persistent activity. We then discuss recent work that asks which mechanisms underlie persistent activity in different brain areas. Finally, we discuss future studies that might tackle this question further. Our goal is to bridge between the communities of researchers who study either single-neuron biophysical, or neural circuit, mechanisms that can generate the persistent activity that underlies working memory.

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“…The mouse is increasingly used as a model system for investigating the cortex, where complex sensory (Ferezou et al, 2007), motor (Li et al, 2016) and cognitive (Carandini and Churchland, 2013;Kim et al, 2016;Manita et al, 2015) functions have been shown to depend on interactions among cortical areas via inter-areal connections, as well as on dynamic control involving higher-order thalamic nuclei (Mease et al, 2016;Sherman, 2016). The highly interactive nature of cortical processing has motivated recent efforts to investigate the statistical properties of inter-areal networks and the development of large-scale models of the cortex that may provide insights into brain function in health and disease (Bullmore and Sporns, 2012).…”

Section: Introductionmentioning

confidence: 99%

The Mouse Cortical Interareal Network Reveals Well Defined Connectivity Profiles and an Ultra Dense Cortical Graph

Gămănuţ

Kennedy

Toroczkai

et al. 2017

Preprint

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The inter-areal wiring pattern of mouse cerebral cortex was analyzed in relation to an accurate parcellation of cortical areas. Twenty-seven retrograde tracer injections were made in 19 areas of a 41 area (plus 7 sub-area) parcellation of the mouse neo-, parahippocampal and perirhinal cortex. Flat mounts of the cortex and multiple histological markers enabled detailed counts of labeled neurons in individual areas. A weight index was determined for each area-to-area pathway based on the Fraction of Extrinsically Labeled Neurons (FLNe). Data analysis allowed cross species comparison with the macaque. Estimation of FLNe statistical variability based on repeat injections revealed high consistency across individuals and justifies using a single injection per area to characterize connectivity. The observed lognormal distribution of connections to each cortical area spanned 5 orders of magnitude and revealed a distinct connectivity profile for each area, analogous to that observed in macaque. The resulting graph has a density of 97% (i.e. 97% of connections that can exist do exist), considerably higher than the 66% density reported for the macaque. Our results provide more sharply defined connectivity profiles and a markedly higher graph density than shown in a recent probabilistic mouse connectome.All rights reserved. No reuse allowed without permission.

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Robust neuronal dynamics in premotor cortex during motor planning

Cited by 422 publications

References 50 publications

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory

The Mouse Cortical Interareal Network Reveals Well Defined Connectivity Profiles and an Ultra Dense Cortical Graph

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