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Neural Decoding
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Neural decoding is a neuroscience-related field concerned with the
reconstruction of sensory and other stimuli from information that has already
been encoded and represented in the brain by networks of neurons.
Reconstruction refers to the ability of the researcher to predict what sensory
stimuli the subject is receiving based purely on neuron action potentials.
Therefore, the main goal of neural decoding is to characterize how the electrical
activity of neurons elicit activity and responses in the brain.
This article specifically refers to neural decoding as it pertains to the
mammalian neocortex.
Overview
When looking at a picture, our brains are constantly making decisions about
what object we are looking at, where we need to move our eyes next, and what
we find to be the most salient aspects of the input stimulus. As these images hit
the back of our retina, these stimuli are converted from varying wavelengths to
a series of neural spikes called action potentials. These pattern of action
potentials are different for different objects and different colors; we therefore
say that the neurons are encoding objects and colors by varying their spike rates
or temporal pattern. 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. By this we mean, it is possible to look at spike train data and say
that the person or animal we are recording from is looking at a red ball.
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With the recent breakthrough in large-scale neural recording and decoding
technologies, researchers have begun to crack the neural code and already
provided the first glimpse into the real-time neural code of memory traces as
memory is formed and recalled in the hippocampus, a brain region known to be
central for memory formation. Neuroscientists have initiated large-scale brain
activity mapping or brain decoding project to construct the brain-wide neural
codes.
Encoding to decoding
Implicit about the decoding hypothesis is the assumption that neural spiking in
the brain somehow represents stimuli in the external world. The decoding of
neural data would be impossible if the neurons were firing randomly: nothing
would be represented. This process of decoding neural data forms a loop with
neural encoding. First, the organism must be able to perceive a set of stimuli in
the world - say a picture of a hat. Seeing the stimuli must result in some internal
learning: the encoding stage. 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: the efficient-coding
hypothesis. Now neural decoding is the process of taking these statistical
consistencies, a statistical model of the world, and reproducing the stimuli. This
may map to the process of thinking and acting, which in turn guide what stimuli
we receive, and thus, completing the loop.
In order to build a model of neural spike data, one must both understand how
information is originally stored in the brain and how this information is used at
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a later point in time. 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.
Spatial resolutions
Much of the neural decoding problem depends on the spatial resolution of the
data being collected. The number of neurons needed to reconstruct the stimulus
with reasonable accuracy depends on the means by which data is collected and
the area being recorded. For example, rods and cones (which respond to colors
of small visual areas) in the retina may require more recordings than simple
cells (which respond to orientation of lines) in the primary visual cortex.
Previous recording methods relied on stimulating single neurons over a repeated
series of tests in order to generalize this neuron's behavior. New techniques such
as high-density multi-electrode array recordings and multi-photon calcium
imaging techniques now make it possible to record from upwards of a few
hundred neurons. Even with better recording techniques, the focus of these
recordings must be on an area of the brain that is both manageable and
qualitatively understood. 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. Other studies use spike trains to evaluate the discriminatory ability of
non-visual senses such as rat facial whiskers and the olfactory coding of moth
pheromone receptor neurons.
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Even with ever-improving recording techniques, one will always run into the
limited sampling problem: given a limited number of recording trials, it is
impossible to completely account for the error associated with noisy data
obtained from stochastically functioning neurons (for example, a neuron's
electric potential fluctuates around its resting potential due to a constant influx
and efflux of sodium and potassium ions). Therefore, it is not possible to
perfectly reconstruct a stimulus from spike data. Luckily, even with noisy data,
the stimulus can still be reconstructed within acceptable error bounds.
Temporal resolutions
Timescales and frequencies of stimuli being presented to the observer are also
of importance to decoding the neural code. 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, and the lateral geniculate nucleus, so one would suspect this to be the
appropriate measuring frequency. This has been confirmed in studies that
quantify the responses of neurons in the lateral geniculate nucleus to whitenoise and naturalistic movie stimuli. At the cellular level, spike-timingdependent plasticity operates at millisecond timescales; therefore, models
seeking biological relevance should be able to perform at these temporal scales.
Agent-based decoding
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
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organizes visual perception problem into a hierarchy of interacting nodes
(neurons). The connections between nodes on the same levels and a lower levels
are termed synapses, and their interactions are subsequently learning. Synapse
strengths modulate learning and are altered based on the temporal and spatial
firing of nodes in response to input patterns.
While it is possible to take the firing rates of these modeled neurons, and
transform them into the probabilistic and mathematical frameworks described
above, agent-based models provide the ability to observe the behavior of the
entire population of modeled neurons. Researchers can circumvent the
limitations implicit with lab-based recording techniques. 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.
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