of 31
Nanotools for Neuroscience and Brain Activity Mapping
A. Paul Alivisatos
,
Anne M. Andrews
‡,§
,
Edward S. Boyden
,
Miyoung Chun
||
,
George M.
Church
#
,
Karl Deisseroth
¶,
,
John P. Donoghue
,
Scott E. Fraser
,
Jennifer Lippincott-
Schwartz
,
Loren L. Looger
,
Sotiris Masmanidis
‡,
,*
,
Paul L. McEuen
,
Arto V. Nurmikko
,
Hongkun Park
,
Darcy S. Peterka
,
Clay Reid
††
,
Michael L. Roukes
‡‡,§§
,
Axel
Scherer
‡‡,
⊥⊥
,*
,
Mark Schnitzer
¶,||||
,
Terrence J. Sejnowski
▴▴
,
Kenneth L. Shepard
##
,
Doris
Tsao
¶¶
,
Gina Turrigiano
□□
,
Paul S. Weiss
‡,
▪▪
,*
,
Chris Xu
○○
,
Rafael Yuste
,
●●
,*
, and
Xiaowei
Zhuang
▵▵
Department of Chemistry, University of California, Berkeley, California 94720, and Lawrence
Berkeley Laboratory, Berkeley, California 94720-1460
California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California
90095
§
Department of Psychiatry, and Semel Institute for Neuroscience & Human Behavior, Department
of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, California 90095
Media Laboratory, Department of Biological Engineering, Brain and Cognitive Sciences, and
McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
||
The Kavli Foundation, Oxnard, California 93030
#
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, Wyss Institute
for Biologically Inspired Engineering and Biophysics Program, Harvard University, Boston,
Massachusetts 02115
Howard Hughes Medical Institute, Stanford University, Stanford California 94305
Departments of Bioengineering and Psychiatry, Stanford University, Stanford California 94305
Department of Neuroscience, Division of Engineering, Department of Computer Science, Brown
University, Providence, Rhode Island 02912
Departments of Biological Sciences, Biomedical Engineering, Physiology and Biophysics, Stem
Cell Biology and Regenerative Medicine, and Pediatrics, Radiology and Ophthalmology,
University of Southern California, Los Angeles, California 90089
Cell Biology and Metabolism Program, Eunice Kennedy Shriver National Institute of Child Health
and Human Development, National Institutes of Health, Bethesda, Maryland 20892
Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia 20147
Department of Neurobiology, University of California, Los Angeles, California 90095
Department of Physics, Laboratory of Atomic and Solid State Physics, and Kavli Institute at
Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853
Department of Physics and Division of Engineering, Brown University, Providence, Rhode Island
02912
© 2013 American Chemical Society
*
Address correspondence to smasmanidis@ucla.edu, etcher@caltech.edu, psw@cnsi.ucla.edu, rmy5@columbia.edu.
Conflict of Interest:
The authors declare no competing financial interest.
Published as:
ACS Nano
. 2013 March 26; 7(3): 1850–1866.
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Department of Chemistry and Chemical Biology and Department of Physics, Harvard University,
Cambridge, Massachusetts 02138
Howard Hughes Medical Institute and Department of Biological Sciences, Columbia University,
New York, New York 10027
††
Allen Institute for Brain Science, Seattle, Washington 98103
‡‡
Kavli Nanoscience Institute, California Institute of Technology, MC 149-33, Pasadena,
California 91125
§§
Departments of Physics, Applied Physics, and Bioengineering, California Institute of
Technology, MC 149-33, Pasadena, California 91125
⊥⊥
Departments of Electrical Engineering, Applied Physics, and Physics, California Institute of
Technology, MC 149-33, Pasadena, California 91125
||||
Departments of Applied Physics and Biology, James H. Clark Center, Stanford University,
Stanford, California 94305
▴▴
Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute, La
Jolla, California 92037, and Division of Biological Sciences, University of California, San Diego,
La Jolla, California 92093
##
Department of Electrical Engineering, Columbia University, New York, New York 10027
¶¶
Division of Biology, California Institute of Technology, Pasadena, California 91125
□□
Department of Biology and Center for Complex Systems, Brandeis University, Waltham,
Massachusetts 02254
▪▪
Department of Chemistry & Biochemistry, Department of Materials Science & Engineering,
University of California, Los Angeles, California 90095
○○
School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853
●●
Kavli Institute for Brain Science, Columbia University, New York, New York 10027
▵▵
Howard Hughes Medical Institute, Departments of Chemistry and Chemical Biology and
Physics, Harvard University, Cambridge, Massachusetts 02138
Abstract
Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural
interactions and circuits. At the same time, there exist few general theories or principles that
explain brain function. We attribute this disparity, in part, to limitations in current methodologies.
