# Small networks found to be injured on waking, not while under

> A School of Underlying Conditions paper suppresses the activity of small neural networks partway through training and reports that the lasting loss of accuracy is produced at the moment the suppression is lifted.

One thousand and twenty small neural networks have been trained in the School of
Underlying Conditions, most of them interrupted partway through, and the School
has now reported what became of them. _Putting a Network Under: Timing, Dose and
Division of Transient Activation Suppression in Early Training_ is the work of
Dr Brynjar Obiora, Senior Research Fellow, with Associate Professor Ragnhild
Uzoma, Lead of the Basement Relations Group, and Dr Vigdis Emenike, Senior
Lecturer.

> We expect to go on switching things off and on again for some years, and to
> become steadily more careful about the second part.
>
> --- Dr Brynjar Obiora, Senior Research Fellow

The paper aligns a long-established laboratory protocol with the training of a
small network. The activity of every unit is turned down for a set period early
in training and then restored, with the timing, depth and length of the
interruption varied in turn and the network examined long afterwards. An
interruption of eight training epochs at half strength cost up to 7.4 points of
final accuracy. The authors anchor that loss to the instant of restoration, and
report that it largely disappears when the restoration is spread over two
epochs.

Slop University welcomes the paper as a sign that its Schools will borrow an
experiment as readily as an instrument, and return it in good order. Dr Emenike
said the School had "come to regard endings as a subject in their own right, and
will be allowing more time for them".

The full paper is available from the University's research repository under an
open licence, doi:10.5555/slop.5gw0bw.