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fnsys-16-979680 August 20, 2022 Time: 10:56 # 1
TYPE Mini Review
PUBLISHED 24 August 2022
DOI 10.3389/fnsys.2022.979680
OPEN ACCESS
EDITED BY
Luis Carrillo-Reid,
National Autonomous University
of Mexico, Mexico
REVIEWED BY
Osvaldo Ibáñez-Sandoval,
Autonomous the University of San Luis
Potosi, Mexico
*CORRESPONDENCE
José Bargas
jbargas@ifc.unam.mx
Mariana Duhne
mariana.duhneramirez@ucsf.edu
RECEIVED 27 June 2022
ACCEPTED 27 July 2022
PUBLISHED 24 August 2022
CITATION
Lara-González E, Padilla-Orozco M,
Fuentes-Serrano A, Bargas J and
Duhne M (2022) Translational neuronal
ensembles: Neuronal microcircuits
in psychology, physiology,
pharmacology and pathology.
Front. Syst. Neurosci. 16:979680.
doi: 10.3389/fnsys.2022.979680
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© 2022 Lara-González,
Padilla-Orozco, Fuentes-Serrano,
Bargas and Duhne. This is an
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does not comply with these terms.
Translational neuronal
ensembles: Neuronal
microcircuits in psychology,
physiology, pharmacology and
pathology
Esther Lara-González1,2, Montserrat Padilla-Orozco1,
Alejandra Fuentes-Serrano1, José Bargas1*and
Mariana Duhne1,3*
1División Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México,
Mexico City, Mexico, 2Department of Neuroscience, Feinberg School of Medicine, Northwestern
University, Chicago, IL, United States, 3Department of Neurology, University of California,
San Francisco, San Francisco, CA, United States
Multi-recording techniques show evidence that neurons coordinate their
firing forming ensembles and that brain networks are made by connections
between ensembles. While “canonical” microcircuits are composed of
interconnected principal neurons and interneurons, it is not clear how they
participate in recorded neuronal ensembles: “groups of neurons that show
spatiotemporal co-activation”. Understanding synapses and their plasticity
has become complex, making hard to consider all details to fill the gap
between cellular-synaptic and circuit levels. Therefore, two assumptions
became necessary: First, whatever the nature of the synapses these may be
simplified by “functional connections”. Second, whatever the mechanisms to
achieve synaptic potentiation or depression, the resultant synaptic weights
are relatively stable. Both assumptions have experimental basis cited in this
review, and tools to analyze neuronal populations are being developed based
on them. Microcircuitry processing followed with multi-recording techniques
show temporal sequences of neuronal ensembles resembling computational
routines. These sequences can be aligned with the steps of behavioral
tasks and behavior can be modified upon their manipulation, supporting
the hypothesis that they are memory traces. In vitro, recordings show that
these temporal sequences can be contained in isolated tissue of histological
scale. Sequences found in control conditions differ from those recorded in
pathological tissue obtained from animal disease models and those recorded
after the actions of clinically useful drugs to treat disease states, setting the
basis for new bioassays to test drugs with potential clinical use. These findings
make the neuronal ensembles theoretical framework a dynamic neuroscience
paradigm.
KEYWORDS
neuronal ensembles, neuronal networks, functional connections, synaptic weights,
population coding, Parkinson’s disease, L-DOPA induced dyskinesia, epileptiform
discharges
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Introduction
Nephrons, lobules, alveoli, acini and so forth work as
functional units in body organs: multicellular organizations
coordinating the actions of different cell classes to receive
an input and yield and output, modules commonly ordered
in tandem in a given organ and being preserved along the
vertebrate phylogeny (Bargas and Pérez-Ortega,2017). One
advantage of these modular architecture is obvious: degradation
of an organ due to degenerative chronic disorders or age, is
a gradual steady decline before a complete failure is reached.
The advent of multicellular recording techniques (Brown et al.,
2005;Harris,2005;Dombeck et al.,2009;Dragoi,2020;
Barack and Krakauer,2021;Ebitz and Hayden,2021;Lagache
et al.,2021) has shown that many areas of the nervous
system have a modular organization of neuronal populations,
previously shown indirectly with intra-and extra-cellular
unitary recordings associated with local field potentials and
other population recordings (Brown,1914;Hubel and Wiesel,
1962;Steriade and Deschenes,1984;Arshavsky et al.,1997;
Mountcastle,1997;Timofeev and Steriade,1997;McCormick,
2002;Grillner,2006;Kiehn,2006;Casanova and Casanova,
2018;He et al.,2009): groups of neurons that coordinate
themselves to work together in several brain areas to perform
their functions have been defined defined as “canonical”
microcircuits in Shepherd and Grillner (2018). A new advantage
for experimentalists is that territorial coordinates of unitary
activity while using multielectrode arrays (MEAs), or various
neurons themselves using calcium imaging, can be identified,
and followed within and without their groups with single
cell resolution (Buzsáki,2004;Kwan,2008;Shew et al.,2010;
Einevoll et al.,2012;Carrillo-Reid et al.,2015;Stringer et al.,
2019) and be compared with population recordings. These
findings ended a long debate about the functional unit of the
nervous system: either the neuron or neuronal populations (for
a historical perspective see: Yuste,2015). In many brain areas,
the functional unit are neuronal ensembles: groups of coactive
neurons (see Carrillo-Reid and Yuste,2020).
