CHAPTER
1 Introduction
Karim G. Oweiss
1.1 BACKGROUND
Have you ever wondered what makes the human brain so unique
compared
to nonhuman brains with similar building blocks when it comes to
the many
complex functions it can undertake, such as instantaneously
recognizing faces,
reading a piece of text, or playing a piano piece with seemingly
very little
effort? Although this long-standing question governs the
founding principles
ofmany areas of neuroscience, the last two decades havewitnessed
a paradigm
shift in the way we seek to answer it, along with many
others.
For over a hundred years, it was believed that profound
understanding of
the neurophysiologicalmechanisms underlying behavior required
monitoring
the activity of the brain’s basic computational unit, the
neuron. Since the
late 1950s, techniques for intra- and extracellular recording of
single-unit
activity have dominated the analysis of brain function because
of perfection in
isolating and characterizing individual neurons’ physiological
and anatomical
characteristics Gesteland et al., 1959; Giacobini et al., 1963;
Evarts, 1968;
Fetz and Finocchio, 1971. Many remarkable findings have
fundamentally
rested on the success of these techniques and have largely
contributed to the
foundation ofmany areas of neuroscience, such as computational
and systems
neuroscience.
Monitoring the activity of a single unit while subjects perform
certain
tasks, however, hardly permits gaining insight into the dynamics
of the underlying
neural system. It is widely accepted that such insight is
contingent
upon the scrutiny of the collective and coordinated activity of
many neural
elements, ranging from single units to small voxels of neural
tissue, many
of which may not be locally observed. Not surprisingly, there
has been a
persistent need to simultaneously monitor the coordinated
activity of these
elements―within and across multiple areas―to gain a better
understanding
Statistical Signal Processing for Neuroscience and
Neurotechnology. DOI: 10.1016B978-0-12-375027-3.00001-6
Copyright . 2010, Elsevier Inc. All rights reserved.
of themechanisms underlying complex functions such as
perception, learning,
and motor processing.
Fulfilling such a need has turned out to be an intricate task
given the
space-time trade-off. As Figure 1.1 illustrates, the activities
of neural elements
can be measured at a variety of temporal and spatial scales.
Action
potentials APs elicited by individual neurons―or spikes―are
typically
1?2ms in duration and are recorded intra- or extracellularly
with penetrating
microelectrodes McNaughton et al., 1983; Gray et al., 1995;
Nicolelis,
1999 or through voltage-sensitive dye-based calcium imaging at
shallow
depths Koester and Sakmann, 2000; Stosiek et al., 2003. These
spike trains
are all-or-none representations of each neuron’s individual
discharge pattern.
Local field potentials LFPs can also be recorded with
penetrating microelectrodes
and are known to represent the synchronized activity of
large
populations of neurons Mitzdorf, 1985. Recent studies suggest
that they
reflect aggregated and sustained local population activity
within～ 250μm of
the recording electrode Katzner et al., 2009, typical of
somato-dendritic currents
Csicsvari et al., 2003. Electrocorticograms ECoGs―also known
as
intracranial EEG iEEG―are intermediately recorded using
subdural electrode
grids implanted through skull penetration and therefore are
considered
semi-invasive as they do not penetrate the blood-brain barrier.
Originally
discovered in the 1950s Penfield and Jasper, 1954, they are
believed to
reflect synchronized post-synaptic potentials aggregated over a
few tens of
100s
1m 10m 100m 1mm 1cm 10cm
1ms 10ms 100ms 1s 10s
Time
Space
LFP
fMRI
ECoG
MEG EEG
Noninvasive
Semi-invasive
Invasive
LFP APs
Skull
ECoG
Scalp
EEG
Dura
Cortex
Action
Potentials
Patch
Clamp
FIGURE 1.1
Spatial and temporal characteristics of neural signals recorded
from the brain. Please see
this figure in color at the companion web site:
www.elsevierdirect.comcompanions
9780123750273
millimeters. Noninvasive techniques include functional magnetic
resonance
imaging fMRI, magnetoencephalography MEG, and
electroencephalography
EEG. The latter signals are recorded with surface electrodes
patched
to the scalp and are thought to represent localized activity in
a few cubic
centimeters of brain tissue, but they are typically smeared out
because of the
skull’s low conductivity effect Mitzdorf, 1985.
Despite the immense number of computing elements and the
inherent difficulty
in acquiring reliable, sustained recordings over prolonged
periods of
time Edell et al., 1992; Turner et al., 1999; Kralik et al.,
2001, information
processing at the individual and population levels remains vital
to understand
the complex adaptationmechanisms inherent inmany brain functions
that can
only be studied at the microscale, particularly those related to
synaptic plasticity
associated with learning and memory formation Ahissar et al.,
1992.
