What can neuronal populations tell us about cognition?

Arandia-Romero, Iñigo; Nogueira, Ramon; Mochol, Gabriela; Moreno-Bote, Rubén

doi:10.1016/j.conb.2017.07.008

Cited by 9 publications

(12 citation statements)

References 98 publications

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“…Identifying the statistical features of neuronal population responses that affect the amount of encoded information and be-havioral performance is critical for understanding neuronal population coding (Arandia-Romero et al, 2017;Panzeri et al, 2017). Changes in network states, such as global modulations of activity (Harris and Thiele, 2011;Luczak et al, 2013;Gutnisky et al, 2017), as well as changes in correlated noise among neurons, have been shown to constrain the amount of information encoded by neuronal populations (Zohary et al, 1994;Ecker et al, 2014;Lin et al, 2015;Schölvinck et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior

et al. 2019

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Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and shared response variability, and global activity modulations, could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions (population signal) and the inverse population covariability along the modulation axis (projected precision). We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions consistently across 4 monkeys (1 female and 1 male Macaca mulatta; and 2 male Macaca fascicularis). In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. We also show that differential correlations reduce the amount of information encoded in finite populations by reducing projected precision. Our results are consistent with predictions of a model that optimally decodes population responses to produce behavior.

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Section: Introductionmentioning

confidence: 99%

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior

et al. 2019

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“…Identifying the statistical features of neuronal population responses that affect the amount of encoded information and behavioral performance is critical for understanding neuronal population coding (Arandia-Romero et al 2017;Panzeri et al 2017). The modulation in mean firing rate of individual neurons with respect to a stimulus parameter is a statistical feature that has been typically taken as evidence for encoding information about that stimulus (Hubel and Wiesel 1959;Mountcastle et al 1967).…”

Section: Introductionmentioning

confidence: 99%

“…However, the aspects of neuronal responses that most directly affect the amount of encoded information are not clear, since experimental designs often do not allow control over other statistical features that could potentially be involved. Furthermore, it is unknown whether the same features of population responses that affect the amount of encoded information also impact behavioral performance (Arandia-Romero et al 2017;Panzeri et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Identifying the most influential features of neural population responses for information encoding and behavior

Nogueira

Anzai

et al. 2019

Preprint

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Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding the neural code. Statistical features such as tuning properties, individual and shared response variability, and global activity modulations could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions and the projection of the inverse population variability along the modulation axis. We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions. In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance.Our results are consistent with predictions of a model that optimally decodes population responses, which suggests that in our behavioral tasks the readout of information is near-optimal.

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“…For encoding, generalized linear models (GLMs), a generalization of multiple linear regression, regress neuronal activity against behavioral variables to determine the set of variables that explain more neuronal activity (Aljadeff et al, 2016;Nogueira et al, 2017). Decoding techniques, typically linear classifiers (Arandia-Romero, Nogueira, Mochol, & Moreno-Bote, 2017;Quian Quiroga & Panzeri, 2009), as well as more recent artificial neural networks (ANNs; Paninski & Cunningham, 2018) are used to predict, trial-by-trial, values of behavioral variables from neuronal activity, either using single neuronal activity or the individual activity of large neuronal populations recorded from multielectrode-arrays or Ca 2+ imaging. These methods are supervised machine learning tools because both behavioral and neuronal variables are preselected and labeled.…”

Section: Statistical Tools For Understanding Datamentioning

confidence: 99%

A roadmap to integrate astrocytes into Systems Neuroscience

Kastanenka

Moreno-Bote

Pittà

et al. 2019

Glia

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Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well‐documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub‐serve coding and higher‐brain functions. First, we reviewed Systems‐like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte–neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy‐efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.

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What can neuronal populations tell us about cognition?

Cited by 9 publications

References 98 publications

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior

Identifying the most influential features of neural population responses for information encoding and behavior

A roadmap to integrate astrocytes into Systems Neuroscience

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