A Role for Hilar Cells in Pattern Separation

  • Slides: 12
Download presentation
A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus: A Computational

A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus: A Computational Approach Journal Club 5/16/12

Outline • Review of Dentate Gyrus Model Types – Introduction to Pattern Separation •

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 • • CA 3 – Pattern Completion and

1. Functional Models with Simplified Physiology • • CA 3 – Pattern Completion and Pattern Storage Capacity of CA 3 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 lesssimilar 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 CA 3 neurons) – Low firing probability of dentate gyrus cells – Low contact probability of dentate granule cells axons to CA 3 pyramidal cells • Variations – Plasticity – Neurogenesis

2. Physiologically Detailed Models • Include detailed cells of many types – Mossy fiber

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

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

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 Model Input – 98% Overlap Output – 68. 4% Overlap

Pattern Separation in Model Input – 98% Overlap Output – 68. 4% Overlap

Pattern Separation: Effect on Input Density • Active Inputs chosen randomly

Pattern Separation: Effect on Input Density • Active Inputs chosen randomly

Experimental Pattern Separation Results Examples • Lesioning dentate gyrus in rats • Human functional

Experimental Pattern Separation Results Examples • Lesioning dentate gyrus in rats • Human functional neuroimaging study • Recording place cells in rat DG and CA 3 when environment is morphed (Leutgeb)

DG and CA 3 Place Cell Changes as Environment is morphed Leutgeb Dentate Gyrus

DG and CA 3 Place Cell Changes as Environment is morphed Leutgeb Dentate Gyrus 1 Model Results CA 3 Leutgeb 2 3 4 5 6 7 Model

Effects of Hilar Lesion Ratzliff et al. 2004 Model

Effects of Hilar Lesion Ratzliff et al. 2004 Model

Takeaways • Review of other types of Models • Model Reproduction of Experimental Results

Takeaways • Review of other types of Models • Model Reproduction of Experimental Results • HIPP and Mossy Cell Activity levels can dynamically regulate pattern separation