Difference between revisions of "ICLM Journal Club"
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<u>Speaker:</u> ''' Gary Sean Escola ''' | <u>Speaker:</u> ''' Gary Sean Escola ''' | ||
| − | + | Title: “Practice makes (too) perfect: Hebbian learning and the persistence of overly trained behaviors in subcortical circuits?” | |
<u>Abstract:</u> Over a century of work in experimental psychology and neuroscience has shown that the recall of memories and the performance of learned behaviors are determined by two principal variables: recency and practice. In this work, we develop a bottom-up and mechanistic understanding of the interplay between these variables in sequentially learned memories and behaviors. To do this, we begin by modeling sequential learning in a single neuron performing classification of random input patterns, and derive a mathematical expression for the neuron's forgetting curve, which quantifies the loss of old information as new information is learned. Because this simple model is unable to address the effects of practice during learning, however, we augment it with a second input pathway consisting of synaptic weights that are modified with associative Hebbian learning, leading to a generalized forgetting curve that additionally depends on the number of times that each pattern is repeated during training. In this model, patterns that are repeated multiple times during training become far more resistant to being overwritten, with near perfect recall long after patterns that are presented only once have been forgotten. We show that this is also true in a more elaborate neural network trained with reinforcement learning to perform a sequentially learned navigation task. Furthermore, due to the slow Hebbian learning in the second pathway, the signals from the two pathways gradually become aligned with one another through repeated practice, driving downstream units in similar ways. By this mechanism, control of the downstream population is gradually passed from a fast, flexible pathway with reward-based learning to a slow, robust pathway with associative learning. We suggest a neurobiological interpretation of this model, identifying the fast input with cortex, the slow input with thalamus, and the downstream population with striatum, the major locus of reinforcement learning in the brain. This interpretation provides a quantitative framework for understanding the formation of habits and the transfer of control from cortical to subcortical circuits as behaviors become automatized through extended practice. | <u>Abstract:</u> Over a century of work in experimental psychology and neuroscience has shown that the recall of memories and the performance of learned behaviors are determined by two principal variables: recency and practice. In this work, we develop a bottom-up and mechanistic understanding of the interplay between these variables in sequentially learned memories and behaviors. To do this, we begin by modeling sequential learning in a single neuron performing classification of random input patterns, and derive a mathematical expression for the neuron's forgetting curve, which quantifies the loss of old information as new information is learned. Because this simple model is unable to address the effects of practice during learning, however, we augment it with a second input pathway consisting of synaptic weights that are modified with associative Hebbian learning, leading to a generalized forgetting curve that additionally depends on the number of times that each pattern is repeated during training. In this model, patterns that are repeated multiple times during training become far more resistant to being overwritten, with near perfect recall long after patterns that are presented only once have been forgotten. We show that this is also true in a more elaborate neural network trained with reinforcement learning to perform a sequentially learned navigation task. Furthermore, due to the slow Hebbian learning in the second pathway, the signals from the two pathways gradually become aligned with one another through repeated practice, driving downstream units in similar ways. By this mechanism, control of the downstream population is gradually passed from a fast, flexible pathway with reward-based learning to a slow, robust pathway with associative learning. We suggest a neurobiological interpretation of this model, identifying the fast input with cortex, the slow input with thalamus, and the downstream population with striatum, the major locus of reinforcement learning in the brain. This interpretation provides a quantitative framework for understanding the formation of habits and the transfer of control from cortical to subcortical circuits as behaviors become automatized through extended practice. | ||
Revision as of 00:52, 23 October 2019
This Week - 18 October 2019 (9:30 a.m., Gonda 2nd Floor Conference Room)
Speaker: Gary Sean Escola
Title: “Practice makes (too) perfect: Hebbian learning and the persistence of overly trained behaviors in subcortical circuits?”
Abstract: Over a century of work in experimental psychology and neuroscience has shown that the recall of memories and the performance of learned behaviors are determined by two principal variables: recency and practice. In this work, we develop a bottom-up and mechanistic understanding of the interplay between these variables in sequentially learned memories and behaviors. To do this, we begin by modeling sequential learning in a single neuron performing classification of random input patterns, and derive a mathematical expression for the neuron's forgetting curve, which quantifies the loss of old information as new information is learned. Because this simple model is unable to address the effects of practice during learning, however, we augment it with a second input pathway consisting of synaptic weights that are modified with associative Hebbian learning, leading to a generalized forgetting curve that additionally depends on the number of times that each pattern is repeated during training. In this model, patterns that are repeated multiple times during training become far more resistant to being overwritten, with near perfect recall long after patterns that are presented only once have been forgotten. We show that this is also true in a more elaborate neural network trained with reinforcement learning to perform a sequentially learned navigation task. Furthermore, due to the slow Hebbian learning in the second pathway, the signals from the two pathways gradually become aligned with one another through repeated practice, driving downstream units in similar ways. By this mechanism, control of the downstream population is gradually passed from a fast, flexible pathway with reward-based learning to a slow, robust pathway with associative learning. We suggest a neurobiological interpretation of this model, identifying the fast input with cortex, the slow input with thalamus, and the downstream population with striatum, the major locus of reinforcement learning in the brain. This interpretation provides a quantitative framework for understanding the formation of habits and the transfer of control from cortical to subcortical circuits as behaviors become automatized through extended practice.
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:
Current Faculty Advisor:
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)
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