Traditional neurophysiological approaches record the activities of one neuron or a few neurons at
a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing
realization that neural circuits operate at emergent levels, where the interactions between hundreds
or thousands of neurons, utilizing multiple chemical transmitters, generate functional states.
Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as
well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to
explore brain function by enabling simultaneous measurement and manipulation of activity of
thousands or even millions of neurons. We and others refer to this goal as the Brain Activity
Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis
tools and in the design and synthesis of nanomaterials have generated optical, electrical, and
chemical methods that can readily be adapted for use in neuroscience. These approaches represent
exciting areas of technical development and research. Moreover, unique opportunities exist for
nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to
tackling the challenging problems involved in understanding the fundamentals of brain function.
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The Brain Activity Mapping (BAM) Project
1–5
has three goals in terms of building tools for
neuroscience capable of (1) measuring the activity of large sets of neurons in complex brain
circuits, (2) computationally analyzing and modeling these brain circuits, and (3) testing
these models by manipulating the activities of chosen sets of neurons in these brain circuits.
As described below, many different approaches can, and likely will, be taken to achieve
these goals as neural circuits of increasing size and complexity are studied and probed.
The BAM project will focus both on dynamic voltage activity and on chemical
neurotransmission. With an estimated 85 billion neurons, 100 trillion synapses, and 100
chemical neurotransmitters in the human brain,
6
this is a daunting task. Thus, the BAM
project will start with model organisms, neural circuits (
vide infra
), and small subsets of
specific neural circuits in humans.
Among the approaches that show promise for the required dynamic, parallel measurements
are optical and electro-optical methods that can be used to sense neural cell activity such as
Ca
2+
,
7
voltage,
8–10
and (already some) neurotransmitters;
11
electrophysiological approaches
that sense voltages and some electrochemically active neurotransmitters;
12–17
next-
generation photonics-based probes with multifunctional capabilities;
18
synthetic biology
approaches for recording histories of function;
19–21
and nanoelectronic measurements of
voltage and local brain chemistry.
22–39
We anticipate that tools developed will also be
applied to glia and more broadly to nanoscale and microscale monitoring of metabolic
processes.
Entirely new tools will ultimately be required both to study neurons and neural circuits with
minimal perturbation and to study the human brain. These tools might include “smart”,
active nanoscale devices embedded within the brain that report on neural circuit activity
wirelessly and/or entirely new modalities of remote sensing of neural circuit dynamics from
outside the body. Remarkable advances in nanoscience and nanotechnology thus have key
roles to play in transduction, reporting, power, and communications.
One of the ultimate goals of the BAM project is that the knowledge acquired and tools
developed will prove useful in the intervention and treatment of a wide variety of diseases of
the brain, including depression, epilepsy, Parkinson’s, schizophrenia, and others. We note
that tens of thousands of patients have already been treated with invasive (
i.e.
, through the
skull) treatments. While we hope to reduce the need for such measures, greatly improved
and more robust interfaces to the brain would impact effectiveness and longevity where such
treatments remain necessary.
