Impact of Hill coefficient and time delay on a perceptual decision-making model
In this paper, a neural mass perceptual decision making model introduced by Piska{\l}a et al. is analyzed. The model describes activity of two neuron populations influenced by each other and external inputs. The groups' activities correspond to the process of making a perceptual binary decision. Existing results are generalized by investigating the impact of both a delay in self-inhibition and a generic Hill coefficient on solutions to the system of differential equations. Several versions of the model with various assumptions are compared using analytical and numerical methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
Relationship between Decimal Hill Coefficient, Intermediate Processes and Mesoscopic Fluctuations
The Hill function is relevant for describing enzyme binding and other processes in gene regulatory networks. Despite its theoretical foundation, it is often empirically used as a useful fitting function. Theoretical pred…
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
Large language model (LLM) scaling laws are empirical formulas that estimate changes in model quality as a result of increasing parameter count and training data. However, these formulas, including the popular Deepmind C…
Language ModelingLanguage ModellingLarge Language ModelComputationally-efficient and perceptually-motivated rendering of diffuse reflections in room acoustics simulation
Geometrical acoustics is well suited for simulating room reverberation in interactive real-time applications. While the image source model (ISM) is exceptionally fast, the restriction to specular reflections impacts its …
Ultrasensitivity and sharp threshold theorems for multisite systems
We study the ultrasensitivity of multisite binding processes where ligand molecules can bind to several binding sites, considering more particularly recent models involving complex chemical reactions in phosphorylation s…
Reconciling Kaplan and Chinchilla Scaling Laws
Kaplan et al. [2020] (`Kaplan') and Hoffmann et al. [2022] (`Chinchilla') studied the scaling behavior of transformers trained on next-token language prediction. These studies produced different estimates for how the num…