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First published online May 26, 2006
Journal of Experimental Biology 209, 2312-2319 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02163
Review Article: Phenotypic Plasticity of the Brain |
Neuronal networks and synaptic plasticity: understanding complex system dynamics by interfacing neurons with silicon technologies
1 Department of Physiology and Biophysics, Hotchkiss Brain Institute,
University of Calgary, Calgary, Alberta, T2N 4N1, Canada
2 Department of Cell Biology and Anatomy, Hotchkiss Brain Institute,
University of Calgary, Calgary, Alberta, T2N 4N1, Canada
* Author for correspondence (e-mail: mcolicos{at}ucalgary.ca)
Accepted 8 February 2006
Summary
Information processing in the central nervous system is primarily mediated through synaptic connections between neurons. This connectivity in turn defines how large ensembles of neurons may coordinate network output to execute complex sensory and motor functions including learning and memory. The synaptic connectivity between any given pair of neurons is not hard-wired; rather it exhibits a high degree of plasticity, which in turn forms the basis for learning and memory. While there has been extensive research to define the cellular and molecular basis of synaptic plasticity, at the level of either pairs of neurons or smaller networks, analysis of larger neuronal ensembles has proved technically challenging. The ability to monitor the activities of larger neuronal networks simultaneously and non-invasively is a necessary prerequisite to understanding how neuronal networks function at the systems level. Here we describe recent breakthroughs in the area of various bionic hybrids whereby neuronal networks have been successfully interfaced with silicon devices to monitor the output of synaptically connected neurons. These technologies hold tremendous potential for future research not only in the area of synaptic plasticity but also for the development of strategies that will enable implantation of electronic devices in live animals during various memory tasks.
Key words: synapse, transistor, photoconductive stimulation, interface, neural circuits, biocomputational device, biomic hybrid
Introduction
Synaptic and network plasticity
Synapses have innate propensity to alter the efficacy of synaptic
transmission between neurons. These changes in synaptic strength can occur
rapidly through mechanisms such as long-term potentiation and depression
(LTP/LTD) (Malenka and Bear,
2004
; Lisman and Spruston,
2005). In this form of plasticity, either the amount of
neurotransmitter released from the presynaptic terminal or its receptor
function in the postsynaptic cell is modulated. This provides a rapid and
immediate mechanism for altering the strength of that particular synapse, and
is usually initiated in response to a specific activity pattern, such as high
frequency stimulation. In addition to such biophysical changes, the
connections between neurons can also undergo direct structural modifications
by altering the total number of synapses
(Dillon and Goda, 2005;
Chklovskii et al., 2004;
Ruthazer, 2005). Such
structural changes underlying long-term plasticity are more permanent, require
longer time periods to develop and are contingent upon de novo
protein synthesis. This form of synaptic plasticity has been the main focus of
neuroscience research over the past decade and has provided greater insights
into both cellular and molecular mechanisms by which synapses between subsets
of neurons can be altered. These advances have not yet, however, resolved the
attributes of network plasticity, nor have they elucidated the cellular basis
of any given behavior – owing primarily to our inability to monitor
direct cellular activity at the level of larger neuronal ensembles.
The network plasticity can be considered as the sum of individually summated synaptic changes that occur in groups of functionally related neurons. While this assumption holds true at the structural level, in terms of information processing and the network dynamics, the situation is actually much more complex. The output of a network is determined by the integrated activity of individual neurons, yet the activity-dependent changes at a single synapse are themselves driven by the complexity of the network itself. Numerous studies have demonstrated that changes in synaptic efficacy between a subset of neurons occur primarily in response to activity at that particular synapse. However, in the network environment the global activity that is distributed throughout many, if not virtually all, of the cells is equally important in determining the functional output of the system. Neurons rarely receive input from a single source; the input signals are thus integrated from many different contacts and subsequently retransmitted in parallel to an equally larger number of targets. So the changes observed at a single synapse are actually the sum of a great number of inputs, and the change in efficacy of that synapse can likewise alter an equally larger number of target neurons. Therefore, the coordination of global activity throughout the network, which must ultimately be mediated by individual synaptic changes, determines which contact points should be modulated selectively.
