Computational and Theoretical Neuroscience Journal Club at CNCD (Past)

As the tools in experiments neuroscience are rapidly developing, this is an important time to consider the broad trends and aims of the computational/theoretical works that are born alongside them. Our aim is to present and discuss papers that reflect these modern directions so that we may better understand how our own work fits into a global neuroscience picture. Some themes and topics that we hope to cover are:

• computational models of neural systems
• novel analysis tools for understanding neural recordings
• recent advances in theoretical neuroscience
• popular theories in neuroscience

Current schedule

2022 Spring Schedule

• 1/26          Josue Nassar
• 2/2             Kurt Butler
• 2/9             Memming Park
• 2/16          Thomas Grunauer
• 2/23          Shahbegh Kalra
• 3/2             Matthew Dowling
• 3/9             Danielle Roedel
• 3/16          No JC (Spring Break)
• 3/23          Siddharth Paliwal
• 3/30          Ayesha Vermani
• 4/6             Tianshu Li
• 4/13          Irene Nozal
• 4/20          Tushar Arora
• 4/27          Dan Waxman
• 5/4             Jake Crosser
• 5/11          No JC (Final exams)
• 5/18          No JC (Final exams)

2022 Spring Papers

1/26  Reward-based training of recurrent neural networks for cognitive and value-based tasks

2/2  Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

2/9  Coherent encoding of subjective spatial position in visual cortex and hippocampus

2/16  Difference Target Propagation

2/23  Multiscale adaptive analysis of circadian rhythms and intradaily variability: Application to actigraphy time series in acute insomnia subjects

3/2  Opposing effects of selectivity and invariance in peripheral vision

3/9  Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

3/23  Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

3/30 Sequential and efficient neural-population coding of complex task information

4/6 Attractor-state itinerancy in neural circuits with synaptic depression

4/13 Sucrose intensity coding and decision-making in rat gustatory cortices

4/20 Generalized Shape Metrics on Neural Representations

4/27 Prefrontal cortex as a meta-reinforcement learning system

5/4 Representational geometry of perceptual decisions in the monkey parietal cortex

2021 Fall Schedule

• 9/8          Xiaoyu Yang
• 9/15       Tong Liang
• 9/22       Jake Crosser
• 9/29       Matthew Dowling
• 10/6       Tianshu Li
• 10/13    Ayesha Vermani
• 10/20    Tushar Arora
• 10/27    Siddharth Paliwal
• 11/3       Piotr Sokol
• 11/10    Ian Jordan
• 11/17    Khue Tran
• 11/24    No JC (Thanksgiving break)
• 12/1       Irene Nozal Martin
• 12/8       No JC (Final exams)
• 12/15    No JC (Final exams)

2021 Fall Papers

9/8    Dynamic Population Coding of Category Information in ITC and PFC (2008)

9/15  Synaptic plasticity as Bayesian inference (2021)

9/22  Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems (2016)

9/29  Building population models for large-scale neural recordings: Opportunities and pitfalls (2021)

10/6  Choice-relevant information transformation along a ventrodorsal axis in the medial prefrontal cortex

10/13  Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity

10/20 Continual Learning Through Synaptic Intelligence

10/27 Emergence of Slow-Switching Assemblies in Structured Neuronal Networks

11/3 Targeted Neural Dynamical Modeling

11/10 Area 2 of primary somatosensory cortex encodes kinematics of the whole arm

11/17 Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

12/1 Inhibitory connectivity defines the realm of excitatory plasticity