Search · four archives
Search · four archives
15 papers · ranked by Valyu relevance
Chih-Hsu Huang, Chou-Ching K. Lin
Nowadays, building low-dimensional mean-field models of neuronal populations is still a critical issue in the computational neuroscience community, because their derivation is difficult for realistic networks of neurons with conductance-based interactions and spike-frequency adaptation that generate nonlinear…
Richard Gast, Daniel Rose, Harald E. Möller, Nikolaus Weiskopf + 1 more
In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different…
Szilvia Szeier, Henrik Jörntell
Behavior ultimately depends on the spatiotemporal patterns of the neuron population activity across the brain. Here we address the issue of how the evolving patterns of population activity can be governed by the intrinsically available mechanisms within the brain. We show how the control of the evolving activity can be…
He Chen, Jun Kunimatsu, Tomomichi Oya, Yuri Imaizumi + 5 more
Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron…
Marc de Kamps, Mikkel Lepperød, Yi Ming Lai
The importance of a mesoscopic description level of the brain has now been well established. Rate based models are widely used, but have limitations. Recently, several extremely efficient population-level methods have been proposed that go beyond the characterization of a population in terms of a single variable. Here…
Michael Okun, Nicholas A. Steinmetz, Armin Lak, Martynas Dervinis + 1 more
Cortical activity is organised across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms − 1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the…
Omer Revah, Fred Wolf, Michael J. Gutnick, Andreas Neef
Sixty years after the concept of population coding in neuronal networks was introduced, we still lack a comprehensive understanding of its performance limits and the role of neuronal physiology. Here, we use dynamic gain analysis in a general model of population coding and demonstrate that disparate parameters of…
Maximilian Puelma Touzel, Fred Wolf
The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporate this feature and…
Yann Zerlaut∗, Sandrine Chemla, Frederic Chavane, Alain Destexhe∗
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field…
Chenfei Zhang, Omer Revah, Fred Wolf, Andreas Neef
The information processing capabilities of large neuronal circuits form the basis of higher-level cognition. It is unknown how the constituent cells’ properties interact with the external stimuli to shape the network’s collective activity patterns and the associated computations. The dynamic gain function, a spectrally…
M. Carlu, O. Chehab, L. Dalla Porta, D. Depannemaecker + 9 more
We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We…
Ahana Gangopadhyay, Darshit Mehta, Shantanu Chakrabartty
In neuromorphic engineering, neural populations are generally modeled in a bottom-up manner, where individual neuron models are connected through synapses to form large-scale spiking networks. Alternatively, a top-down approach treats the process of spike generation and neural representation of excitation in the…
P. Tewarie, A. Daffertshofer, B.W. van Dijk
Neural mass models are accepted as efficient modelling techniques to model empirical observations such as disturbed oscillations or neuronal synchronization. Neural mass models are based on the mean-field assumption, i.e. they capture the mean-activity of a neuronal population. However, it is unclear if neural mass…
S. Luccioli, A. Barzilai, E. Ben-Jacob, P. Bonifazi + 1 more
We consider a sparse random network of excitatory leaky integrate-andfire neurons with short-term synaptic depression. Furthermore to mimic the dynamics of a brain circuit in its first stages of development we introduce for each neuron correlations among in-degree and out-degree as well as among excitability and the…
Chenfei Zhang, David Hofmann, Andreas Neef, Fred Wolf
Populations of cortical neurons respond to common input within a millisecond. Morphological features and active ion channel properties were suggested to contribute to this astonishing processing speed. Here we report an exhaustive study of ultrafast population coding for varying axon initial segment (AIS) location…