An example of such a mechanism would be the balance between inhibitory and
excitatory synapses within a cortical network, which is modulated both during
development and also in the adult brain in an activity-dependent fashion
(Graf et al., 2004;
Levinson and El-Husseini,
2005; Liu, 2004;
Prange et al., 2004). By
shifting the inhibitory/excitatory balance, either temporarily or permanently,
dramatic changes in network behavior can be observed. These changes not only
generate seizure like activity (McCormick
and Contreras, 2001;
Romo-Parra et al., 2003), but
can also produce synchronized versus uncoordinated firing patterns
within the network (Aradi and Maccaferri,
2004; Kudela et al.,
2003; van Pelt et al.,
2005). Synchronized neural activity is an effective way to provide
information to individual synapses regarding the network behavior. While
somewhat cliché, the adage that `neurons that fire together, wire
together' holds true; and provides a viable mechanism for coordinating
synaptic change in a large number of neurons dedicated to a specific
information processing task.
To understand network function and plasticity, two levels of investigation need to be performed. On a synaptic level, the mechanisms underlying the basic process of synapse formation and loss, as well as the biochemical mechanisms to decide between excitatory versus inhibitory synapses, must be determined. On a network level, we need to characterize the activity signals received by individual neurons, which in turn, instruct them vis-à-vis their role in the final output. In order to do this several approaches have been used. While extremely varied in both the experimental paradigm and technology employed, they have one main feature in common: the goal of simultaneously recording activity from a large number of neurons. Inherent in this pursuit, however, is the caveat that as we increase the number of neurons in our recording paradigm, we decrease the resolution and the fidelity of our recordings.
Strategies to investigate network plasticity
Brain imaging technologies – all neurons but low resolution
Working towards the goal of simultaneously imaging activity in the entire
brain, several newer technologies have been employed. For example, functional
magnetic resonance imaging (MRI), which is based on the detection of blood
oxygen levels using molecular resonance imaging technology, can determine
non-invasively which areas of the brain are most active, as the increased
metabolism is reflected by the change in blood flow. A similar principle is
employed for positron emission tomography (PET) scans, where a radiolabeled
glucose analogue is tracked in three dimensions; increased metabolism is again
a measure of activity.
Several studies have used this technique to map brain regions responsible
for specific behavioral functions following training or during recovery from
stroke, and have addressed global plasticity by monitoring metabolic activity
in areas assigned to a specific function
(Jasanoff, 2005;
Mechelli et al., 2005;
Smirnakis et al., 2005;
Sowell et al., 2004). While
spectacular results have been achieved, the level of resolution is still
restricted to large areas of the brain containing millions of neurons. To
further enhance the resolution and interpretation of such metabolism-based
imaging studies, these techniques have been combined with high-resolution
electroencephalography (EEG) and magnetoencephalograpnhy (MEG) in order to
correlate the anatomical images with more detailed firing patterns of the
neurons. In addition to three-dimensional imaging technology, new biochemical
techniques are also being developed to use voltage-sensitive and
calcium-sensitive dyes (Baker et al.,
2005) as well as fluorescence resonance energy transfer
(FRET)-based systems (Chanda et al.,
2005) to watch (or monitor) neuronal activity. Brain imaging
technologies have a strong advantage in their ability to examine large
ensembles of neurons simultaneously, thus giving a tremendous overview of the
activity in the entire brain.
In vivo network dynamics – hundreds of neurons, better resolution
Investigation of how changes in neuronal connectivity alter network
function as a whole requires a much higher level of resolution. During the
past decade, advances in microscopy techniques offer greatly improved new
strategies towards this goal. Two-photon imaging of living neuronal tissue
expressing the fluorescent protein GFP has allowed detailed imaging of the
neuronal architecture both before and after a learning paradigm
(Mainen et al., 1999). These
technologies have uncovered many details about the physical properties of
neuronal connectivity and plasticity. However, to achieve this level of
resolution one must limit the amount of tissue that one can monitor
simultaneously. This is accomplished in vitro by taking slices of
brain tissue, often from the hippocampus – a brain structure intensely
studied due to its involvement in learning and memory. However, recent studies
have demonstrated that this approach is also feasible in the intact animal, by
using calcium-sensitive fluorescent indicators of activity in the visual
cortex (Ohki et al.,
2005).
