Difference between revisions of "ICLM Journal Club"

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=<font color="blue">'''This Week - 03 December 2021 (9:30 a.m., via Zoom)'''</font>=
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=<font color="blue">'''This Week - 17 December 2021 (9:30 a.m., via Zoom)'''</font>=
  
<u>Speaker:</u> '''Dean Buonomano '''
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<u>Speaker:</u> '''Carl Schoonover & Andrew Fink '''
  
<u>Title: </u> ''' “ Does the brain implement the most powerful learning rule in machine learning?” '''
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<u>Title: </u> ''' “ Learning and forgetting in the primary olfactory cortex ” '''
  
<u>Abstract:</u>  Arguably, understanding the learning rules that govern synaptic connectivity and strength provide the most important level of understanding in neuroscience, because it is this algorithmic understanding that potentially provides the ability to emulate the brain. I will discuss what would comprise "understanding" in neuroscience, and focus on a paper that attempts to retrofit the most powerful learning rule in machine learning (backpropagation) into the brain by relying on dendritic bursts, feed-forward and feed-back connectivity, and short-term depression/facilitation.  
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<u>Abstract:</u>  AWe have discovered that in the rodent primary olfactory cortex (piriform) the pattern of neural activity evoked by a smell changes with the passage of time. These changes, which unfold absent a task or learning paradigm, accumulate to such an extent that after just a few weeks odor responses bear little resemblance to their original form. The piriform has been traditionally hypothesized to establish the identity of odorants. Our observations have forced us to radically reconsider the role of this vast brain region in olfactory perception. We propose that the piriform operates instead as a flexible learning system, a ‘scratch pad’ that continually learns and continually overwrites itself. This poses the problem of how transient memory traces can subsequently be stored over long timescales.
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These results also raise the question of what the piriform learns. We have designed a behavioral assay that provides a sensitive readout of whether mice expect a given sensory event. Using this assay we have demonstrated that mice learn the identity, order and precise timing of elements in a sequence of neutral odorants, A-->B, without reward or punishment. Simultaneous recordings in naïve primary olfactory cortex (piriform) show strong and distinct responses to both A and B.  These diminish with experience in a manner that tracks these expectations: predictable cues, such as B in the A-->B sequence, evoke hardly any response in experienced animals. This does not reflect simple adaptation. When B is presented alone, it elicits robust activation. When B is omitted, and A is presented alone, piriform exhibits vigorous activity at the precise moment when the animal, expecting odor B, encounters nothing. Thus, when the external world conforms to expectation, piriform is relatively quiescent, but any departure from the expected results in vigorous activation. The biological learning mechanisms that generate this predictive activity, a feature more commonly encountered in higher order cortices, can be readily studied and probed in a circuit only two synapses from the sensory periphery.
  
 
<u>Relevant Papers:</u>
 
<u>Relevant Papers:</u>
  
https://www.nature.com/articles/s41593-021-00857-x#Sec8
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Schoonover, C.E., Ohashi, S.O., Axel, R. Fink, A.J.P. (2021) Representational drift in primary olfactory cortex. Nature 594: 541–546.
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Fink A.J.P., Axel R., Schoonover, C.E. (2019) A virtual burrow assay for head–fixed mice measures habituation, discrimination, exploration and avoidance without training. eLife 2019;8:e45658
  
  

Revision as of 23:49, 14 December 2021

This Week - 17 December 2021 (9:30 a.m., via Zoom)

Speaker: Carl Schoonover & Andrew Fink

Title: “ Learning and forgetting in the primary olfactory cortex ”

Abstract: AWe have discovered that in the rodent primary olfactory cortex (piriform) the pattern of neural activity evoked by a smell changes with the passage of time. These changes, which unfold absent a task or learning paradigm, accumulate to such an extent that after just a few weeks odor responses bear little resemblance to their original form. The piriform has been traditionally hypothesized to establish the identity of odorants. Our observations have forced us to radically reconsider the role of this vast brain region in olfactory perception. We propose that the piriform operates instead as a flexible learning system, a ‘scratch pad’ that continually learns and continually overwrites itself. This poses the problem of how transient memory traces can subsequently be stored over long timescales.

These results also raise the question of what the piriform learns. We have designed a behavioral assay that provides a sensitive readout of whether mice expect a given sensory event. Using this assay we have demonstrated that mice learn the identity, order and precise timing of elements in a sequence of neutral odorants, A-->B, without reward or punishment. Simultaneous recordings in naïve primary olfactory cortex (piriform) show strong and distinct responses to both A and B. These diminish with experience in a manner that tracks these expectations: predictable cues, such as B in the A-->B sequence, evoke hardly any response in experienced animals. This does not reflect simple adaptation. When B is presented alone, it elicits robust activation. When B is omitted, and A is presented alone, piriform exhibits vigorous activity at the precise moment when the animal, expecting odor B, encounters nothing. Thus, when the external world conforms to expectation, piriform is relatively quiescent, but any departure from the expected results in vigorous activation. The biological learning mechanisms that generate this predictive activity, a feature more commonly encountered in higher order cortices, can be readily studied and probed in a circuit only two synapses from the sensory periphery.

Relevant Papers:

Schoonover, C.E., Ohashi, S.O., Axel, R. Fink, A.J.P. (2021) Representational drift in primary olfactory cortex. Nature 594: 541–546.

Fink A.J.P., Axel R., Schoonover, C.E. (2019) A virtual burrow assay for head–fixed mice measures habituation, discrimination, exploration and avoidance without training. eLife 2019;8:e45658


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). Please email us at iclm.journalclub@gmail.com if you would like to get regular updates regarding our journal club and weekly reminders.

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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