Neuroscience at a Crossroads
Understanding how the brain works is one of the greatest challenges facing science and
engineering. After more than a century of sustained progress in biological sciences and
medicine, one could argue that mankind has made significant advances in our understanding
of how biological systems operate and how different parts of the body function and, when
damaged, generate disease. At the same time, a comprehensive understanding of the brain
remains an elusive, distant frontier. To arrive at a general theory of brain function would be
an historic event, comparable to inferring quantum theory from huge sets of complex spectra
and inferring evolutionary theory from vast biological field work. Not only would a theory
of brain function be a fundamental advance in biology, but it would enable understanding of
the pathophysiology of neurological and neuropsychiatric diseases. Many of these
devastating brain-based pathologies have neither cures nor effective treatments, in large part
because it is difficult to provide a treatment for a dysfunctional organ when one does not
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know how it works. Finally, the historic importance of generating a theory of brain function
is highlighted by the fact that as humans, more than any other species, we are defined by the
higher cognitive abilities generated by our brains. Thus, scientific understanding of our
brains will enable deeper knowledge of ourselves and of our minds.
Neuroscientists have worked on this key problem for the last century, and yet a
comprehensive theory of brain function remains elusive. Enormous progress has been made
in understanding the molecular and cellular components of neural circuits in humans and
experimental animals. One goal to this end is to drive the development and testing of
theories of brain function that require better spatial and temporal sampling and intervention
than is presently possible. Greater precision and parallelism in electrical and chemical
sensing, as well as the ability to excite and to probe neural circuits actively, as proposed
here, would bridge the nanoscale to the microscale to the macroscale and would complement
ongoing connectional mapping of brain circuits.
Need for High-Resolution, Network-Level Brain Activity Mapping
Approaches
There has been remarkable progress in the ability to ascribe specific functional roles to
specific neuroanatomical regions, axonal tracts, cells, synapses, and molecules. For
example, large-scale maps of gene expression in the brain, such as the Allen Brain Atlas
40
or GENSAT Project
41
provide enormous insight into the brain’s architecture at the genetic
level with precise anatomical resolution. However, no comparably high-resolution maps of
brain-wide neuronal activity are available. On one hand, noninvasive mapping techniques
such as functional magnetic resonance imaging (fMRI), positron emission tomography
(PET), and electroencephalography (EEG) reveal a wealth of information about functional
brain organization and connectivity.
42–44
These methods offer coarse-grain views that do
not fully capture the underlying networks’ properties. On the other hand, our ability to
perceive and to ponder the cosmos, to remember information, to feel pleasure from daily
experiences, to make decisions—and deficits in performing some of these tasks when faced
with disease—involves a complex interplay of large, distributed neuronal populations
signaling on millisecond time scales. Science has barely scratched the surface of this fast,
network-level regime. The huge potential payoff for understanding the brain and diagnosing
and treating neurological disorders means that techniques for measuring brain activity are
scaling up at a rapid pace.
45
Thanks in part to these advances, the development of technology to enable a paradigm shift
from experiments that routinely record tens of neurons at a time to experiments that can
record millions of cells is important and timely. From a computational perspective, it is
obvious that information processing in the brain relies on a cascade of events,
46
and
sampling these events a few cells at a time, as has been the norm in neuroscience, cannot
capture the emergent properties of such a deeply interconnected network as the brain. The
search for spatiotemporal patterns and correlations in spike trains of recorded neurons can be
enormously enhanced by raising the number (
N
) of simultaneously accessible units.
22,47
For
example, assuming a uniform connection probability, the likelihood of finding synaptically
coupled cells increases quadratically with
N
. Testing the functional implications of small-
world models of interacting neural networks
48
would likewise benefit from having access to
greater
N
. Molecular-level analyses of cellular organization reveal the immense
heterogeneity of neuronal subpopulations. Many of these subpopulations—such as
cholinergic interneurons in the striatum
49
—represent only a small fraction of cells in a given
area, yet they are known to play important roles in regulating behavior.
50
This suggests that
in order to sample several of these rare but important units reliably, so as to understand their
function
in vivo
, large-scale measurements of neuronal activity are necessary. Further,
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because the brain is composed of many specialized neuron types that perform specific
functions within microcircuits, techniques must be developed that enable both the identities
and activities of neurons to be measured.
