Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in the brain by networks of neurons.
[1] Reconstruction refers to the ability of the researcher to predict what sensory stimuli the subject is receiving based purely on neuron action potentials.
When looking at a picture, people's brains are constantly making decisions about what object they are looking at, where they need to move their eyes next, and what they find to be the most salient aspects of the input stimulus.
As these images hit the back of the retina, these stimuli are converted from varying wavelengths to a series of neural spikes called action potentials.
Now, if someone were to probe the brain by placing electrodes in the primary visual cortex, they may find what appears to be random electrical activity.
These neurons are actually firing in response to the lower level features of visual input, possibly the edges of a picture frame.
This highlights the crux of the neural decoding hypothesis: that it is possible to reconstruct a stimulus from the response of the ensemble of neurons that represent it.
In other words, it is possible to look at spike train data and say that the person or animal being recorded is looking at a red ball.
Implicit about the decoding hypothesis is the assumption that neural spiking in the brain somehow represents stimuli in the external world.
After varying the range of stimuli that is presented to the observer, we expect the neurons to adapt to the statistical properties of the signals, encoding those that occur most frequently:[6] the efficient-coding hypothesis.
This may map to the process of thinking and acting, which in turn guide what stimuli we receive, and thus, completing the loop.
This neural coding and decoding loop is a symbiotic relationship and the crux of the brain's learning algorithm.
Furthermore, the processes that underlie neural decoding and encoding are very tightly coupled and may lead to varying levels of representative ability.
The number of neurons needed to reconstruct the stimulus with reasonable accuracy depends on the means by which data is collected and the area which is being recorded.
Many studies look at spike train data gathered from the ganglion cells in the retina, since this area has the benefits of being strictly feedforward, retinotopic, and amenable to current recording granularities.
The duration, intensity, and location of the stimulus can be controlled to sample, for example, a particular subset of ganglion cells within a structure of the visual system.
[10] Other studies use spike trains to evaluate the discriminatory ability of non-visual senses such as rat facial whiskers[11] and the olfactory coding of moth pheromone receptor neurons.
Quicker timescales and higher frequencies demand faster and more precise responses in neural spike data.
In humans, millisecond precision has been observed throughout the visual cortex, the retina,[14] and the lateral geniculate nucleus.
This has been confirmed in studies that quantify the responses of neurons in the lateral geniculate nucleus to white-noise and naturalistic movie stimuli.
defines an ensemble of signals, and represents the likelihood of seeing a stimulus in the world based on previous experience.
; the traditional approach to calculating this probability distribution has been to fix the stimulus and examine the responses of the neuron.
In spike train number coding, each stimulus is represented by a unique firing rate across the sampled neurons.
Adding a small temporal component results in the spike timing coding strategy.
Here, the main quantity measured is the number of spikes that occur within a predefined window of time T. This method adds another dimension to the previous.
Another description of neural spike train data uses the Ising model borrowed from the physics of magnetic spins.
is the partition function In addition to the probabilistic approach, agent-based models exist that capture the spatial dynamics of the neural system under scrutiny.
One such model is hierarchical temporal memory, which is a machine learning framework that organizes the visual perception problem into a hierarchy of interacting nodes (neurons).
Synapse strengths modulate learning and are altered based on the temporal and spatial firing of nodes in response to input patterns.
Because this approach does rely on modeling biological systems, error arises in the assumptions made by the researcher and in the data used in parameter estimation.