Notwithstanding the existence ofmicrowirebundles since the
1980s, the 1990s
have witnessed some striking advances in solid-state technology
allowing
microfabrication of high-densitymicroelectrode arrays HDMEAs
on single
substrates to be streamlined Drake et al., 1998; Normann et
al., 1999. These
HDMEAs have significantly increased experimental efficiency and
neural
yield see for example Nicolelis 1999 for a review of this
technology. As
a result, a paradigm shift in neural recording techniques has
been witnessed
in the last 15 years that has paved the way for this technology
to become a
building block in a number of emerging clinical
applications.
As depicted in Figure 1.2, substantial progress in invasive
brain surgery,
in parallel with the revolutionary progress in engineering the
devices just
discussed, has fueled a brisk evolution of brain-machine
interfaces BMIs.
Broadly defined, a BMI system provides a direct communication
pathway
between the brain and a man-made device. The latter can be a
simple electrode,
an active circuit on a silicon chip, or even a network of
computers. The
overarching theme of BMIs is the restorationrepair of damaged
sensory, cognitive,
and motor functions such as hearing, sight, memory, and
movement
via direct interactions between an artificial device and the
nervous system.
It may even go beyond simple restoration to conceivably
augmenting these
functions, previously imagined only in the realm of science
fiction.
1.2 MOTIVATION
The remarkable advances in neurophysiology techniques,
engineering
devices, and impending clinical applications have outstripped
the progress
in statistical signal processing theory and algorithms
specifically tailored to:
1 performing large-scale analysis of the immense volumes of
collected
4 CHAPTER 1 Introduction
1958
Penfield publishes The Excitable Cortex in
Conscious Man, summarizing two decades of
work using electrical recording to map the
motor and sensory cortices
1960
Neurosurgeons meeting in
Rome devise ethical
guidelines for research that
uses recording probes in
patients undergoing brain
surgery
1963
Action
potentials
recorded from
single neurons
1987
Deep brain
stimulation
introduced
1988
Studies using microelectrodes
begin to explore key functions,
such as language processing
2005
Advanced data analysis captures
weak signals from multiple single
neurons
2006
First human demonstration of
2D cursor control using
simultaneously recorded single
units in the motor cortex
1941
First recordings deep in
basal ganglia of the human
brain
1934
Electrodes first used during
brain surgery to record from
surface of the cortex
1886
Horsley publishes Brain
Surgery, a detailed
description of operating on
the human brain in the
British medical journal anect
FIGURE 1.2
Timeline of neural recording and stimulation during invasive
human brain surgery.
Source: Modified from Abbott 2009.
neural data; 2 explaining many aspects of natural signal
processing that
characterize the complex interplay between the central and
peripheral nervous
systems; and 3 designing software and hardware architectures
for practical
implementation in clinically viable neuroprosthetic and BMI
systems.
Despite the existence of a growing body of recent literature on
these topics,
there does not exist a comprehensive reference that provides a
unifying
theme among them. This book is intended to exactly fulfill this
need and
is therefore exclusively focused on the most fundamental
statistical signal
processing issues that are often encountered in the analysis of
neural data for
basic and translational neuroscience research. Itwaswrittenwith
the following
objectives in mind:
1. To apply classical and modern statistical signal processing
theory and
techniques to fundamental problems in neural data analysis.
2. To present the latest methods that have been developed to
improve our
understanding of natural signal processing in the central and
peripheral
nervous systems.
3. To demonstrate howthe combined knowledge from the first two
objectives
can help in practical applications of neurotechnology.
1.3 OVERVIEW AND ROADMAP
A genuine attempt was made to make this book comprehensive, with
special
emphasis on signal processing and machine learning techniques
applied to
the analysis of neural data, and less emphasis on modeling
complex brain
functions. Readers interested in the latter topic should refer
to the many
excellent texts on it.1 The sequence of chapters was structured
to mimic
the process that researchers typically follow in the course of
an experiment.
First comes data acquisition and preconditioning, followed by
information
extraction and analysis. Statistical models are then built to
fit experimental
data, and goodness-of-fit is assessed. Finally, the models are
used to design
and build actual systems thatmay provide therapeutic benefits or
augmentative
capabilities to subjects.
In Chapter 2, Oweiss and Aghagolzadeh focus on the joint problem
of
detection, estimation, and classification of neuronal action
potentials in noisy
microelectrode recordings―often referred to as spike detection
and sorting.
1See for example, Dayan and Abbott, Theoretical
Neuroscience:Computational and Mathematical
Modeling of Neural Systems; Koch and Segev, Methods in Neuronal
Modeling: From Ions to Networks;
Izhikevich,Dynamical Systems in Neuroscience:The Geometry of
Excitabilityand Bursting;
and Rieke,Warland, van Steveninck and Bialek: Spikes: Exploring
the Neural Code.
