Hi, I am interested whether there have been researches done on making a biophysical neural-network version of the drift-diffusion decision-making model. Specifically, I hope to know whether such a biologically constrained model can achieve all of the following features:

1. Transformation of sensory signals to linearly increasing firing rate of neurons over time.

2. The rate of climbing firing rate reflects the strength of sensory evidence.

3. Giving the same strength of sensory evidence, the rate of climbing firing rate varies randomly from trials to trials.

4. Decision is made when the firing rate of neurons reaches a specific threshold.

5. The distribution of decision latency (or response time) fits well with experimentally measured response time in simple decision tasks.