Searching for cognitive specializations of neurons using mutual information framework
- Authors: Pospelov N.A.1, Sotskov V.P.1, Plusnin V.V.1, Rogozhnikova O.S.1, Toropova K.A.1, Ivashkina O.I.1, Anokhin K.V.1
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Affiliations:
- Institute for Advanced Brain Studies, Lomonosov Moscow State University
- Issue: Vol 18, No 4 (2023)
- Pages: 878-881
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623516
- DOI: https://doi.org/10.17816/gc623516
- ID: 623516
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Abstract
Of particular interest for researching the cognitive specializations of neurons is their correlation with environmental variables and animal behavior. Mutual information (MI) is a preferable method for measuring such correlations, as it allows for the assessment of non-linear relationships between variables, detects synchronization, and provides both significance and strength quantification. However, calculating MI for real data is significantly challenging. In this study, we used updated MI calculation techniques to analyze the connection between calcium fluorescence signaling and behavioral variables. Our approach encompasses novel strategies which we compiled into a software program known as INTENS (Information-Theoretic Evaluation of Neuronal Specializations), and it enabled to identify specialized neurons in mice hippocampal calcium activity data while they explored the arena with varying levels of novelty.
Numerous methods exist for analyzing the relationship between neuron spikes and behavioral variables, including information-theoretical approaches [1]. Extracting information about the relationship between calcium fluorescent signals and behavior is of particular interest due to the signal’s ability to provide crucial information about subthreshold activations of the neuron. In this study, we use the GCMI Gaussian copula entropy method to calculate mutual information [2]. This method relies on the fact that mutual information between two random variables is independent of their marginal distributions and only depends on the type of copula used (a multidimensional distribution where each marginal distribution is uniform).
The actual MI was compared to its corresponding values computed on the time-shifted signals for assessing the statistical significance of the computed information association between the calcium signal and the behavioral variable. Additionally, we devised a technique for gauging the strength of the coupling effect. This involved normalizing the mutual information between the fluorescence signal and the behavior with the entropy value of both variables, previously calculated as random variables. Importantly, the approach outlined earlier is effective for analyzing continuous variables such as calcium signal and animal speed, as well as pairs of continuous and discrete variables such as calcium signal and the presence or absence of grooming.
The analysis of calcium signals recorded from the CA1 region of the hippocampus revealed neuronal specializations related to the animal’s external environment, such as place cells, and specializations related to its behavioral activities, including neurons activated during running, rearing, and freezing. Some neurons selectively activated in response to discrete parameters included the animal’s location within the arena (center, walls, and corners) and its speed (rest, slow, and fast). A total of 781 specializations were detected across 472 neurons throughout all four sessions of the experiment. Notably, a single neuron could have several specializations. However, more than half (55%) of the neurons were found to have only one specialization.
Full Text
Of particular interest for researching the cognitive specializations of neurons is their correlation with environmental variables and animal behavior. Mutual information (MI) is a preferable method for measuring such correlations, as it allows for the assessment of non-linear relationships between variables, detects synchronization, and provides both significance and strength quantification. However, calculating MI for real data is significantly challenging. In this study, we used updated MI calculation techniques to analyze the connection between calcium fluorescence signaling and behavioral variables. Our approach encompasses novel strategies which we compiled into a software program known as INTENS (Information-Theoretic Evaluation of Neuronal Specializations), and it enabled to identify specialized neurons in mice hippocampal calcium activity data while they explored the arena with varying levels of novelty.
Numerous methods exist for analyzing the relationship between neuron spikes and behavioral variables, including information-theoretical approaches [1]. Extracting information about the relationship between calcium fluorescent signals and behavior is of particular interest due to the signal’s ability to provide crucial information about subthreshold activations of the neuron. In this study, we use the GCMI Gaussian copula entropy method to calculate mutual information [2]. This method relies on the fact that mutual information between two random variables is independent of their marginal distributions and only depends on the type of copula used (a multidimensional distribution where each marginal distribution is uniform).
The actual MI was compared to its corresponding values computed on the time-shifted signals for assessing the statistical significance of the computed information association between the calcium signal and the behavioral variable. Additionally, we devised a technique for gauging the strength of the coupling effect. This involved normalizing the mutual information between the fluorescence signal and the behavior with the entropy value of both variables, previously calculated as random variables. Importantly, the approach outlined earlier is effective for analyzing continuous variables such as calcium signal and animal speed, as well as pairs of continuous and discrete variables such as calcium signal and the presence or absence of grooming.
The analysis of calcium signals recorded from the CA1 region of the hippocampus revealed neuronal specializations related to the animal’s external environment, such as place cells, and specializations related to its behavioral activities, including neurons activated during running, rearing, and freezing. Some neurons selectively activated in response to discrete parameters included the animal’s location within the arena (center, walls, and corners) and its speed (rest, slow, and fast). A total of 781 specializations were detected across 472 neurons throughout all four sessions of the experiment. Notably, a single neuron could have several specializations. However, more than half (55%) of the neurons were found to have only one specialization.
ADDITIONAL INFORMATION
Authors’ contribution. All authors made a substantial contribution to the conception of the work, acquisition, analysis, interpretation of data for the work, drafting and revising the work, final approval of the version to be published and agree to be accountable for all aspects of the work.
Funding sources. The research was supported by the Non-Commercial Foundation for Support of Science and Education “INTELLECT”.
Competing interests. The authors declare that they have no competing interests.
About the authors
N. A. Pospelov
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Author for correspondence.
Email: nik-pos@yandex.ru
Russian Federation, Moscow
V. P. Sotskov
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Email: nik-pos@yandex.ru
Russian Federation, Moscow
V. V. Plusnin
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Email: nik-pos@yandex.ru
Russian Federation, Moscow
O. S. Rogozhnikova
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Email: nik-pos@yandex.ru
Russian Federation, Moscow
K. A. Toropova
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Email: nik-pos@yandex.ru
Russian Federation, Moscow
O. I. Ivashkina
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Email: nik-pos@yandex.ru
Russian Federation, Moscow
K. V. Anokhin
Institute for Advanced Brain Studies, Lomonosov Moscow State University
Email: nik-pos@yandex.ru
Russian Federation, Moscow
References
- Strong SP, de Ruyter van Steveninck RR, Bialek W, Koberle R. On the application of information theory to neural spike trains. Pac Symp Biocomput. 1998;621–632.
- Ince RA, Giordano BL, Kayser C, et al. A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Hum Brain Mapp. 2017;38(3):1541–1573. doi: 10.1002/hbm.23471
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