6 CHAPTER 1 Introduction
The importance of this problem stems from the fact that its
outcome affects
virtually all subsequent analysis. In the absence of a clear
consensus in the
community on what constitutes the best method, spike detection
and sorting
have been and will continue to be a subject of intense research
because
techniques for multiunit recordings have started to emerge. The
chapter provides
an in-depth presentation of the fundamentals of detection and
estimation
theory as applied to this problem. It then offers an overview of
traditional
and novel methods that revolve around the theory, in particular
contrasting
the differences―and potential benefits―that arise when detecting
and sorting
spikes with a single-channel versus a multi-channel recording
device.
The authors further link multiple aspects of classic and modern
signal processing
techniques to the unique challenges encountered in the
extracellular
neural recording environment. Finally, they provide a practical
way to performthis
task using a computationally efficient, hardware-optimized
platform
suitable for real-time implementation in neuroprosthetic devices
and BMI
applications.
In Chapter 3, Johnson provides an overview of classical
information theory,
rooted in the 1940s pioneeringwork of Claude Shannon Shannon,
1948,
as applied to the analysis of neural coding once spike trains
are extracted
from the recorded data. He offers an in-depth analysis of how to
quantify the
degree to which neurons can individually or collectively process
information
about external stimuli and can encode this information in their
output spike
trains. Johnson points out that extreme care must be exercised
when analyzing
neural systems with classical information-theoretic quantities
such as
entropy. This is because little is known about how non-Poisson
communication
channels that often describe neuronal discharge patterns provide
optimal
performance bounds on stimulus coding. The limits of classical
information
theory as applied to information processing by spiking neurons
are discussed
as well. Finally, Johnson provides some interesting thoughts on
post-Shannon
information theory to address the more puzzling questions of how
stimulus
processing by some parts of the brain results in conveying
useful information
to other parts, thereby triggering meaning ful actions rather
than just communicating
signals between the input and the output of a communication
system―the hallmark of Shannon’s pioneering work.
InChapter 4,Song andBerger focus on the systemidentification
problem―
that is, determining the input-output relationship in the case
of a multi-input,
multi-output MIMO system of spiking neurons. They first
describe a nonlinear
multiple-input, single-output MISO neuron model consisting of
a
number of components that transform the neuron’s input spike
trains into
synaptic potentials and then feed back the neuron’s output
spikes through nonlinearities
to generate spike-triggered after-potential contaminated by
noise
to capture system uncertainty. Next they describe how this MISO
model is
combined with similar models to predict the MIMO transformation
taking
place in the hippocampus CA3-CA1 regions, both known to play a
central
role in declarative memory formation. Song and Berger suggest
that such a
model can serve as a computational framework for the development
of memory
prostheseswhich replaces damaged hippocampus circuitry. As
ameans to
bypass a damaged region, they demonstrate the utility of their
MIMO model
to predict output spike trains from the CA1 region using input
spike trains to
the CA3 region. They point out that the use of hidden variables
to represent
the internal states of the system allows simultaneous estimation
of all model
parameters directly from the input and output spike trains. They
conclude
by suggesting extensions of their work to include nonstationary
input-output
transformations that are known to take place as a result of
cortical plasticity―
for example during learning newtasks―a feature not captured by
their current
approach.
In Chapter 5, Eldawlatly andOweiss take a more general approach
to identifying
distributed neural circuits by inferring connectivity between
neurons
locally within and globally acrossmultiple brain areas,
distinguishing between
two types of connectivity: functional and effective. They first
review techniques
that have been classically used to infer connectivity between
various
brain regions fromcontinuous-time signals such as fMRI
andEEGdata.Given
that inferring connectivity among neurons is more challenging
because of the
stochastic and discrete nature of their spike trains and the
large dimensionality
of the neural space, the authors provide an in-depthfocus on
this problem using
graphical techniques deeply rooted in statistics and machine
learning. They
demonstrate that graphical models offer a number of advantages
over other
techniques, for example by distinguishing between mono-synaptic
and polysynaptic
connections and in inferring inhibitory connections among
other
features that existing methods cannot capture. The authors
demonstrate the
application of their method in the analysis of neural activity
in the medial
prefrontal cortex mPFC of an awake, behaving rat performing a
working
memory task. Their results demonstrate that the networks
inferred for similar
behaviors are strongly consistent and exhibit graded transition
between their
dynamic states during the recall process, thereby providing
additional evidence
in support of the long withstanding Hebbian cell assembly
hypothesis
Hebb, 1949.
In Chapter 6, Chen, Barbieri, and Brown discuss the application
of a
subclass of graphical models―the state-space models―to the
analysis of
neural spike trains and behavioral learning data. They first
formulate point
process and binary observation models for the data and review
the framework
of filtering and smoothing under the state space paradigm. They
provide a
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