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A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus : A Computational Approach
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Transcript of A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus : A Computational Approach
A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus: A Computational Approach
Journal Club5/16/12
Outline
• Review of Dentate Gyrus Model Types– Introduction to Pattern Separation
• Model Setup• Model Results / Comparison to Experimental
Data
1. Functional Models with Simplified Physiology
• CA3 – Pattern Completion and Pattern Storage• Storage Capacity of CA3 is highest if inputs do not overlap• Increase storage capacity by decreasing overlap of input patterns• Pattern Separation
– Definition: “ability to transform a set of similar input patterns into a less-similar set of output patterns”
– Methods• Fewer elements are active in each pattern• Those that are active can be orthogonalized
• Dentate Gyrus inherent pattern separation properties – (decrease probability that two separate entorhinal input activate the same subset of CA3
neurons)– Low firing probability of dentate gyrus cells– Low contact probability of dentate granule cells axons to CA3 pyramidal cells
• Variations– Plasticity– Neurogenesis
2. Physiologically Detailed Models
• Include detailed cells of many types– Mossy fiber sprouting can lead to granule cell
hyperexcitability– Nonrandom connections between granule cells
could produce hyperexcitable, seizure-prone circuits
– Did not directly address pattern separation• (authors include Santhakumar, Morgan and
Soltez)
3. Sequence Learning Models
• Excitatory granule cell–mossy cell–granule cell loops could form circuits with variable delays
• Allows dentate gyrus to recover temporal structure originally present in entorhinal inputs.
• (authors include Lisman, Buzsaki)
Myers and Scharfman Model• Model Components
– Perforant Path Inputs– Granule Cells – Interneurons– Mossy Cells
• glutamatergic– HIPP Cells
• GABAergic
• Conclusions– Reproduction of Experimental Results – Pattern separation can be dynamically
regulated by HIPP and Mossy Cells
Pattern Separation in ModelInput – 98% Overlap Output – 68.4% Overlap
Pattern Separation: Effect on Input Density
• Active Inputs chosen randomly
Experimental Pattern Separation Results Examples
• Lesioning dentate gyrus in rats• Human functional neuroimaging study• Recording place cells in rat DG and CA3 when
environment is morphed (Leutgeb)
DG and CA3 Place Cell Changes as Environment is morphed
CA3Dentate Gyrus
Leutgeb Model
Leutgeb
Model
Results
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Effects of Hilar LesionModelRatzliff et al. 2004
Takeaways
• Review of other types of Models• Model Reproduction of Experimental Results• HIPP and Mossy Cell Activity levels can
dynamically regulate pattern separation