Towards the neural population doctrine

The supplementary motor area (SMA) is believed to contribute to higher-order aspects of motor control.To examine this contribution, we employed a novel cycling task and leveraged an emerging strategy: testing whether population trajectories possess properties necessary for a hypothesized class of computations. We found that, at the single-neuron level, SMA exhibited multiple response features absent in M1. We hypothesized that these diverse features might contribute, at the population level, to avoidance of 'population trajectory divergence' -ensuring that two trajectories never followed the same path before separating. Trajectory divergence was indeed avoided in SMA but not in M1. Network simulations confirmed that low trajectory divergence is necessary when guidance of future action depends upon internally tracking contextual factors. Furthermore, the empirical trajectory geometryhelical in SMA versus elliptical in M1 -was naturally reproduced by networks that did, versus did not, internally track context. 2Relative to primary motor cortex (M1), SMA activity is less coupled to actions of a specific body part 10-12 .3 Instead, SMA computations appear related to learned sensory-motor associations 11 , reward 4 anticipation 13 , internal initiation and guidance of movement 3,14 , movement timing 15,16 , and movement 5 sequencing 7,17,18 . Single-neuron responses in SMA reflect a variety of task-specific contingencies. For 6 example, in a sequence of three movements, a neuron may burst only when pulling precedes pushing. 7Another neuron might respond before the third movement regardless of the particular sequence 19 . 8 Different response features are observed in different tasks. Single SMA neurons exhibit a mixture of 9 ramping and rhythmic activity during an interval timing task 20 , and the SMA population exhibits 10 amplitude-modulated circular trajectories during rhythmic tapping 21 . A common thread linking prior 11 studies is that SMA computations are hypothesized to be critical when pending action depends upon 12 internal, abstract, and/or contextual factors. An important challenge is linking these high-level ideas to 13 network-level implementations. What general properties should activity exhibit in networks performing 14 the hypothesized class of computations? 15There exist many quantitative methods for relating population activity and computation (e.g., [22][23][24] ). 16These include decoding key hypothesized signals (e.g., via regression 25 ), or directly comparing empirical 17 and simulated population activity (e.g., via canonical correlation 26 ). An emerging strategy is to consider 18 the geometry of the population response: the arrangement of population states across conditions 23,27,28 19 and/or the shape traced by activity in neural state-space 15,26,[29][30][31][32][33][34][35][36] . A given geometry may be consistent 20 with some types of computation but not others 37 . An advantage of this approach is that it is sometimes 21 possible to measure geometric properties that are expected to hold f...

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“…There exist many quantitative methods for relating population activity and computation (e.g., [22][23][24] ).…”

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confidence: 99%

Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation

Russo

Khajeh

Bittner

et al. 2019

Preprint

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“…Recent experimental work has shown information in the brain is represented in a distributed manner, across the neural population (Saxena and Cunningham, 2019;Yuste, 2015). These population responses are thought to reside on a low dimensional manifold (Cunningham and Yu, 2014), with the position of neural activity on this manifold representing the value of a cognitive variable (e.g.…”

Section: Scientific Applications Of Aclsmentioning

confidence: 99%

Learning to Control the Brain through Adaptive Closed-Loop Patterned Stimulation

Tafazoli

MacDowell

Che

et al. 2020

Preprint

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Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed an adaptive, closed-loop stimulation (ACLS) system that uses patterned, multi-site electrical stimulation to control the pattern of activity in a population of neurons. Importantly, ACLS is a learning system; it monitors the response to stimulation and iteratively updates the stimulation pattern to produce a specific neural response. In silico and in vivo experiments showed ACLS quickly learns to produce specific patterns of neural activity (~15 minutes) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Altogether, our results show ACLS can learn, in real-time, to generate specific patterns of neural activity, providing a framework for using closed-loop learning to control neural activity.

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“…So far, we have only considered firing rates and excitatory-inhibitory balance averaged over discrete neural populations. Cortical circuits implement distributed neural representations that are not always captured by homogeneous population averages [33]. Balance is realized at the level of synaptic currents to individual neurons (as opposed to currents averaged over populations) is often referred to as "detailed balance" [34,25].…”

Section: Homeostatic Plasticity Achieves Semi-balance At Single Neuromentioning

confidence: 99%

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance

Baker

Zhu

Rosenbaum

2019

Preprint

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Balanced excitation and inhibition is widely observed in cortical recordings. How does this balance shape neural computations and stimulus representations? This problem is often studied using computational models of neuronal networks in a dynamically balanced state. However, these balanced network models predict a linear relationship between stimuli and population responses, in contrast to the nonlinearity of cortical computations. We show that every balanced network architecture admits some stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state is unavoidable in networks driven by multiple stimuli, is more consistent with experimental data, has a direct mathematical relationship to artificial neural networks, and permits nonlinear stimulus representations and nonlinear computations.

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Towards the neural population doctrine

Cited by 253 publications

References 61 publications

Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation

Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation

Learning to Control the Brain through Adaptive Closed-Loop Patterned Stimulation

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance

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