(C) Neural pattern inference via hidden Markov model (HMM). An HMM (left, schematics) is fit to a representative session in (D), returning a set of neural patterns (emission matrix, center) and atransition probabilitymatrix (TPM; right). Each pattern is a population firing rate vector (c...
Using Markov chain Monte Carlo sampling, we performed 1000 calculations after 1000 burn-ins and then used the median from the estimates. For each increment, the back-calculated fork length was plotted against the fork length at the time of catch to check the bias. The bias in which estimated...
One of the most influential working hypotheses in psychology, to this day, is that human information processing in higher-order cognition (e.g., judgment and decision-making) is constrained by having to process objects serially, one at a time. However, a rather large body of research, accumul...
The only non-deterministic process is token selection. There is randomness involved in it (to a variable degree). That's why GPT-based chatbots can give different answers to the same prompt.We will use the phrase "Happy New Year! I wish" as the prompt and make the model generate 10 ...
Even for a static sensory input (as in the present study), each step of inference might depend on pre- ceding steps, with sudden insights changing the course of the process. Recurrent neural net- work models can capture such processes and will be essential for understanding...
For discrete prices process, the volatility at the given time of trade 𝑡t can be estimated by quadratic variation (𝑄𝑉)(QV) as: 𝑄𝑉𝑡=∫𝑡𝑡−1𝜎2𝑠𝑑𝑠+∑𝑡−1<𝑠≤𝑡𝜅2𝑠𝑡QVt=∫t−1tσs2ds+∑t−1<s≤tκs2t (1) where ∫𝑡𝑡−1...