  Elements of neural networks, both biological and artificial, can be described
by their selectivity for specific cognitive features. Understanding these
features is important for understanding the inner workings of neural networks.
For a living system, such as a neuron, whose response to a stimulus is unknown
and not differentiable, the only way to reveal these features is through a
feedback loop that exposes it to a large set of different stimuli. The
properties of these stimuli should be varied iteratively in order to maximize
the neuronal response. To utilize this feedback loop for a biological neural
network, it is important to run it quickly and efficiently in order to reach
the stimuli that maximizes certain neurons' activation with the least number of
iterations possible. Here we present a framework with an efficient design for
such a loop. We successfully tested it on an artificial spiking neural network
(SNN), which is a model that simulates the asynchronous spiking activity of
neurons in living brains. Our optimization method for activation maximization
is based on the low-rank Tensor Train decomposition of the discrete activation
function. The optimization space is the latent parameter space of images
generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the
first time that effective AM has been applied to SNNs. We track changes in the
optimal stimuli for artificial neurons during training and show that highly
selective neurons can form already in the early epochs of training and in the
early layers of a convolutional spiking network. This formation of refined
optimal stimuli is associated with an increase in classification accuracy. Some
neurons, especially in the deeper layers, may gradually change the concepts
they are selective for during learning, potentially explaining their importance
for model performance.
