### Sparse Learning Recall Networks

Recall-based functions are classically indicative of a mirror neuron system in which each approximation of the neural representation remains equally utilized, functioning as a load balancing mechanism. Commonly attributed to the preemptive execution of a planned task, the retention of memory in mirror neural systems tends to be modular in persistence and metaphysical in nature. Sparse neural systems interpret signals from cortical portions of the brain, allowing learned behaviors from multiple portions of the brain to execute simultaneously as observed in Fink’s studies on cerebral memory structures. It is theorized that the schematic representation of memory in these portions of the brain exists in memory fields only after a number of transformations have occurred in response to the incoming stimulus. Within these transformations lies the inherent differentiating factor in functional learning behavior: specifically, those which cause the flawed memory functions in the patients of such mental illnesses.

#### Semantic Learning Transformation

Now, similar to my fluid intelligence paper, we will need to semantically represent all types of ideas in a way that most directly allows for future transformations and biases to be included. For this, we will use a mutated version of the semantic lexical transformations.

The transformation of raw stimulus, in this case a verbal and unstructured story-like input, to a recall-able and normalized memory field will be simulated by a spatial transformer network. These mutations in raw input are the inherent reason for differentiated recall mechanisms between all humans. An altered version of the spatial transformer network, as developed in \cite{JaderbergSpatialNetworks} in Google’s Deepmind initiative, will be used to explicitly allow the spatial manipulation of data within the neural stack. Recall gradients mapped from our specialized network find their activation complexes similar to that of the prefrontal cortex in the brain,

An altered version of the spatial transformer network, as developed in Google’s Deepmind initiative, will be used to explicitly allow the spatial manipulation of data within the neural stack. Recall gradients mapped from our specialized network find their activation complexes similar to that of the prefrontal cortex in the brain, tasked with directing and encoding raw stimulus.

##### The Spatial Transformer Network (Unsupervised)

Originally designed for pixel transformations inside a neural network, the sampling grid or the input feature map will be parameterized to fit the translational needs of comprehension. The formulation of such a network will incorporate an elastic set of spatial transformers, each with a localisation network and a grid generator. Together, these will function as the receptive fields interfacing with the hypercolumns.

Now these transformer networks allowed us to parameterize any type of raw stimulus to be parsed and propagated through a more abstracted and generalized network capable of modulating fluid outputs.

The localisation network will take a mutated input feature map of $U\in { \textbf{R}}^{ { H }_{ i }\times { W }_{ i }\times { C }_{ i } }$, with width $W$, height $H$, channels $C$ and outputs ${\theta }_{i }$. $i$ represents a differentiated gradient-dimensional resultant prioritized for storage in the stack. This net feature map allows the convolution of learned transformations to a neural stack in a compartmentalized system. A key characteristic of this modular transformation, as noted in Jaderberg’s spatial networks, is that the parameters of the transformations in the input feature map, as the size of $\theta$, can vary depending on the transformation type. This allows the sparse network to easily retain the elasticity needed to react to any type of stimulus, giving opportunity for compartmentalized learning space. The net dimensionality of the transformation ${ \tau }_{ \theta }$ on the feature map can be represented: $\theta ={ f }_{ loc }\left( x \right)$. In any case, the ${ f }_{ loc }\left( \right)$ can take any form, especially that of a learning network. For example, for a simple laplace transform, $\theta$ will assume a 6-dimensional position, and ${ f }_{ loc }\left( \right)$ will take the form of a convolutional network or a fully connected network (\cite{AndrewsIntegratingRepresentations}). The form of ${ f }_{ loc }\left( \right)$ is unbounded and nonrestrictive in domain, allowing all forms of memory persistence to coexist in the spatial stack.