Statistical methods for dissecting interactions between brain areas

Semedo, João D.; Gokcen, Evren; Machens, Christian K.; Kohn, Adam; Yu, Byron M.

doi:10.1016/j.conb.2020.09.009

Cited by 62 publications

(68 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the visual system, neurons in each area send projections to a variety of different sensory, association, and motor areas, and only a small proportion of neuronal population activity is shared between even highly connected brain areas (Semedo et al, 2019). Recent work used correlative methods to identify a functional 'communication subspace', which consists of the dimensions of neuronal population space in which trial-to-trial variability is shared between areas (Semedo et al, 2019(Semedo et al, , 2020. We similarly adopt the term 'communication' to refer to functional communication (i.e., shared trial-to-trial variability in responses to the same visual stimulus).…”

Section: Introductionmentioning

confidence: 99%

Attention improves information flow between neuronal populations without changing the communication subspace

Srinath

Ruff

Cohen

2021

Preprint

View full text Add to dashboard Cite

Visual attention allows observers to flexibly use or ignore visual information, suggesting that information can be flexibly routed between visual cortex and neurons involved in decision-making. We investigated the neural substrate of flexible information routing by analyzing the activity of populations of visual neurons in the medial temporal area (MT) and oculomotor neurons in the superior colliculus (SC) while rhesus monkeys switched spatial attention. We demonstrated that attention increases the efficacy of visuomotor communication: trial-to-trial variability of the population of SC neurons was better predicted by the activity of MT neurons (and vice versa) when attention was directed toward their joint receptive fields. Surprisingly, this improvement in prediction was not explained or accompanied by changes in the dimensionality of the shared subspace or in local or shared pairwise noise correlations. These results suggest a mechanism by which visual attention can affect perceptual decision-making without altering local neuronal representations.

show abstract

Section: Introductionmentioning

confidence: 99%

Attention improves information flow between neuronal populations without changing the communication subspace

Srinath

Ruff

Cohen

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In particular, it is important to ensure that apparent changes in inter-areal interactions do not arise solely from changes in the structure of activity within each area (see ref. 53, in press).…”

mentioning

confidence: 99%

“…We previously reported that the interaction between V1 and V2 was low dimensional (termed a communication subspace) using Reduced-Rank Regression (RRR)29 . RRR is closely related to Canonical Correlation Analysis (CCA), which we employed in this work (for a review, see ref 53,. in press).…”

mentioning

confidence: 99%

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

Semedo

Jasper

Zandvakili

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Brain function relies on the coordination of activity across multiple, recurrently connected, brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", such that feedback signaling does not directly affect activity that is fed forward.

show abstract

“…Although axonal projections may not be recorded between areas, dynamical systems models using recordings from both areas 1 and 2 can provide insight into the information communicated between areas. In particular, B 1-to-2 can be thought of as a communication subspace (CS) that selectively extracts features of x 1 to propagate to x 2 , summarizing the role of inter-area axonal projections 11,12,28 . The CS may not be aligned with neural dimensions of highest variance (such as the principal components) but may instead communicate activity along low variance dimensions that are necessary for downstream computation.…”

Section: Models Of Large Scale Brain-wide Neural Population Dynamicsmentioning

confidence: 99%

“…LDSs and nonlinear dynamical systems models 5,6 , paired with measurements from populations of individual neurons from one or more brain areas [7][8][9][10][11][12][13][14] , have produced new insights into the putative computational functions being performed 1 . Here we discuss emerging opportunities to expand dynamical systems insights into brain function.…”

mentioning

confidence: 99%

Measurement, manipulation and modeling of brain-wide neural population dynamics

Shenoy

Kao

2021

Nat Commun

View full text Add to dashboard Cite

Neural recording technologies increasingly enable simultaneous measurement of neural activity from multiple brain areas. To gain insight into distributed neural computations, a commensurate advance in experimental and analytical methods is necessary. We discuss two opportunities towards this end: the manipulation and modeling of neural population dynamics. Neural circuits comprise networks of individual neurons that perform sensory, cognitive, and motor functions. Neuronal biophysics, together with these circuits, give rise to neural population dynamics, which express how the activity of the neural population evolves through time in principled ways. Neural population dynamics provide a framework for understanding neural computation. Prior studies have modeled neural population dynamics to gain insight into computations involved in decision-making, timing, and motor control 1. Here, we present emerging opportunities for new experiments and analyses that use a dynamical systems framework to better understand brain circuits, how they interact, and how they relate to behavior. The simplest model of neural population dynamics is a linear dynamical system (LDS). An LDS (Fig. 1a) is described by a dynamics equation (x(t + 1) = Ax(t) + Bu(t)) and an observation equation (y(t) = Cx(t) + d). Typically, y(t) reflects experimental measurements, such as a vector where each element is the number of action potentials fired by a neuron in a brief time bin (e.g., 10 ms). The vector x(t) is a "neural population state" that captures information in y(t). This neural population state can be thought of as a representation of the dominant activity patterns in the experimental neural recordings. Typically, x(t) is an abstract representation in a lowdimensional subspace (or manifold) found via dimensionality reduction 2 (Fig. 1b, neural state), reflecting that the neural activity is correlated and the dominant patterns can be described by a relatively small number of variables. The neural population state can also represent the activity of each neuron in the original dimensionality of the measured data (e.g., 100D if 100 neurons). The observation equation relates the observed action potentials (y(t)) to the neural population state (x(t)) through an observation matrix (C). The vector, d, is a constant offset (e.g., to model baseline firing). The neural population state moves through neural state space, constituting a neural population trajectory. The dynamics equation expresses how the neural population state (x(t)) progresses through time as a function of a dynamics matrix (A), an input matrix (B) and

show abstract

Statistical methods for dissecting interactions between brain areas

Cited by 62 publications

References 66 publications

Attention improves information flow between neuronal populations without changing the communication subspace

Attention improves information flow between neuronal populations without changing the communication subspace

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

Measurement, manipulation and modeling of brain-wide neural population dynamics

Contact Info

Product

Resources

About