Dataview
LIST
FROM #Collection
WHERE file.name = this.Entry-For📗 -> Chapter 1: Introduction
🎤 Vocab
Reductionism - A complex system is reduced to its simplest parts
Reconstructionism - A complex system is rebuilt from simpler parts
Emergence - The hope that complexity arises from simpler parts, often without a lot of the finer details included.
❗ Information
Small summary
✒️ -> Scratch Notes
- 20 billion neurons in neocortex
Textbook Organization
Part 1
- Chapter 2 -> Neurons
- Chapter 3 -> Aggregate neural networks
- Chapter 4 -> Learning Mechanisms
Part 2:
- Human thought (cognition)
- Chapter 6 -> Perception & Attention
- Chapter 7 -> Learning and Memory
- Chapter 8 -> Motor control and RL
- Chapter 9 -> Executive Function
- Chapter 10 -> Language
Dopamine and Reward: Why do we get bored with things so quickly? Because our dopamine system is constantly adapting to everything we know, and only gives us rewards when something new or different occurs. We’ll see how this all happens through interacting brain systems that drive phasic dopamine release.
Computational Approach
This class and textbook frequently use computer models. Why?
- It helps student learning
- Similar to how computers are needed in climate modeling, the similar level of complexity in neuroscience necessitates it
How do we know these models aren’t just completely made-up fantasies? The answer seems simple: the models must be constrained by data at as many levels as possible, and they must generate predictions that can then be tested empirically
The goal is not necessarily to mirror a brain, as this model wouldn’t be any easier to understand than the brain itself.
Instead, we want the simplest model that captures the data.
The end goal is to refine the model, eliminating mistakes and hoping that some form of emergence takes place.
- The hope that higher function will emerge naturally when simpler parts are combined correctly, like gears producing torque when combined
Levels of Analysis:
Marr’s Levels of Analysis are a famous framework for psychological analysis:
• Computational — what computations are being performed? What information is being processed?
• Algorithmic — how are these computations being performed, in terms of a sequence of information processing steps?
• Implementational — how does the hardware actually implement these algorithms?
Although lower levels of analysis are harder, they are often more informative. The authors use the analogy of trying to fit together a jig saw puzzle, with higher levels not having color and only shape (everything gets confused) vs. lower levels where you have the hints of colors to work with.
Through computational models in Chapter 8 (Memory), we can see that these biological details produce high levels of pattern separation which keep memories highly distinct, and thus enable rapid learning without creating catastrophic levels of interference
Computational models in Chapter 10 (Executive Function) show that the dopamine system can exhibit a kind of time travel needed to translate later utility into an earlier decision of what information to maintain, and those in Chapter 7 (Motor) show that the effects of dopamine on the basal ganglia circuitry are just right to facilitate decision making based on both positive and negative outcomes.
Gary Cottrell’s best computational modeling papers
🗣️ -> Reactions
Big Picture: I enjoyed the introduction to the philosophy of computational cognitive neuroscience. I’ve often found it difficult to find a satisfying approach to learning about the brain that is at a level that lets me understand what is going on, while not being completely abstracted away from the biological underpinnings. We’ll see as we move through this course if this level of analysis is a ‘sweet spot’ for me, and as always this depends on what is being studied.
Specific: I think that the chapters descriptions about the goals of computational modeling are very intriguing. At the moment, the idea of making a computational model to be an end goal feels a bit odd to me, the analogy to climate science rings a bit hollow to me because climate science often seeks to make predictions, while neuroscience seeks to understand mechanisms. That being said, I think I’m missing a lot of context about this assertion and I’m excited to see how modeling advances science in this course.