Difference between revisions of "ICLM Journal Club"

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=<font color="blue">'''This Week - 04 December 2020 (9:30 a.m., via Zoom)'''</font>=
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=<font color="blue">'''This Week - 11 December 2020 (9:30 a.m., via Zoom)'''</font>=
  
<u>Speaker:</u> ''' Daniel Aharoni'''
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<u>Speaker:</u> ''' Laura DeNardo'''
  
<u>Title:</u> “Developing new tools for imaging network dynamics in freely behaving animals”
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<u>Title:</u> “The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection”
  
<u>Abstract:</u> One of the biggest challenges in neuroscience is to understand how neural circuits in the brain process, encode, store, and retrieve information. Meeting this challenge requires tools capable of recording and manipulating the activity of intact neural networks in freely behaving animals. Head-mounted miniature fluorescence microscopes are among the most promising of these tools. Taking advantage of the past decade of advancements in fluorescent neural activity reports, these microscopes use wide-field single photon excitation to image activity across large populations of neurons in freely behaving animals. They are capable of imaging the same neural population across months and in a wide range of different brain regions.
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<u>Abstract:</u> Behavioral control is not unitary. It comprises parallel systems, model based and model free, that respec- tively generate flexible and habitual behaviors. Model-based decisions use predictions of the specific con- sequences of actions, but how these are implemented in the brain is poorly understood. We used calcium imaging and optogenetics in a sequential decision task for mice to show that the anterior cingulate cortex (ACC) predicts the state that actions will lead to, not simply whether they are good or bad, and monitors whether outcomes match these predictions. ACC represents the complete state space of the task, with reward signals that depend strongly on the state where reward is obtained but minimally on the preceding choice. Accordingly, ACC is necessary only for updating model-based strategies, not for basic reward-driven action reinforcement. These results reveal that ACC is a critical node in model-based control, with a specific role in predicting future states given chosen actions.
  
Initiated six years ago, the Miniscope Project -- an open-source collaborative effort-- aims at accelerating innovation of miniature microscope technology while also extending access to this technology to the entire neuroscience community. Currently, we are working on advancements ranging from optogenetic stimulation and wire-free operation to simultaneous optical and electrophysiology recording. Through continued optimization and innovation, miniature microscopes will likely play a critical role in extending the reach of neuroscience research and creating new avenues of scientific inquiry. 
 
  
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<u>Relevant Paper(s):</u>  https://www.sciencedirect.com/science/article/pii/S0896627320308096
  
<u>Relevant Paper(s):</u>  https://www.nature.com/articles/nature17955
 
https://www.nature.com/articles/s41593-019-0559-0
 
https://www.nature.com/articles/s41592-018-0266-x
 
miniscope.org
 
https://github.com/Aharoni-Lab/Miniscope-v4/wiki
 
  
 
='''About Us'''=
 
='''About Us'''=

Revision as of 20:02, 10 December 2020

This Week - 11 December 2020 (9:30 a.m., via Zoom)

Speaker: Laura DeNardo

Title: “The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection”

Abstract: Behavioral control is not unitary. It comprises parallel systems, model based and model free, that respec- tively generate flexible and habitual behaviors. Model-based decisions use predictions of the specific con- sequences of actions, but how these are implemented in the brain is poorly understood. We used calcium imaging and optogenetics in a sequential decision task for mice to show that the anterior cingulate cortex (ACC) predicts the state that actions will lead to, not simply whether they are good or bad, and monitors whether outcomes match these predictions. ACC represents the complete state space of the task, with reward signals that depend strongly on the state where reward is obtained but minimally on the preceding choice. Accordingly, ACC is necessary only for updating model-based strategies, not for basic reward-driven action reinforcement. These results reveal that ACC is a critical node in model-based control, with a specific role in predicting future states given chosen actions.


Relevant Paper(s): https://www.sciencedirect.com/science/article/pii/S0896627320308096


About Us

Introduction

The Integrative Center for Learning and Memory (ICLM) is a multidisciplinary center of UCLA labs devoted to understanding the neural basis of learning and memory and its disorders. This will require a unified approach across different levels of analysis, including;

1. Elucidating the molecular cellular and systems mechanisms that allow neurons and synapses to undergo the long-term changes that ultimately correspond to 'neural memories'.

2. Understanding how functional dynamics and computations emerge from complex circuits of neurons, and how plasticity governs these processes.

3. Describing the neural systems in which different forms of learning and memory take place, and how these systems interact to ultimately generate behavior and cognition.

History of ICLM

The Integrative Center for Learning and Memory formally LMP started in its current form in 1998, and has served as a platform for many interactions and collaborations within UCLA. A key event organized by the group is the weekly ICLM Journal Club. For more than 10 years, graduate students, postdocs, principal investigators, and invited speakers have presented on topics ranging from the molecular mechanisms of synaptic plasticity, through computational models of learning, to behavior and cognition. Dean Buonomano oversees the ICLM journal club with help of student/post doctoral organizers. For other events organized by ICLM go to http://www.iclm.ucla.edu/Events.html.

Current Organizers:

Megha Sehgal (Silva Lab) & Giselle Fernandes (Silva Lab)

Current Faculty Advisor:

Dean Buonomano


Past Organizers:

i) Anna Matynia(Aug 2004 - Jun 2008) (Silva Lab)

ii) Robert Brown (Aug 2008 - Jun 2009) (Balleine Lab)

iii) Balaji Jayaprakash (Aug 2008 - Nov 2011) (Silva Lab)

iv) Justin Shobe & Thomas Rogerson (Dec 2011 - June 2013) (Silva Lab)

v) Walt Babiec (O'Dell Lab) (2013-2014)

vi) Walt Babiec (O'Dell Lab) & Helen Motanis (Buonomano Lab) (2014-2017)

vii) Helen Motanis (Buonomano Lab) & Shonali Dhingra (Mehta Lab) (2017-2018)

viii) Shonali Dhingra (Mehta Lab) (2018-2020)

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