Perhaps the greatest wealth of information about neuronal function in a network setting has been obtained by electrophysiological analysis of tissue slices. By recording both directly from single neurons using whole-cell patch-clamp techniques or from small groups of neurons using extracellular recording electrodes, we can gather detailed information about the electrical dynamics of functioning mammalian neural networks. Brain tissue slices can be kept alive and functional for hours at a time, and have the advantage of maintaining the same network connectivity as is observed in the intact animals. Studies of intact tissue slices have uncovered phenomena such as LTP and LTD as well as spike-timing dependent plasticity, in which acute timing of activity in several parallel and convergent pathways can initiate plasticity in the network. Although these experiments have pioneered our understanding of how neurons function at a systems level, they are however drastically limited in the number of neurons that can be examined simultaneously.
Newest paradigm
Dissociated cultures – fewer neurons, higher resolution
While tissue slices have the advantage of maintaining network connectivity,
they suffer from a short lifespan during the experimental procedure. To
overcome this problem, two main strategies have been developed. One is the use
of organotypic cultures, where tissue slices are partially dissociated and
allowed to grow on a nylon membrane, maintaining their basic connectivity and
allowing nutrient access to all the neurons in the network. This technique has
been used to demonstrate longer-term phenomena, which could not otherwise be
observed in the isolated brain or tissue slices, due to their shorter lifespan
(Dong and Buonomano, 2005;
Hasenstaub et al., 2005).
Neurons dissociated in cell culture from their respective areas in the
intact brain can recapitulate their connectivity patterns and remain viable
for months, provided that appropriate substrate and trophic factors conditions
are met (Potter and DeMarse,
2001). A caveat with this approach is that the structural and
functional integrity of the original network is lost. However, neurons taken
from embryonic or neonatal animals have a remarkable capacity to rewire
themselves accordingly, and if provided with growth and synapse conducive
conditions, they are likely to network in a manner similar to that in
vivo. For example, the neuronal connectivity that endows the developing
retina to generate waves of activity in vivo, is recapitulated in
cell culture, when these neurons are grown in the presence of appropriate
growth factors (Colicos et al.,
2004). Such judicious use of culture techniques has enabled us to
reconstruct networks of functionally connected neurons, which generate
rhythmical patterned activity, in a manner similar to that seen in
vivo (Syed et al., 1990).
Similarly, many in vivo-like neuronal activity patterns, including
LTP and LTD, as well as a large variety of spike-timing-dependent phenomena,
have been observed in cultured neurons
(Hasenstaub et al., 2005;
Netoff et al., 2005;
Aradi and Maccaferri, 2004).
Additionally, cells in culture can be transfected with specific genes to
express exogenous proteins or to lower the levels of endogenous molecules.
Culture systems are therefore a practical way to directly determine the
functional significance of various proteins in neuronal functions.
Various imaging and electrophysiological recording techniques employed in vivo can also be adopted for cultured neurons, albeit at a much higher resolution and with greater fidelity. Because dissociated cells can be placed in close contact with the substrate as compared with their in vivo counterparts, this provides a unique advantage with which to develop an entirely new way of interfacing recording devices with neurons.