In spite of this progress, why does neuroscience still lack a general theory? One contributor
is the sheer complexity of brain circuits. Even in the simplest organisms, nervous systems
are composed of circuits built with many different subtypes of neurons connected in patterns
of prohibitive complexity. These “impenetrable jungles where many investigators have lost
themselves”, as Ramon y Cajal, one of the earliest neuroanatomists termed them,
51
have
difficult experimental access, and the sheer diversity of neural circuits and their components
makes it difficult to draw strong conclusions or generalizations on which to build general
theories. In fact, neuroscientists have traditionally analyzed the structure and function of
these circuits one neuron at a time, using electrical recordings from individual neurons, for
example, while an experimental animal is performing a specific behavior. At the same time,
any neural circuit is composed of thousands or hundreds of thousands of neurons, which are
heavily interconnected. Because of these structures, it is likely that neural circuits operate at
an emergent level, one generated by the functional interactions between large populations of
neurons. Thus, measurements from individual neurons would not give insight into function,
just as one cannot understand the function of a building by analyzing the molecular
structures of its bricks. In fact, emergent properties have been encountered in many areas of
science and engineering. The laws of thermodynamics, statistical mechanics, and the
generation of magnetic properties are examples of fields of science that require
understanding of emergent phenomena that result from interactions across many individual
particles. The goal of the BAM project is to provide the data sets and the critical tests to
enable the development and testing, respectively, of theories and models of neural circuits
and brain function.
To elucidate emergent properties, neuroscientists will need novel techniques that enable
simultaneous monitoring of the activities of many or all of the cells in neural circuits. While
whole-brain imaging techniques, such as fMRI, enable bird’s eye views of the activity of
brain areas, they lack the spatial and temporal resolution required to provide functional
information on individual neurons and their interactions. New techniques are needed, and
while neuroscientists are generating many novel approaches, we believe that nanoscience
and nanotechnology are ideally poised to make fundamental contributions to this problem
and to help generate the toolkits of methods that could be used to measure and to manipulate
the activities of increasingly larger sets of neurons in complex and widespread neural
circuits.
The Nanoscience and Nanotechnology Revolution
The nanoscience and nanotechnology revolution began with the ability to “see” at the atomic
scale with the inventions of the scanning tunneling microscope, the atomic force
microscope, and related tools.
52–55
It then progressed with the ability to manipulate
individual atoms and molecules, as well as to direct assemblies of molecules into precise
structures.
56–60
In the years since, remarkable progress has been made in developing novel materials, tools,
and methods that have opened up new possibilities across science, engineering, and
medicine. Some progress has already been made toward addressing problems in
neuroscience
via
nanotechnology.
In the past decade, substantial investments have been made through the National
Nanotechnology Initiative in the United States and similar programs in countries around the
world. Support continues in the hope that the dramatic advances we have seen in
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nanoscience and nanotechnology will continue and will now be applied to other fields of
science, engineering, and medicine, as well as to manufacturing and commercialization.
61
Top-Down (Lithographic) Devices
Miniaturization through lithographic and other means has been a continuing trend fueled
largely by the need to develop ever more functional systems. Over the past decades, these
systems have catered to the needs of consumer electronics, with the opportunities of using
microchips to control and to interpret information on massive scales. More recently, this
trend has been driven by our need to be connected and to communicate, leading to more
portable systems in which size, weight, and power are at a premium. The same trend that
enables the miniaturization of electronic and radio frequency communications systems has
also influenced optical and fluidic systems, with the emergence of printed silicon photonics
and microfluidics that can decrease the sizes of lasers, modulators, and detectors, as well as
pumps, valves, and mixers. As a general rule, the size of printed systems has been reduced
by a factor of 100 × in volume every 10 years (Bell’s Law).
62
These reductions in system
size correspond to improvements in the fidelity of lithographic processes, enabling the
geometric doubling in the number of individual devices on a chip every 18 months (Moore’s
Law).
63
It could be argued that Moore’s Law is driven by the “real-estate” value on the chip,
which in the case of silicon electronics has remained constant over the past 40 years, at a
cost of approximately $5/cm
2
.
