Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons

Meshulam, Leenoy; Gauthier, Jeffrey; Brody, Carlos D.; Tank, David W.; Bialek, William

doi:10.1103/physrevlett.123.178103

Cited by 91 publications

(204 citation statements)

References 89 publications

Supporting

Mentioning

182

Contrasting

Order By: Relevance

“…Similarly, here we show that the observations of Ref. [14], including scaling properties of the free energy, the cluster covariance, the cluster autocorrelations, and the flow of the cluster activity distribution to a non-Gaussian fixed point can be explained, within experimental error, by a model of non-interacting neurons coupled to latent dynamical fields. This is the first model to explain such a variety of spatio-temporal scaling phenomena observed in large-scale biological data.…”

supporting

confidence: 80%

“…A promising resolution to the problem is to adapt the Renormalization Group (RG) [12] framework for coarse-graining systems in statistical physics to find relevant features and large-scale behaviors in biological data sets as well. Indeed, recently, RG-inspired coarse-graining showed an emergence of nontrivial scaling behaviors in neural populations [13,14]. Specifically, the authors analyzed the activity of over 1000 neurons in the mouse hippocampus as the animal repeatedly ran through a virtual maze.…”

mentioning

confidence: 99%

“…Below we introduce the model, implement the coarsegraining of Ref. [14] on data generated from it and compare our findings with experimental results. We conclude by discussing which other experimental systems may exhibit similar scaling relations under the RG procedure.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Morrell

Sederberg

Nemenman

2020

Preprint

View full text Add to dashboard Cite

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse-graining had been previously applied to experimental neural recordings, which showed over two decades of scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model the experiment by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally-observed scalings, suggesting that they may arise from coupling the neural population activity to latent dynamic stimuli. Further, parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to non-place stimuli.

show abstract

supporting

confidence: 80%

mentioning

confidence: 99%

See 1 more Smart Citation

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Morrell

Sederberg

Nemenman

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Importantly, with today's technology, the spiking activity of more than 1000 neurons can be measured simultaneously (see e.g. [52]), so that much better statistics can be collected. In principle, one should be able to compute the Jensen's force in this type of experiments.…”

Section: Experimental Measurements Of the Lai Phase And The Jensen'smentioning

confidence: 99%

Jensen’s force and the statistical mechanics of cortical asynchronous states

Buendía

Villegas

Santo

et al. 2019

Sci Rep

View full text Add to dashboard Cite

Cortical networks are shaped by the combined action of excitatory and inhibitory interactions. Among other important functions, inhibition solves the problem of the all-or-none type of response that comes about in purely excitatory networks, allowing the network to operate in regimes of moderate or low activity, between quiescent and saturated regimes. Here, we elucidate a noise-induced effect that we call “Jensen’s force” –stemming from the combined effect of excitation/inhibition balance and network sparsity– which is responsible for generating a phase of self-sustained low activity in excitation-inhibition networks. The uncovered phase reproduces the main empirically-observed features of cortical networks in the so-called asynchronous state, characterized by low, un-correlated and highly-irregular activity. The parsimonious model analyzed here allows us to resolve a number of long-standing issues, such as proving that activity can be self-sustained even in the complete absence of external stimuli or driving. The simplicity of our approach allows for a deep understanding of asynchronous states and of the phase transitions to other standard phases it exhibits, opening the door to reconcile, asynchronous-state and critical-state hypotheses, putting them within a unified framework. We argue that Jensen’s forces are measurable experimentally and might be relevant in contexts beyond neuroscience.

show abstract

“…The motivation is clear: to make sense of neural activity, we must first make sense of the behavior that activity produces (Carandini, 2012). Despite the 420 current excitement surrounding new neural analysis techniques (Williams et al, 2020;Duncker and Sahani, 2018;Meshulam et al, 2019;Semedo et al, 2019;Kobak et al, 2016;Gao et al, 2016), however, analysis of the behavior facilitating those techniques has received comparatively little attention. The standard suite of behavioral analysis tools is ill-equipped to capture dynamic and complex behavioral strategies.…”

Section: Discussionmentioning

confidence: 99%

Extracting the Dynamics of Behavior in Decision-Making Experiments

Roy

Bak

Akrami

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding how animals update their decision-making behavior over time is an important problem in neuroscience. Decision-making strategies evolve over the course of learning, and continue to vary even in well-trained animals. However, the standard suite of behavioral analysis tools is ill-equipped to capture the dynamics of these strategies. Here, we present a flexible method for characterizing time-varying behavior during decision-making experiments. We show that it successfully captures trial-to-trial changes in an animal's sensitivity to not only task-relevant stimuli, but also task-irrelevant covariates such as choice, reward, and stimulus history. We use this method to derive insights from training data collected in mice, rats, and human subjects performing auditory discrimination and visual detection tasks. With this approach, we uncover the detailed evolution of an animal's strategy during learning, including adaptation to time-varying task statistics, suppression of sub-optimal strategies, and shared behavioral dynamics between subjects within an experimental population. 45 et al., 2009). Other work from Kattner et al. (2017) extended the standard psychometric curve to allow its parameters to vary continuously across trials. Bak et al. (2016) described a model for defining smoothly evolving weights that could track changing sensitivities to specific behavioral covariates. While the model could track behavioral dynamics in theory, the optimization procedure strongly constrained both the complexity of the model and the size of the data to which it could 50 65 unprecedented insight into the development of behavioral strategies. ResultsOur primary contribution is a method for characterizing the evolution of animal decision-making behavior on a trial-to-trial basis. Our approach consists of a dynamic Bernoulli generalized linear model (GLM), defined by a set of smoothly evolving psychophysical weights. These weights 70 characterize the animal's decision-making strategy at each trial in terms of a linear combination of available task variables. The larger the magnitude of a particular weight, the more the animal's decision relies on the corresponding task variable. Learning to perform a new task therefore involves driving the weights on "relevant" variables (e.g., sensory stimuli) to large values, while driving weights on irrelevant variables (e.g., bias, choice history) toward zero. However, classical 75 modeling approaches require that weights remain constant over long blocks of trials, which precludes tracking of trial-to-trial behavioral changes that arise during learning and in non-stationary task environments. Below, we describe our modeling approach in more detail. Dynamic Psychophysical Model for Decision-Making TasksAlthough our method is applicable to any binary decision-making task, for concreteness we intro-80 duce our method in the context of the task used by the International Brain Lab (IBL) (illustrated in Figure 1A) (IBL et al., 2020). In this visual detection task, a mouse is positioned in f...

show abstract

Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons

Cited by 91 publications

References 89 publications

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Jensen’s force and the statistical mechanics of cortical asynchronous states

Extracting the Dynamics of Behavior in Decision-Making Experiments

Contact Info

Product

Resources

About