The transistor–neurons interface
When transistor technologies were in their infancy, parallels were drawn
between computers and neuronal systems everywhere from a laboratory setting to
Hollywood. Specifically, researchers were struck by the similarity between the
charge transfer in a biological membrane and the semiconductor elements of the
transistor. The possibility of using the transistor in close contact with the
biological membrane of neurons was first investigated in the late 1970s and
early 1980s, when field effect transistors were used to record brain activity
during normal animal behavior (Boyko and
Bures, 1975; Fontani,
1981; Sasaki et al.,
1983). The goal of these experiments was primarily to collect
behavioral data from animals by transmitting information collected by standard
electrodes in contact with the neurons in an intact brain. Using transistor
devices, this information could be relayed to the recording unit. However, in
1988 the Rutledge lab developed a silicon-based microelectrode/neuronal
connection, which marked the start of a more sophisticated method of
interfacing the silicon materials with the transistor in direct contact with
the biological membrane (Regehr et al.,
1988). In 1991 Peter Fromherz of the Max Planck Institute
succeeded in developing a neuron–silicon junction between an insulated
gate field effect transistor and a Retzius cell of the leech
(Fromherz et al., 1991). This
landmark paper marked the beginning of the refinement of the
neural–transistor technology, enabling interfaces between living neurons
and silicon as a viable tool for information transfer between the two.
Interfaces between neurons and electronics have since evolved over the
years and now take two basic forms, each with specific goals. Currently,
commercially available multielectrode arrays with 64 points for stimulation
and recording can function without direct physical coupling with neuronal
membrane (Arnold et al., 2005;
van Pelt et al., 2005). These
devices are extremely useful for recording excitatory postsynaptic current
(EPSC) field potentials, and can stimulate groups of neurons situated above
the electrode. By using the same array principal, but incorporating a
transistor and capacitor at each node rather than simple electrodes, one can
directly contact the membrane of individual neurons, allowing intracellular
recording and stimulation. Recently, in a collaborative effort between
researchers at the Syed lab at the University of Calgary and Peter Fromherz
(Max Planck Institute), a proof of principal experiment was performed using
this technology (Kaul et al.,
2004). Specifically, individually identifiable, pre- and
postsynaptic neurons from the snail Lymnaea were directly cultured on
the silicon chip in a soma–soma configuration
(Feng et al., 1997;
Hasenstaub et al., 2005;
Munno and Syed, 2003;
Smit et al., 2001)
(Fig. 1). The presynaptic
neuron visceral dorsal 4 (VD4) makes excitatory, cholinergic synapses with its
postsynaptic partner left pedal dorsal 1 (LPeD1) in cell culture and this
synapse exhibits short-term synaptic plasticity
(Fig. 1a). When their neuronal
somata were juxtaposed on a silicon chip, in the presence of the
brain-conditioned medium (Fig.
1), excitatory synapses reformed that were similar to those
observed under normal conditions (Fig.
2). Most importantly, the silicon chip technology allowed us not
only to stimulate the presynaptic cell via the capacitor located
underneath the neuron, but also to monitor postsynaptic action potentials
non-invasively. The chip was used to induce synaptic potentiation, which was
successfully recorded through the chip
(Fig. 2B). Thus, by stimulating
the presynaptic neuron with a capacitor, and by recording the post-tatanic
potentiation in the postsynaptic cell through the transistor, the biological
synapse between two neurons was modulated by computer control
(Kaul et al., 2004).
Repetitive stimulation of the first neuron strengthened its connection with
the second neuron and this change in the synaptic efficacy was detected by the
output capacitor (Fig. 2B).
This essentially created a biological neuronal memory on a silicon chip,
demonstrating a complete loop whereby both biological and electronic
circuitries were successfully interfaced.
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), which had long been hypothesized to occur during memory
formation but had never been visualized directly. The photoconductive
stimulation technology is currently used to study neural network pathologies
such as autism spectrum disorders, and can be adapted as an in vitro
model to examine the network effects of long-term drug abuse. Long-term stimulation of neuronal networks
One of the main advantages of photoconductive stimulation is the robustness
of its interface and the non-invasive nature of the protocol. We have recently
developed an incubator-mounted version of the interface that can provide
target stimulation of neurons for long periods of time. As a proof of
principal experiment, we have chosen a simple paradigm to test its
functionality and to determine the basic effects of patterned stimulation on
neuronal network development. Neuronal cultures are grown on 1 cm square
silicon wafers, and when mounted in the device, a central region of the
network is stimulated periodically at high frequency. A large body of evidence
suggests that developing neural systems use activity to define their final
patterns of connectivity. By determining the structure of a neural network in
the regions of the stimulated versus unstimulated wafer, we can
determine the effect of the activity on neuronal connectivity patterns.
