Browsing by Subject "Simulations"
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(2021)Effective population coding is dependent on connectivity, active and passive postsynaptic membrane parameters but how it relates to information transfer and information representation in the brain is still poorly understood. Recently, Brendel et al. (2020) showed how spiking neuronal networks can efficiently represent a noise input signal. This "D_Model” successfully showed that spiking neural networks can recreate input signal representations and how these networks can be resilient to the loss of neurons. However, this model has multiple unphysiological characteristics, such as instantaneous firing and the lack of units related to physical values. The aim of the present study is to build upon the D_Model to add biological accuracy to it and study how information transfer is affected by biophysical parameters. We first modified the D_Model in the MATLAB environment to allow for the simultaneous firing of the neurons. Using our CxSystem2 simulator in a Python environment (Andalibi et al. 2019), we built a network replicating the one used in the D_Model. We quantified the information transfer of Leaky Integrate-and-Fire units that had identical physiological values for both inhibitory and excitatory units (Comrade class) as well as more biologically accurate physiological values (Bacon class). We used various information transfer metrics such as granger causality, transfer entropy, and reconstruction error to quantify the information transfer of the network. We examined the behaviour of the network while altering the values of the capacitance, synaptic delay, equilibrium potential, leak conductance, reset potential, and voltage threshold. Broad parameter searches showed that no single set of biophysical parameters maximised all information transfer metrics, but some ranges fully blocked information transfer by either saturating or stopping neuronal firing. This suggests theoretical boundaries on the possible electrophysiological values neurons can have. From narrow searches within electrophysiological ranges, we conclude that there is no single optimal set of physiological values for information transfer. We hypothesise that different neuronal types may specialise in transferring different aspects of information such as accuracy, efficiency, or to act as frequency filters.
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Synthetic Observations of Simulated Clusters : Bridging the Gap between Observations and Simulations (2023)We investigated the connection between the 3D physical properties of stellar clusters and their measured counterparts from their 2D observed images; primarily focusing on the relationship be- tween the 3D half-mass radius (Rh3D) and the effective radius (Rheff) (also known as the 2D half-light radius) of stellar clusters. We generated an ensemble of 3D models of stellar clusters using the McLuster code. This ensemble is made up of subgroups consisting of different stellar counts, half-mass radius, concentration, maximum mass of the initial mass function, and degree of mass segregation. Each subgroup covered a broad range of their respective property in order to provide a comprehensive overview of the Rh3D to Rheff relationship as a function of these variables. Then, utilizing myosotis, we created synthetic observations of these models and investigated how the Rh3D of the cluster could be inferred from the measured Rheff of the synthetic photometric map. Our analysis reveals that for systems where all stars are of equal mass, independent of their size, the half-mass radius is equal to Rh3D ≈ 1/α Rheff where α ∼ 0.76. We show that the value of α can be inferred by a geometric relationship. We also find that this relationship holds for systems with varying values of concentration. For unsegregated systems of unequal stellar masses, we observe that the value of α oscillates around 0.76, with the amplitude of the oscillations increasing as the maximum mass of the system increases. As Rh3D by construction does not change, the only parameter to cause this variation in α is the Rheff . When we looked at mass segregated systems, we found that the value of Rheff (and similarly α) decreases generally monotonically as a function of the degree of the segregation. The presence of stars of unequal mass is the dominant factor that determines the measurements of Rheff , beyond the geometric effects of projection. The prevalence of this factor is attributed to the non-linear relationship between mass and luminosity that results in a few tens of massive stars greatly influencing the overall luminosity of the cluster, and therefore, its effective radius.
Now showing items 1-2 of 2