Difference between revisions of "ICLM Journal Club"
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| − | Speaker: ''' | + | Speaker: ''' David Glanzman ''' |
| − | Title: ''' | + | Title: ''' Silent memory engrams as the basis for retrograde amnesia ''' |
Abstract: The aim of this work is to study pattern recognition in the cerebellar cortex by re-examining Marr’s theory of cerebellar function (Marr, 1969) based on the current state of knowledge. To meet these objectives we constructed a large-scale network model of the cerebellar cortex. The network model included a single Purkinje cell and the different types of neurons in the volume of cerebellar cortex that could contribute to the input to the Purkinje cell. As predicted by Marr, mossy fibre (MF) patterns that were applied to the granule cell layer (GCL) network model were transformed into sparse parallel fibre (PF) patterns with a larger arity. The ability of the GCL network to perform pattern sparsification was increased by adding feed-forward inhibition, but decreased by adding gap junctions between the Golgi cells. Moreover, the sparsity of the PF patterns that were generated by the GCL network operating in the synchronized oscillatory mode was lower than in the GCL network in the asynchronous activity state. In addition, network models that exhibited synchronized oscillations transformed MF input patterns into PF patterns that were more similar to each other, which can be considered as performing a generalization task. This generation of similar PF patterns resulted in an inability to perform pattern recognition in these synchronized network models. Only network models that operated in an asynchronous activity mode could perform pattern separation and pattern recognition. | Abstract: The aim of this work is to study pattern recognition in the cerebellar cortex by re-examining Marr’s theory of cerebellar function (Marr, 1969) based on the current state of knowledge. To meet these objectives we constructed a large-scale network model of the cerebellar cortex. The network model included a single Purkinje cell and the different types of neurons in the volume of cerebellar cortex that could contribute to the input to the Purkinje cell. As predicted by Marr, mossy fibre (MF) patterns that were applied to the granule cell layer (GCL) network model were transformed into sparse parallel fibre (PF) patterns with a larger arity. The ability of the GCL network to perform pattern sparsification was increased by adding feed-forward inhibition, but decreased by adding gap junctions between the Golgi cells. Moreover, the sparsity of the PF patterns that were generated by the GCL network operating in the synchronized oscillatory mode was lower than in the GCL network in the asynchronous activity state. In addition, network models that exhibited synchronized oscillations transformed MF input patterns into PF patterns that were more similar to each other, which can be considered as performing a generalization task. This generation of similar PF patterns resulted in an inability to perform pattern recognition in these synchronized network models. Only network models that operated in an asynchronous activity mode could perform pattern separation and pattern recognition. | ||
Revision as of 11:17, 28 November 2017
This Week - 1 December 2017 (9:30 a.m., Gonda 2nd Floor Conference Room)
Speaker: David Glanzman
Title: Silent memory engrams as the basis for retrograde amnesia
Abstract: The aim of this work is to study pattern recognition in the cerebellar cortex by re-examining Marr’s theory of cerebellar function (Marr, 1969) based on the current state of knowledge. To meet these objectives we constructed a large-scale network model of the cerebellar cortex. The network model included a single Purkinje cell and the different types of neurons in the volume of cerebellar cortex that could contribute to the input to the Purkinje cell. As predicted by Marr, mossy fibre (MF) patterns that were applied to the granule cell layer (GCL) network model were transformed into sparse parallel fibre (PF) patterns with a larger arity. The ability of the GCL network to perform pattern sparsification was increased by adding feed-forward inhibition, but decreased by adding gap junctions between the Golgi cells. Moreover, the sparsity of the PF patterns that were generated by the GCL network operating in the synchronized oscillatory mode was lower than in the GCL network in the asynchronous activity state. In addition, network models that exhibited synchronized oscillations transformed MF input patterns into PF patterns that were more similar to each other, which can be considered as performing a generalization task. This generation of similar PF patterns resulted in an inability to perform pattern recognition in these synchronized network models. Only network models that operated in an asynchronous activity mode could perform pattern separation and pattern recognition.
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:
Shonali Dhingra & Helen Motanis
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)
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