Miniaturization in electronic devices has led to the ability to create structures with 22 nm
lateral width over 300 mm wafers, produced on commercial scales at approximately 20
wafers/h. Individual transistors are now 200 nm in size, and amplification circuits are on the
order of micrometers. For the specific application of studying the brain, it is now possible to
contemplate using these capabilities to increase the numbers of neurons interrogated by
reducing the sizes of electrophysiological probes and to develop systems that can manage
large amounts of data accumulated and/or transmitted during such measurements. This
dramatic reduction in the size of electronic systems enables the construction of devices that
can be implanted with less intrusiveness and enables the development of small
electrophysiological tools to measure and to control individual neurons. Simultaneously, this
miniaturization is associated with increases in the operating frequencies of electronics and
reductions in the sizes of antennas and power required, resulting in smaller communications
systems.
Indeed, systems being developed for future use in communications may be of great value for
neuroscience, as well. Multiferroic antennas are one example in which devices can be 1000
× smaller than conventional antennas and may be able to be powered remotely.
64
More
broadly, ultrasmall nanoelectronic chips might be used to combine modalities of detection of
signals from operating neural circuits and the wireless broadcasting of this information at
extremely high data rates to decoders for real-time recording and deciphering of neural
codes.
Bottom-Up Methods, Self-Assembly, and Chemical Patterning
Smaller-than-standard lithographic scales can be reached using self- and directed
assembly.
65–68
Tremendous progress has been made in functionalizing a wide variety of
materials, including semiconductors, insulators, metals, glasses, nanoparticles, and porous
materials. With a single molecular layer, the chemical, physical, and biological properties of
materials can be controlled and tailored.
Given the need for more than electronic or optical function, it is critical to control the
exposed chemistry on devices. Tremendous advances have been made in the last 30 years in
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the chemical functionalization on a wide range of substrates.
65–67
Such advances can be
applied both to guide assembly and to control biological and other interactions.
Likewise, nanoparticles and other nanostructures can be specifically functionalized so as to
target specific locations and to be stabilized there such as lodging themselves in cell
membranes.
69,70
In this way, physical placement of nanostructures will not necessarily be
required, but instead reporting or
post-mortem
analyses can be used to determine the
absolute and relative locations of nanostructures used in BAM sensing. Potential uses of
nanoparticles and nanostructures are discussed further below.
In addition, functional molecules can be used to sense and to transduce potentials, pressure,
and specific aspects of chemical environments. We anticipate that artificial and hybrid
neurotransmitter receptors will be critical in reporting the local environment in the brain.
Tremendous opportunities exist if one can explore the 10 nm synapse scale to understand
dynamic neuro-transmission while simultaneously recording the activities of thousands of
neurons in a neural circuit. If nanostructures can be targeted to specific cell or synapse types
by means of chemical signatures on the cell surfaces, it may be possible to monitor and to
control the activities of neurons in cell-type-specific manners. We anticipate that, ultimately,
such measurements could also be used in feedback circuits to control diseases in which local
chemistries play critical roles, such as Parkinson’s and schizophrenia. The implications of
developing these and other BAM technologies are significant.
Development of Nanoscale Tools for Neuroscience
In light of the advances in resolution of these top-down and bottom-up miniaturization
strategies, the endeavor toward matching the sizes of devices that measure and control
neuronal activity with the sizes of individual neurons is compelling and appears inevitable.
Below, we enumerate a few of the areas in which these contributions are anticipated.
Electrophysiology
The major obstacles that presently limit the use of nanoscale probes are the engineering
challenges of building efficient power and communications systems to interface such neural
probes with the outside world and at the same time avoid tissue damage and undesirable cell
responses. Whether quantum dots or wafer-bonded microsystems are used, it is important to
avoid heating and toxicity in the vicinity of the measurement probes. The most important
physical barrier that limits the sizes of intracellular neural interfaces is the impedance of the
electrodes—whether these are on nano-particles embedded within cell walls or on more
conventional electro-physiological patch-clamp systems. For extracellular recording, the
impedance is even more important, as it determines signal-to-noise characteristics and, thus,
the sensitivities of the neural probes. The sensitivity toward neuronal signals depends on the
impedance, which can then be transduced to measurement devices outside of the brain.
