arXiv subjects

Mohammad Komareji

Publications and source records attributed to Mohammad Komareji.

4 recordsLinked to original sources

arXiv2025

Multilayer Perceptron Neural Network Model: A Novel Approach for LFP Contrast Sensitivity Tuning

Local field potentials (LFPs) have been demonstrated to be an important measurement to study the activity of a local population of neurons. The response tunings of LFPs have been mostly reported as weaker and broader than spike tunings. Therefore, selecting optimized tuning methods is essential for appropriately evaluating the LFP responses and comparing them with neighboring spiking activity. In this paper, new models for tuning of the contrast response functions (CRFs) are proposed. To this end, luminance contrast-evoked LFP responses recorded in primate primary visual cortex (V1) are first analyzed. Then, supersaturating CRFs are distinguished from linear and saturating CRFs by using monotonicity index (MI). The supersaturated recording data are then identified through static identification methods including multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, fuzzy model, neuro-fuzzy model, and the local linear model tree (LOLIMOT) algorithm. Our results demonstrate that the MLP neural network, compared to traditional and modified hyperbolic Naka-Rushton functions, exhibits superior performance in tuning the local field potential responses to luminance contrast stimuli, resulting in successful tuning of a significantly higher number of neural recordings of all three types. These results suggest that the MLP neural network model can be used as a novel approach to measure a better fitted contrast sensitivity tuning curve of a population of neurons than other currently used models.

eess.SP↗

arXiv2017

Swarming collapse under limited information flow between individuals

The emergence of collective decision in swarms and their coordinated response to complex environments underscore the central role played by social transmission of information. Here, the different possible origins of information flow bottlenecks are identified. Using a combination of network-, control- and information-theoretic elements applied to a group of interacting self-propelled particles, the effect of varying information capacity of the signaling channel on dynamic collective behaviors is revealed. We find a sufficient condition on the information data rate that guarantees the effectiveness of swarming while also highlighting the profound connection with the topology of the underlying interaction network. We also show that when decreasing the data rate, the swarming behavior invariably vanishes following a second-order phase transition irrespective of the intrinsic noise level. The variations along the transition line are found to be in good agreement with information-theoretic predictions.

nlin.AO↗

arXiv2014

Controllability of a swarm of topologically interacting autonomous agents

Controllability of complex networks has been the focal point of many recent studies in the field of complexity. These landmark advances shed a new light on the dynamics of natural and technological complex systems. Here, we analyze the controllability of a swarm of autonomous self-propelled agents having a topological neighborhood of interactions, applying the analytical tools developed for the study of the controllability of arbitrary complex directed networks. To this aim we thoroughly investigate the structural properties of the swarm signaling network which is the information transfer channel underpinning the dynamics of agents in the physical space. Our results show that with 6 or 7 topological neighbors, every agent not only affects, but is also affected by all other agents within the group. More importantly, still with 6 or 7 topological neighbors, each agent is capable of full control over all other agents. This finding is yet another argument justifying the particular value of the number of topological neighbors observed in field observations with flocks of starlings.

physics.bio-ph↗

arXiv2014

Resilience and Controllability of Dynamic Collective Behaviors

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics.

physics.soc-ph↗