These are just some papers that I found that are not too complex for me to get into, but have a great deal of useful material for me to investigate. These are just for reading and documenting, but not for Paper Reimplementation as those should be more computer science related.
When doing this, I want to try applying what I learned in Artem Kirsanov’s How To Read a Research Paper Effectively.
Slow Dynamics and High Variability in Balanced Cortical Networks with Clustered Connections
This paper investigates how clustering of excitatory connections in cortical networks can introduce slow firing rate fluctuations and high trial-to-trial variability, even with a balance of excitation and inhibition. The authors show that modest clustering can substantially change network dynamics, leading to transitions between high and low activity states in neuronal clusters. This results in both fast spiking variability and slow firing rate fluctuations, consistent with experimental observations in cortical networks.
- Start a seed note investigating this paper
Slow Dynamics and High Variability in Balanced Cortical Networks with Clustered Connections
Performance-optimized Hierarchical Models Predict Neural Responses in Higher Visual Cortex
This study developed a hierarchical neural network model that matched human performance on object recognition tasks. Remarkably, this model was highly predictive of neural responses in higher visual cortical areas like V4 and inferior temporal cortex, even though it was not designed to fit neural data. The results suggest that the biological mechanisms underlying visual processing may have been shaped by a process of performance optimization.
- Start a seed note investigating this paper
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Neuronal Population Coding of Movement Direction
The paper shows that while individual neurons in the motor cortex are only broadly tuned to movement direction, the combined activity of a population of these neurons can precisely encode the direction of arm movements. This population-level encoding allows for precise motor control despite the broad tuning of individual neurons.
- Start a seed note investigating this paper