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Barta T, Kostal L. Shared input and recurrency in neural networks for metabolically efficient information transmission. PLoS Comput Biol 2024; 20:e1011896. [PMID: 38394341 PMCID: PMC10917264 DOI: 10.1371/journal.pcbi.1011896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 03/06/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.
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Affiliation(s)
- Tomas Barta
- Laboratory of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Lubomir Kostal
- Laboratory of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
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Jabarin R, Dagash W, Netser S, Singh S, Paul BK, Barkai E, Wagner S. Modulation of social investigation by anterior hypothalamic nucleus rhythmic neural activity. iScience 2022; 26:105921. [PMID: 36685037 PMCID: PMC9852926 DOI: 10.1016/j.isci.2022.105921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/17/2022] [Accepted: 12/29/2022] [Indexed: 12/31/2022] Open
Abstract
Social interactions involve both approach and avoidance toward specific individuals. Currently, the brain regions subserving these behaviors are not fully recognized. The anterior hypothalamic nucleus (AHN) is a poorly defined brain area, and recent studies have yielded contradicting conclusions regarding its behavioral role. Here we explored the role of AHN neuronal activity in regulating approach and avoidance actions during social interactions. Using electrophysiological recordings from behaving mice, we revealed that theta rhythmicity in the AHN is enhanced during affiliative interactions, but decreases during aversive ones. Moreover, the spiking activity of AHN neurons increased during the investigation of social stimuli, as compared to objects, and was modulated by theta rhythmicity. Finally, AHN optogenetic stimulation during social interactions augmented the approach toward stimuli associated with the stimulation. These results suggest the role for AHN neural activity in regulating approach behavior during social interactions, and for theta rhythmicity in mediating the valence of social stimuli.
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Affiliation(s)
- Renad Jabarin
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel
| | - Wael Dagash
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel
| | - Shai Netser
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel
| | - Shelly Singh
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel
| | - Blesson K. Paul
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel
| | - Edi Barkai
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel
| | - Shlomo Wagner
- Sagol Department of Neurobiology, the Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Mt. Carmel, Haifa 3498838, Israel,Corresponding author
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Tian Y, Sun P. Percolation may explain efficiency, robustness, and economy of the brain. Netw Neurosci 2022; 6:765-790. [PMID: 36605416 PMCID: PMC9810365 DOI: 10.1162/netn_a_00246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 03/11/2022] [Indexed: 01/09/2023] Open
Abstract
The brain consists of billions of neurons connected by ultra-dense synapses, showing remarkable efficiency, robust flexibility, and economy in information processing. It is generally believed that these advantageous properties are rooted in brain connectivity; however, direct evidence remains absent owing to technical limitations or theoretical vacancy. This research explores the origins of these properties in the largest yet brain connectome of the fruit fly. We reveal that functional connectivity formation in the brain can be explained by a percolation process controlled by synaptic excitation-inhibition (E/I) balance. By increasing the E/I balance gradually, we discover the emergence of these properties as byproducts of percolation transition when the E/I balance arrives at 3:7. As the E/I balance keeps increase, an optimal E/I balance 1:1 is unveiled to ensure these three properties simultaneously, consistent with previous in vitro experimental predictions. Once the E/I balance reaches over 3:2, an intrinsic limitation of these properties determined by static (anatomical) brain connectivity can be observed. Our work demonstrates that percolation, a universal characterization of critical phenomena and phase transitions, may serve as a window toward understanding the emergence of various brain properties.
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Affiliation(s)
- Yang Tian
- Department of Psychology and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China,Laboratory of Advanced Computing and Storage, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd., Beijing, China,* Corresponding Author: ;
| | - Pei Sun
- Department of Psychology and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China,* Corresponding Author: ;
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Endo D, Kobayashi R, Bartolo R, Averbeck BB, Sugase-Miyamoto Y, Hayashi K, Kawano K, Richmond BJ, Shinomoto S. A convolutional neural network for estimating synaptic connectivity from spike trains. Sci Rep 2021; 11:12087. [PMID: 34103546 PMCID: PMC8187444 DOI: 10.1038/s41598-021-91244-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/21/2021] [Indexed: 02/05/2023] Open
Abstract
The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.
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Affiliation(s)
- Daisuke Endo
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan
| | - Ryota Kobayashi
- Mathematics and Informatics Center, The University of Tokyo, Tokyo, 113-8656, Japan
- Department of Complexity Science and Engineering, The University of Tokyo, Chiba, 277-8561, Japan
- JST, PRESTO, Saitama, 332-0012, Japan
| | - Ramon Bartolo
- Laboratory of Neuropsychology, NIMH/NIH/DHHS, Bethesda, MD, 20814, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, NIMH/NIH/DHHS, Bethesda, MD, 20814, USA
| | - Yasuko Sugase-Miyamoto
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8568, Japan
| | - Kazuko Hayashi
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8568, Japan
- Japan Society for the Promotion of Science, Tokyo, 102-0083, Japan
| | - Kenji Kawano
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8568, Japan
| | - Barry J Richmond
- Laboratory of Neuropsychology, NIMH/NIH/DHHS, Bethesda, MD, 20814, USA
| | - Shigeru Shinomoto
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
- Brain Information Communication Research Laboratory Group, ATR Institute International, Kyoto, 619-0288, Japan.
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Barta T, Kostal L. Regular spiking in high-conductance states: The essential role of inhibition. Phys Rev E 2021; 103:022408. [PMID: 33736083 DOI: 10.1103/physreve.103.022408] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 02/03/2021] [Indexed: 06/12/2023]
Abstract
Strong inhibitory input to neurons, which occurs in balanced states of neural networks, increases synaptic current fluctuations. This has led to the assumption that inhibition contributes to the high spike-firing irregularity observed in vivo. We used single compartment neuronal models with time-correlated (due to synaptic filtering) and state-dependent (due to reversal potentials) input to demonstrate that inhibitory input acts to decrease membrane potential fluctuations, a result that cannot be achieved with simplified neural input models. To clarify the effects on spike-firing regularity, we used models with different spike-firing adaptation mechanisms, and we observed that the addition of inhibition increased firing regularity in models with dynamic firing thresholds and decreased firing regularity if spike-firing adaptation was implemented through ionic currents or not at all. This fluctuation-stabilization mechanism provides an alternative perspective on the importance of strong inhibitory inputs observed in balanced states of neural networks, and it highlights the key roles of biologically plausible inputs and specific adaptation mechanisms in neuronal modeling.
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Affiliation(s)
- Tomas Barta
- Institute of Physiology of the Czech Academy of Sciences, 14220 Prague, Czech Republic; Charles University, First Medical Faculty, 12108 Prague, Czech Republic; and Institute of Ecology and Environmental Sciences, INRAE, 78026 Versailles, France
| | - Lubomir Kostal
- Institute of Physiology of the Czech Academy of Sciences, 14220 Prague, Czech Republic
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