Recurrent Memory

This repository contains the code for reproducing the results reported in the following paper:

Orhan AE, Ma WJ (2019) A diverse range of factors affect the nature of neural representations underlying short-term memory. Nature Neuroscience, 22, 275–283.

The code is written in Theano (0.8.2) + Lasagne (0.2.dev1). The code was originally run on a local computer cluster. If you are interested in running the following experiments on a cluster, I have some simple shell scripts that can facilitate this. Please contact me about this or about any other questions or concerns. You can find my contact information on my web page.

Experiments

As described in the paper, there are six main experimental conditions.

To run experiments in the basic condition:
```
python run_basic_expts.py --task 0 --model 0 --lambda_val 0.98 --sigma_val 0.0 --rho_val 0.0
```
where task is the integer code for the task, model is the integer code for the model, lambda_val is the value of the lambda_0 hyper-parameter, sigma_val is the value of the sigma_0 hyper-parameter divided by sqrt(N) (where N=500 in all simulations), and rho_val is the value of the rho hyper-parameter in the paper. For the tasks reported in the paper, use the following integer codes for task: DE-1 (0), DE-2 (1), CD (2), GDE (4), 2AFC (6), Sine (7), COMP (8).
To run experiments in the Hebbian synaptic plasticity condition:
```
python run_basic_expts.py --task 0 --model 1 --lambda_val 0.98 --sigma_val 0.0 --rho_val 0.0
```
i.e. set the model argument to 1. This uses the model with fast weights as described in the paper.

To run experiments in the tethered condition:

python run_tethered_expts.py --task 0 --model 0 --lambda_val 0.98 --sigma_val 0.0 --rho_val 0.0

To run experiments in the dynamic input condition:

python run_dynamic_expts.py --task 0 --model 0 --lambda_val 0.98 --sigma_val 0.0 --rho_val 0.0

To run experiments in the variable delay duration condition:

python run_vardelay_expts.py --lambda_val 0.98 --sigma_val 0.0 --rho_val 0.0

The directory multitask contains files pertaining to the multitask training condition.

Analysis

utils.py contains the function compute_SI that demonstrates how to compute the mean sequentiality index (SI) for a batch of trials as described in the paper.