| | Models | Description |
| 1. |
Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex
|
|
|
See https://inm-6.github.io/multi-area-model/ for any updates. |
| 2. |
Self-organization of cortical areas in development and evolution of neocortex (Imam & Finlay 2021)
|
|
|
"Using physical parameters representing primary and secondary visual areas as they vary from monkey to mouse, we derived a network growth model to explore if characteristic features of secondary areas could be produced from correlated activity patterns arising from V1 alone." |
| 3. |
Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021)
|
|
|
This work proposes a substrate for learned sequential representations, via a network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a biophysically realistic learning rule based on “eligibility traces”, which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. |
| 4. |
Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997)
|
|
|
This archive contains 3 mod files for NEURON that implement the short term
synaptic plasticity model described in
Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R.,
and Nelson, S.B..
A quantitative description of short-term plasticity at
excitatory synapses in layer 2/3 of rat primary visual cortex.
Journal of Neuroscience 17:7926-7940, 1997.
Contact ted.carnevale@yale.edu if you have questions
about this implementation of the model. |
| 5. |
Switching circuit for optimal context integration during static + moving contexts (Voina et al 2022)
|
|
|
The brain processes information at all times and much of that information is context-dependent.The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1). We find that relatively few “switching units” — akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population — are sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures. |
| 6. |
Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)
|
|
|
Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000
The original files for this model are located at
the web site http://www-lnc.usc.edu/~karchie/synmap |
| 7. |
Visual Cortex Neurons: Dendritic study (Anderson et al 1999)
|
|
|
Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999 |
