Neural constraints on learning

Sadtler, Patrick T.; Quick, Kristin M.; Golub, Matthew D.; Chase, Steven M.; Ryu, Stephen I.; Tyler-Kabara, Elizabeth C.; Yu, Byron M.; Batista, Aaron P.

doi:10.1038/nature13665

Cited by 599 publications

(875 citation statements)

References 40 publications

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“…ALM responses to perturbations resembles robust systems, where critical state variables are particularly stiff 26 . Selective stability of neural dynamics supports the idea that behaviorrelated activity comprises only a low-dimensional subspace of neural activity space 33,40 , constrained by the structure of neural circuits 43 . Our findings place constraints on the circuit architectures that underlie memory-related cortical activity and suggest general principles of robust system control in the brain.…”

Section: Discussionmentioning

confidence: 58%

Robust neuronal dynamics in premotor cortex during motor planning

Li¹,

Daie²,

Svoboda³

et al. 2016

Nature

445

485

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Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in mouse premotor cortex to probe robustness of persistent neural representations during motor planning. Preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

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Section: Discussionmentioning

confidence: 58%

Robust neuronal dynamics in premotor cortex during motor planning

Li¹,

Daie²,

Svoboda³

et al. 2016

Nature

445

485

View full text Add to dashboard Cite

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“…Future studies using large ensembles of simultaneously recorded neurons and single-trial analyses can inform these issues (28,29). Simultaneous recordings could also test for transient dynamics that are not locked to task timing, as well as test theoretical model predictions for correlated fluctuations within specific coding subspaces (30).…”

Section: Discussionmentioning

confidence: 99%

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.

328

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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“…(Afshar et al 2011; Harvey, Coen, and Tank 2012; Kaufman et al 2014;Sadtler et al 2014;Kobak et al 2016a) ]. Recovering these dynamics on single trials is essential for illuminating the relationship between neural population activity and behavior, and for advancing therapeutic neurotechnologies such as closed-loop deep brain stimulation and brain-machine interfaces.…”

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confidence: 99%

Inferring single-trial neural population dynamics using sequential auto-encoders

Pandarinath

O’Shea

Collins

et al. 2017

Preprint

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411

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Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract 'de-noised' single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural population's activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.Increasing evidence suggests that in many brain areas, such as the motor and prefrontal cortices, the activity of large populations of neurons, termed the neural population state, is often well-described by low-dimensional dynamics [e.g. (Afshar et al. 2011; Harvey, Coen, and Tank 2012; Kaufman et al. 2014;Sadtler et al. 2014;Kobak et al. 2016a) ]. Recovering these dynamics on single trials is essential for illuminating the relationship between neural population activity and behavior, and for advancing therapeutic neurotechnologies such as closed-loop deep brain stimulation and brain-machine interfaces. However, recovering population dynamics All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/152884 doi: bioRxiv preprint first posted online Jun. 20, 2017; on single trials is difficult due to trial-to-trial variability (e.g. in behavior or arousal) and fluctuations in the spiking of individual neurons. Even with dramatic increases in the numbers of neurons that can be simultaneously recorded using multichannel electrode arrays or optical imaging, accurately recovering population dynamics from single trials remains a significant challenge for data-analysis methods.Standard analyses sacrifice single-trial information for the sake of better estimates of trial-averaged neural states (Ahrens et al. 2012; Kobak et al. 2016b) . Techniques for extrac...

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Neural constraints on learning

Cited by 599 publications

References 40 publications

Robust neuronal dynamics in premotor cortex during motor planning

Robust neuronal dynamics in premotor cortex during motor planning

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

Inferring single-trial neural population dynamics using sequential auto-encoders

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