  A major area in neuroscience research is the study of how the brain processes
spatial information. Neurons in the brain represent external stimuli via neural
codes. These codes often arise from stereotyped stimulus-response maps,
associating to each neuron a convex receptive field. An important problem
consists in determining what stimulus space features can be extracted directly
from a neural code. The neural ideal is an algebraic object that encodes the
full combinatorial data of a neural code. This ideal can be expressed in a
canonical form that directly translates to a minimal description of the
receptive field structure intrinsic to the code. In here, we describe a
SageMath package that contains several algorithms related to the canonical form
of a neural ideal.
