📗 -> 10/1/24: The Neuron
🎤 Vocab
❗ Unit and Larger Context
This lecture focuses on the neuron
This class:
- Many characterization of the brain-mind relationship tell us what is involved, but not necessarily how
- As you learn about links between neural and psychological processes and think about your answers to how the brain secretes the mind, ask:
- If the links got scrambled, would that no longer make sense?
- Could I build it?
- This is where CCN is key
✒️ -> Scratch Notes
Active learning project
Analyzing the neuron at different levels:
- Psychological
- Biological
- Mathematical
- Computational
It is a goal for the course that everyone can explain each of these levels to someone else
Detector Model
A detector model of the neuron abstracts it to a detector, similar to a smoke detector
Has:
- Inputs, that feed it info (dendrite)
- An integrator, that makes a decision (cell body, membrane potential)
- Output, that makes an output (axon)
… Went to ecn exp …
How do we simulate neurons?
…
Part C
Ions: Sodium (Na+), Chloride (Cl-), Potassium (K+) flow in/out of neuron under forces of electricity and diffusion
Part D
…
A chemical imbalance (concentration not equal inside and out of the cell) is induced to allow the neuron to fire. This is an active process, to prevent chemicals from diffusing like they want to.
Calculating current
🧪-> Survey
- Explain to a friend/relative/person on the bus (outside the field), in your own words: How can we understand the neuron’s role in our thoughts and actions, at these levels?: a) psychological, b) biological, c) mathematical, d) computational
a) A neuron is the brains information transmitter, and when they’re activated enough they’ll pass along a signal to other brain areas. For example, we have neurons in our brain that are associated with goals and desires, and these signals are passed on to neurons in other brains areas to help inform our decisions.
b) At the biological level, a neuron is a cell that takes in a chemical or electrical signal from another neuron, and create an output, or spike, that passes along its signal to other signals in turn if the input signal is strong enough. They can also be specialized to be receptive to special kinds of neurons, like neurons in your eye that can convert the light hitting your retina into signal that other neurons can interpret.
c) Mathematically, a neuron is a combiner that takes in a number of inputs from other cells, will process them in its cell body, and then create an output in the form of a spike. This can be seen as similar to a function, which will take an input and then create an output. In fields like deep learning, we have created functions based on these neurons that have led to really advancements in the fields of language and image processing.
d) Computationally, we can understand the behavior of a neuron by simulating its environment and response. In fact, we have models that take into account the chemicals that a signal neuron is surrounded by and emulates its response pretty well!
The neuron is one of the base units by which information is transmitted in the brain, so it has a critical place in understanding the mind, and psychology by extension. One example of its use is in fMRI, which tracks the BOLD signal, which is in turn associated with increased neuronal firing. This method has helped us to track activity throughout the brain, such as helping to identify the fusiform face area.
- How far do you follow the discussion of how we simulate neurons?
I’ve followed the discussion pretty well, but I’m still a bit confused on how this translates to efforts outside of modeling. I think as we get through the class I’ll understand this a bit more, but I’m still a bit unsure of how modeling efforts combine with scientific advancement in science.
🔗 -> Related Word
- Link all related words