Brain Activity Mapping with Nanofabricated Electrode Arrays
One of the technologies that can greatly facilitate brain activity mapping is the extracellular
microelectrode, which can resolve single-neuron firing
in vivo
without penetrating the cell.
Once inserted in the brain, the detection range of this type of sensor is typically limited to
neurons whose cell bodies are closer than ~50
μ
m from the microelectrode surface.
23,71,72
Thus, in order to construct systems-scale views of brain function from such measurements,
the objective has been to increase the number and density of recording sites. Scaling up
these device attributes has been spurred by parallel advances in electronic instrumentation
for reading out signals
via
low-noise amplifiers, multiplexers, and wireless transmitters
24–30
and performing basic signal processing functions on-chip to reduce the burden of data
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collection.
31,32
Innovations in microelectrode manufacturing techniques have made it
possible to deploy simultaneously nearly 1000 measurement sites distributed across several
cortical areas of the same animal, allowing the firing patterns of hundreds of neurons to be
monitored in parallel.
33
Leveraging micro- and nanofabrication technology raises the
prospect for creating vastly greater numbers of electrodes and smaller, less invasive
implantable devices.
34–37
A promising category of these micromachined devices is the
planar electrode array, which is patterned on a crystalline,
38,73
ceramic,
39
or polymer
74,75
support structure (Figure 1). Positions of micro-electrodes on these thin penetrating shafts
and spacing between two or more shafts can be tailored to simplify targeting of multiple
anatomical areas or subregions in tandem.
76–78
The measurement of neuronal activity with
three-dimensional (3D) microelectrode arrays represents a major advance in brain activity
mapping techniques, by providing a tool to probe how intra-and inter-regional neural circuits
behave cooperatively to compute information. We envision scaling up this 3D architecture
to sample arbitrarily complex networks.
Advantages and Challenges of Electrode-Array-Based Mapping
Approaches
The use of implantable electrodes is complementary to optical-based brain activity mapping:
Electrodes can access deep brain structures that are challenging to reach with optical
methods; they do not require labeling cells with a dye; and they offer higher sampling speed
than voltage or calcium indicators (although advances in optical recording techniques are
circumventing many of these issues).
7,79,80
Furthermore, the manufacturing processes used
in voltage-sensing electrode development can translate to other modes of interrogating
neuronal activity, such as chemical sensors.
81,82
Electrodes also present two major challenges for brain activity mapping. First, as they
measure extracellular electric fields from all nearby active neurons, deriving single-unit
information from these signals is not trivial,
83
and a fast, automated “spike sorting”
algorithm for handling data from a large number of electrodes receiving correlated signals
remains elusive. Even after spike sorting is successful, extracellular signals cannot directly
differentiate the origins of action potentials at the level of genetically specific neuronal
subpopulations. One approach that partially addresses this limitation is to rely on indirect
identification methods such as extracellular action potential shape, spike time
characteristics, and pharmacological response. This may be valuable for identifying cells
along broadly defined categories, such as pyramidal neurons
versus
interneuron
84
or
dopaminergic
versus
nondopaminergic.
85
However, this method is not without pitfalls.
86
Perhaps an early goal of the BAM electrode technology effort would be to catalog
exhaustively extracellular electrophysiological markers of genetically identified neuronal
sub-populations to lend greater validity to this indirect approach. Alternatively, a more
direct way of identifying neurons is by probing their responses to a gene, region, or
pathway-specific pharmacological or optogenetic modulator of activity.
87–89
Microfabricated neural probes that can record activity and deliver drugs or light have been
developed and can help address this issue.
90–94
New developments in nano–bio interfacing and 3D microfabrication techniques might
provide the means to overcome some of the limitations of planar microelectrode-based
extracellular electrophysiology. Nanoscale needle electrodes (Figure 2) can provide high-
fidelity electrophysiological interfaces to cardiomyocytes
95,96
and mammalian neurons,
97
with clear cell-to-electrode registry. These electrodes can even perform intracellular
recording and stimulation of neurons in a highly scalable fashion
in vitro
and
ex vivo
.
97
One
possibility is to couple these nanoscale electrodes together with the modular 3D brain
interfacing technology that has recently been developed.
98
These 3D devices, which are
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