ArticleFrontiers in computational neuroscience2014
Differential effects of excitatory and inhibitory heterogeneity on the gain and asynchronous state of sparse cortical networks.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness.Neuroscience of consciousness · 2025Article
- Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.National science review · 2025Article
- Neural heterogeneity controls computations in spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Intrinsic neural diversity quenches the dynamic volatility of neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Dynamics and Information Import in Recurrent Neural Networks.Frontiers in computational neuroscience · 2022Article
- The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles.The European journal of neuroscience · 2021Review
- Optimal responsiveness and information flow in networks of heterogeneous neurons.Scientific reports · 2021Article
- Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework.Scientific reports · 2021Article
- Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder.Frontiers in psychiatry · 2020Article
- On the Validity of Neural Mass Models.Frontiers in computational neuroscience · 2020Article
- Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.Scientific reports · 2018Article
- Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.Scientific reports · 2018Article
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Abstract
Recent experimental and theoretical studies have highlighted the importance of cell-to-cell differences in the dynamics and functions of neural networks, such as in different types of neural coding or synchronization. It is still not known, however, how neural heterogeneity can affect cortical computations, or impact the dynamics of typical cortical circuits constituted of sparse excitatory and inhibitory networks. In this work, we analytically and numerically study the dynamics of a typical cortical circuit with a certain level of neural heterogeneity. Our circuit includes realistic features found in real cortical populations, such as network sparseness, excitatory, and inhibitory subpopulations of neurons, and different cell-to-cell heterogeneities for each type of population in the system. We find highly differentiated roles for heterogeneity, depending on the subpopulation in which it is found. In particular, while heterogeneity among excitatory neurons non-linearly increases the mean firing rate and linearizes the f-I curves, heterogeneity among inhibitory neurons may decrease the network activity level and induces divisive gain effects in the f-I curves of the excitatory cells, providing an effective gain control mechanism to influence information flow. In addition, we compute the conditions for stability of the network activity, finding that the synchronization onset is robust to inhibitory heterogeneity, but it shifts to lower input levels for higher excitatory heterogeneity. Finally, we provide an extension of recently reported heterogeneity-induced mechanisms for signal detection under rate coding, and we explore the validity of our findings when multiple sources of heterogeneity are present. These results allow for a detailed characterization of the role of neural heterogeneity in asynchronous cortical networks.
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