CNS*2020 Online has ended

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CNS*2020 Online

Welcome to the Sched instance for CNS*2020 Online! Please read the instruction document on detailed information on CNS*2020.

#1

K1: Deep reinforcement learning and its neuroscientific implications

#2

K2: A new computational framework for understanding vision in our brain

#3

K3: Information and Decision-Making

#4

K4: Computational models of neural development

#5

Methods from Data Science for Model Simulation, Analysis, and Visualization

#6

O4: Towards multipurpose bio-realistic models of cortical circuits

#7

O1: Dopamine role in learning and action inference

#8

Characterizing neural dynamics using highly comparative time-series analysis

#9

O2: Neural Manifold Models for Characterising Brain Circuit Dynamics in Neurodegenerative Disease

#10

F3: Neuronal morphology imposes a tradeoff between stability, accuracy and efficiency of synaptic scaling

#11

W1 S0: Methods of Information Theory in Comptuational Neuroscience - Opening Remarks

#12

Building mechanistic multiscale models, from molecules to networks, using NEURON and NetPyNE

#13

Introduction to the Brain Dynamics Toolbox

#14

F2: Using evolutionary algorithms to explore single-cell heterogeneity and microcircuit operation in the hippocampus

#15

Information theory and directed network inference (using JIDT and IDTxl)

#16

Discussion with Matt Botvinick

#17

The use of Keras with Tensor Flow applied to neural models and data analysis

#18

O14: Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

#19

O5: How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex

#20

W9 S1 - Machine learning and mechanistic modeling for understanding brain in health and disease

#21

O9: Discovering synaptic mechanisms underlying the propagation of cortical activity: A model-driven experimental and data analysis approach

#22

W3 S7: Deep Inference and Information Gain

#23

W3 S0: Information-Theoretic Models in Psychology and Neuroscience (Opening remarks)

#24

O13: Cortical integration and segregation explained by harmonic modes of functional connectivity

#25

W05 S00: Opening Remarks On Spatiotemporal Dynamics in Neuroimaging: Models and Analysis

#26

O12: A Spatial Developmental Generative Model of Human Brain Structural Connectivity

#27

W9 S8 - Strong inhibition and stable temporal dynamics in spiking networks sustain working memory

#28

W06 S1: An overview of data, tools, and computational analysis at the Allen Insitute

#29

W1 S6: Structure of information to understand the physical basis of consciousness

#30

W4 S5: The Brian Simulator

#31

Tools and techniques to bridge the gap between models and closed-loop neuroscience experiments

#32

O3: Coupled experimental and modeling representation of the mechanisms of epileptic discharges in rat brain slices

#33

F1: Delineating Reward/Avoidance Decision Process in the Impulsive-compulsive Spectrum Disorders through a Probabilistic Reversal Learning Task

#34

F4: Who can turn faster? Comparison of the head direction circuit of two species

#35

W4 S10: The Human Brain Project (HBP) Brain Simulation Platform (BSP)

#36

W4 S6: The NEURON Simulator

#37

O7: 'Awake Delta' and Theta-Rhythmic Modes of Hippocampal Network Activity Track Intermittent Locomotor Behaviors in Rat

#38

W3 S4: Working Memory 2.0

#39

O10: Neural flows: estimation of wave velocities and identification of singularities in 3D+t brain data

#40

W4 S12: NeuroML and Open Source Brain

#41

W06 S6: Is cortex a hierarchically organized set of canonical local circuits?

#42

W3 S3: Neural Circuits Underlying Bayesian Inference in Time Perception

#43

W4 Discussion 1: Where should we be in 5 years and what tools/resources are missing?

#44

W4 S7: NetPyNE modeling tool

#45

P192: Estimating Transfer Entropy in Continuous Time For Spike Trains

#46

W9 S9 - Toward multi-scale brain data assimilation

#47

W06 S17: Functional computation in the mouse visual cortex

#48

W9 S2 - Using reinforcement learning to train biophysically detailed models of visual-motor cortex to play Atari games

#49

W06 S19:Explorations of self-supervised learning in the neocortex using open data

#50

W06 S18:Testing theories of sensory coding and learning with the Allen Institute's Brain Observatory