Difference between revisions of "ICLM Journal Club"

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=<font color="blue">'''This Week - 05 March 2021 (9:30 a.m., via Zoom)'''</font>=
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=<font color="blue">'''This Week - 12 March 2021 (9:30 a.m., via Zoom)'''</font>=
  
<u>Speaker:</u> '''Hessameddin Akhlaghpour'''
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<u>Speaker:</u> '''Mayank Mehta'''
  
<u>Title:</u> “Where is Life's Computer? An RNA-Based Theory of Natural Universal Computation
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<u>Title:</u> “Deciphering the interactions between large neuronal networks
  
<u>Abstract:</u>  Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal computation, i.e., Turing-equivalent in scope. Finite-dimensional dynamical systems (which encompass most models of neural networks, intracellular signaling cascades, and gene regulatory networks) fall short of universal computation, but are assumed to be capable of explaining cognition and development. I present a class of models that bridge two concepts from distant fields: combinatory logic (or, equivalently, lambda calculus) and RNA molecular biology. A set of basic RNA editing rules can make it possible to compute any computable function with identical algorithmic complexity to that of Turing machines. The models do not assume extraordinarily complex molecular machinery or any processes that radically differ from what we already know to occur in cells. Distinct independent enzymes can mediate each of the rules and RNA molecules solve the problem of parenthesis matching through their secondary structure. The most plausible of these models does not strictly mimic the operation rules of combinatory logic or lambda calculus; it relies on standard RNA transcription from static genomic templates and the editing rules can be implemented with merely cleavage and ligation operations. This demonstrates that universal computation is well within the reach of molecular biology. It is therefore reasonable to assume that life has evolved – or possibly began with – a universal computer that yet remains to be discovered. The variety of seemingly unrelated computational problems across many scales can potentially be solved using the same RNA-based computation system. Experimental validation of this theory may immensely impact our understanding of memory, cognition, development, disease, evolution, and the early stages of life.
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<u>Abstract:</u>  The world of physics is driven by powerful mathematical theories that make strange predictions --e.g. black holes, that are verified by very sophisticated experiments. Is it possible to develop such theories in systems neuroscience? Most systems neuroscience questions involve interaction between large neuronal networks, e.g. thalamo-cortical, cortico-hippocampal, cortico-striatal etc. There are many wonderful experimental studies and large scale simulations of these interactions. But, the number of parameters involved are so huge and unknown that it is virtually impossible to match the experiments and simulations to obtain a mathematically sound theory, let alone its experimental test. I will describe our recent attempts to address this challenge and some successes. I will keep the presentation short and focus on a discussion of the experimental and theoretical challenges involved in deciphering network dynamics in vivo.
  
<u>Relevant Paper(s):</u>    https://arxiv.org/abs/2008.08814
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='''About Us'''=
 
='''About Us'''=

Revision as of 00:34, 10 March 2021

This Week - 12 March 2021 (9:30 a.m., via Zoom)

Speaker: Mayank Mehta

Title: “Deciphering the interactions between large neuronal networks ”

Abstract: The world of physics is driven by powerful mathematical theories that make strange predictions --e.g. black holes, that are verified by very sophisticated experiments. Is it possible to develop such theories in systems neuroscience? Most systems neuroscience questions involve interaction between large neuronal networks, e.g. thalamo-cortical, cortico-hippocampal, cortico-striatal etc. There are many wonderful experimental studies and large scale simulations of these interactions. But, the number of parameters involved are so huge and unknown that it is virtually impossible to match the experiments and simulations to obtain a mathematically sound theory, let alone its experimental test. I will describe our recent attempts to address this challenge and some successes. I will keep the presentation short and focus on a discussion of the experimental and theoretical challenges involved in deciphering network dynamics in vivo.


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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