I am a beginner in HTM and I looked at the HTM school video on TM. I was trying to understand the pseudocode of the TM implementation from the BAMI book. But I have difficulty in understanding since the terminologies used there are quite new to me (considering that I am not from biology background). So, here are my questions:
- The BAMI book mentions this in TM section:
Every cell has many distal dendrite segments. If the sum of the active synapses on a distal segment exceeds a threshold, then the associated cell enters the predicted state. Since there are multiple distal dendrite segments per cell, a cell’s predictive state is the logical OR operation of several constituent threshold detectors.- My understanding is that every cell in a column has one-to-one connection with other cells in the same layer. But the statement says each cell has multiple segments which inturn might connect to different cells. I am confused with this statement. Please explain whats dendrite segments, synapses with the context from HTM school video.
- Is the SDR union concept used in TM? If so, can you tell me in which part of the algorithm?
- I also looked at the pseudocode of TM in BAMI book. I dont understand these terms:
LEARNING_THRESHOLD. I dont understand what synapses mean here (whether its one-on-connection from one cell to another in the same layer or something else) because of my confusion in question 1.
Other set of questions I had after studying TM:
- How does the brain decode these predicted neuron signals from TM to say a str value (incase of prediction of next word for example)?
- What is the effect of having multiple active cells per mini-column at every timestep in TM? Does it make the predictions robust?
- Please point me towards the biological terminologies to learn to understand the TM pseudocode.