Fig. 3 shows an image of a
region of the neural network cultured on the silicon wafer. To detect changes
in the pattern of synaptic distribution following the stimulation paradigm, we
labeled synapses with antibodies to the active zone protein bassoon. Images
spanning a strip across the entire wafer are acquired and joined.
Fig. 3C shows a representative
density map of the distribution of synapses in a stimulated versus an
unstimulated wafer. In the center of the wafer is a visible change in the
pattern of connectivity as a result of the stimulation. Interestingly, while
the pattern appears to have increased in complexity, there is a decrease in
the overall number of synapses present. This complements observations
indicating that decreased neuronal activity results in an increase in the
number of synapses (Lauri et al.,
2003; Nakayama et al.,
2005). Intuitively this makes sense, in that if there is a
specific pattern (information) within the stimulus, in order to represent this
information only specific synapses are desirable in the final network. The
ability to manipulate and visualize such large regions of neural network
demands detailed statistical and mathematical processing of the data.
Fig. 4 shows the Delaney
triangulation pattern of synaptic cluster distribution for both stimulated and
unstimulated neuronal cultures. Stimulation results in grouping subsets of
neuronal blocks, and we are currently investigating the correlation between
the geometric structures and the underlying stimulation frequency underlying
network plasticity. By using techniques to analyze variance and heterogeneity,
regions of nonrandom patterns can be extracted from these images.
Fig. 5 shows an example of an
analysis of directional variance (colored strings) overlaid on top of an image
of the original network. These analyses will allow us to correlate structural
changes that occur in response to specific frequency patterns. It is through
such changes that the network structural and functional plasticity may be
achieved.
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Future developments
Perhaps the most exciting prospect currently under development is a fusion between the photoconductive stimulation and transistor/capacitor based technologies. By combining the highly adaptable and high-resolution targeting system of light with the intracellular recording capacity of the transistors, we are currently adopting a unique approach to provide large-scale integration of biological neural networks with digital technology. Photoconductive electronics will provide a means not only to understand the dynamics of neural networks, but will also form the basis for harnessing the massively parallel information processing capacity of neurons (Fig. 6). This will allow us to move back up the scale of resolution, to study large ensembles of neurons at a resolution of single neuron recordings. While neuronal networks formed from dissociated cells can provide valuable information, by incorporating this technology with brain slices, a more naive network structure can be analyzed. In this way, we can obtain a complete picture of how large neural networks not only process information, but also how they endogenously adapt to and change the network behavior. Finally, future technologies will also focus on designing implantable recording and stimulating devices for freely behaving animals.
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List of abbreviations
Acknowledgments
We thank Wali Zaidi and Andrea Sullivan for technical assistance, and Johanna Hung and Audrey Kertesz for the network analysis studies. This work was supported by Alberta Ingenuity Fund grants to M.A.C., and Natural Sciences and Engineering Research Council of Canada (NSERC) grants to N.I.S.
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7 DIV) the chips were placed into parallel
stimulation devices, the control chip receiving no stimulation whereas the
second chip received periodic high frequency stimulation in a circular region
at the center of the wafer. Following stimulation for 1–3 days, chips
were removed from the device and fixed, and then processed for immunostaining
with anti-bassoon antibody. Bassoon is a presynaptic active zone protein,
allowing the visualization of synapses. Images are acquired at low
magnification and joined together to span the entire chip. (A) Unstimulated
network, (B) central region of stimulated chip. Several mathematical methods
were used to analyze the synaptic distribution; the first steps in analysis
include deconvolution, thresholding and watershed analysis of the synaptic
distribution images. (C) Sample synaptic density profiles represented by
density distribution, illustrating altered patterns that result from long-term
stimulation. Pseudo-color scale represents regions of low to high synaptic
density.
