While reading David Field’s seminal paper “What is the
role of sensory coding?”, I became aware of compelling evidence supporting the
hypothesis that our cortex represents sensory information using a ‘sparse’
code. Sparse coding has its roots in information theory and relates to the
optimal coding principle whereby a system’s aim is to represent a signal using
as few ‘bits’ as possible. In an optimal code, each bit must be as independent
as possible from other bits since any information carried simultaneously by two
bits is redundant and decreases the optimality of the code. In the context of
Neuroscience, this translates to our cortex using as few neurons as possible to
encode sensory events, with active neurons carrying mutually independent
information. Several findings substantiate this claim, but perhaps the most
remarkable one comes from computer simulation using unsupervised
feature-learning algorithms. Similarly to our sensory cortices, the aim of such
algorithms is to learn a representational basis to encode sensory stimuli.
Simulations have shown that when these algorithms implement sparse coding
strategies (i.e., when they are forced to use as few elements as possible to
represent a stimulus), they develop ‘receptive fields’ that are strikingly
similar to those of neurons in our cortex. The close correspondence between
simulation and biology indicates that our cortex might indeed perform a sparse
encoding of sensory information.
Towards the end of his paper, Field mentions several
interesting reasons why a sparse code might be beneficial. However, I failed to
develop a personal intuition as to why our cortex might implement such a sparse
code. After finishing the paper, I looked up from my computer and I gazed
through the window. At this moment, I saw a gigantic object appear in front of
me. It was a very complex stimulus that filled up my whole visual field. It was
made of hundreds, if not thousands, of small moving patches each containing
infinite details. Yet, however intricate this stimulus was, a single percept
came to my mind and I thought: “a tree”. It is at this point that I realized
what the main advantage of sparse coding is: it summarizes our sensory environment to essential features. I then
got up and walked around the Institute. As I looked around me, objects emerged
from their background. I didn’t see keys, letters, buttons and cables but a
computer; nor did I see a detailed fabric, wheels and armrests, but simply a
chair. I could almost feel my cortex process visual information. It was
efficiently summarizing my sensory environment so that I only perceived
large-scale objects. In other words, my cortex was sparsely encoding my
environment: with just a few concepts (‘chair’, ‘computer’, ‘desk’) it
described the information that my retina perceived through millions of
photoreceptors.
Is this then the role of sensory coding? To sparsely
represent sensory information such that relevant components are readily
accessible? One of the purposes of cortical circuitry should then be to guide
the development of sensory neurons’ receptive field so that each captures the
maximum statistical structure in the environment. During perception, neurons
should compete so that solely those that carry the most information about a
sensory stimulus are allowed to be active. In this way, only a few active
neurons carrying mutually independent information can accurately summarize a
complex sensory environment.
- R. Holca
