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[Paper Review] Implicit encoding of prior probabilities in optimal neural populations
With poisson and fisher information, this paper shows that warping adjusts neuron density and gain to prioritize high-probability stimuli
[Paper Review] Hippocampal Engram Formation and Memory Precision
This study investigates the neurobiological mechanisms underlying the development of precise episodic-like memory in mice, focusing on CA1 region.
[Paper Review] Learning efficient task-dependent representations with synaptic plasticity
cs's recommendation! proposed stochastic recurrent neural circuit model. analyzed how prior is encoded and how intrinsic noise affects.
[Probabilistic Machine Learning] Gaussian Process
GPFA (Gaussian-Process Factor Analysis) and CCA (Canonical Correlation Analysis)
[Paper Review] Disentangling Representations through Multi-task Learning
multi-task learning. repelling representation in parallel setting.
[Paper Review] Competition Dynamics Shape Algorithmic Phases of In-Context Learning
algorithmic phases, competition dynamics, in-context learning. showed previous observations including transient state of good generalization mode.
[Paper Review] Signatures of Criticality in Efficient Coding Networks
This paper studies two big ideas in neuroscience: criticality and efficient coding
[Paper Review] Recurrence resonance - noise-enhanced dynamics in recurrent neural networks
random noise enhances exploring multiple attractors
[Paper Review] Fragmentation of grid cell maps in a multicompartment environment
In this paper, they recorded neural activity in grid cells and place cells when rats ran through a hairpin maze.
[Probabilistic Machine Learning] Hidden Markov Model (forward-backward algorithm)
HMM (forward-backward algorithm) study and implementation
[Paper Review] Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
concept space, signal strength, learning dynamics and trajectories, phenomenological model, sudden transition in learning phase, underspecification
[Paper Review] Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
curse of memory: the necessity of memory decay for approximation
[Paper Review] Understanding and Controlling Memory in Recurrent Neural Networks
curriculum learning, attractor formation
[Paper Review] A complexity-based theory of compositionality
tried to quantify the concept of compositionality through the lens of Kolmogorov complexity, algorithmic information quantity
[Paper Review] The Simplicity Bias in Multi-Task RNNs: Shared Attractors, Reuse of Dynamics, and Geometric Representation
they observed the fundamentals of RNN behavior - simplicity bias, a tendency to share attractors and reuse dynamics.
[Paper Review] Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
task period dynamics, representation, task compositionality
[Paper Review] Task representations in neural networks trained to perform many cognitive tasks
task representation, compositionality
[Paper Review] Decision making, movement planning and statistical decision theory & Statistical decision theory and the selection of rapid, goal-directed movements
Decision making, motor task, uncertainty 관련해서 2000년대 꽤나 현재 연구의 초석이 되는 두 논문을 간단히 정리한 내용이다.