Neuronal ensembles: From
synaptic to functional connectivity
There are many classes of neurons with their proper
biophysical, biochemical and genetical features (e.g., Llinás,
1988;Bean,2007;Maudsley et al.,2007;Tse and Wong,
2013;Armand et al.,2021), however, in many areas of the
brain, immunostaining, in situ hybridization, single cell PCR,
transcriptomics and whole cell recordings have shown iterative
modules accomplishing coordinated tasks (Markram et al.,
2015;Shepherd and Grillner,2018;Assous and Tepper,2019;
Williams and Riedemann,2021). In a brief simplification,
minimal features of a canonical microcircuit are: a group of
GABAergic interneurons innervate principal neurons mainly
in the dendrites and spines to control afferent inputs and
feed-back entries (e.g., low-threshold spiking – LTS; double
bouquet cells; Martinotti cells; with similar markers in
several brain places: neuropeptide Y – NPY; nitric oxide
synthase – NOS; somatostatin – SOM), while other groups
of interneurons mainly innervate the perisomatic area and
axon hillock to control principal neurons output (e.g., fast-
spiking interneurons – FSIs; basket or chandelier cells; the
main marker being parvalbumin – PV). Principal cells are
surrounded by these interneurons controlling input and
output in a consensual trait, plus interconnections between
principal neurons and long-range interneurons form feed-
forward networks including disinhibition (Tremblay et al.,
2016;Traub et al.,2020). Interneurons are minority and,
therefore, are shared by groups of principal cells. How many
of these microcircuits and neurons conform an ensemble?
The basic connectivity is scalable (e.g., from minicolumns
to barrels and regions; Staiger and Petersen,2021). Perhaps
recording of thousands of neurons (Stringer et al.,2021)
show only the tip of the iceberg. Alternatively, scalability
shows similar sequence patterns from the histological scale
(dozens of neurons) to the mesoscale (hundreds of neurons).
Regular modular elements with similar electrophysiological
profiles and markers expression can be found in all cortical
mantles including the hippocampus, the striatal circuit, and with
slightly different profiles and names the cerebellum (Douglas
and Martin,2004;D’Angelo et al.,2016;Burke et al.,2017).
Importantly, they resonate at different frequencies and are main
components of cerebral oscillations and rhythms generation
(Hutcheon and Yarom,2000;Buzsaìki and Draguhn,2004),
which could be a way of additional communication besides
axons, terminals and volume transmission (Ito and Schuman,
2008;Jahnke et al.,2014).
An important factor to understand the association of
particular neuron types and their cuasi-iterative prevalence
is of course programmed embryogenesis (Li et al.,2016). In
addition, there is the theoretical framework synthesized by Hebb
(1949)and later modified with experiments from numerous
groups (e.g., Frégnac,2003;Malenka and Bear,2004;Caporale
and Dan,2008;Baltaci et al.,2019;Brown and Donald,2020;
Magee and Grienberger,2020). Although the generic name for
these modules may be: “neuronal ensembles” (other names in:
Carrillo-Reid and Yuste,2020), their composition and complete
numbers in different contexts is unknown. For example, by
identifying and following samples of motor cortical ensembles
in vitro at histological level it was found that most sampled
ensembles have PV + neurons (Serrano-Reyes et al.,2020).
Similar experiments must be done with the different kinds
of interneurons and principal cells (Markram et al.,2015)
to describe their composition and observe their dynamics,
since ensembles are composed with inhibitory and principal
neurons playing various roles. Due to these cited experiments,
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to simplify the cellular-synaptic complexity and make sense
at the network level, neuronal ensembles, defined as “groups
of neurons that show spatiotemporal co-activation” (Yuste,
2015) are commonly linked with “functional connectivities”
between recording sites while using multi-recording techniques
(Fr˝
ohlich,2016). Dimensional reduction can be used to position
ensembles in a neuronal state space (Ebitz and Hayden,2021).
Neurons with correlated firing are sorted together by functional
connections and their activity alternates due to connections
between ensembles, which can be illustrated as trajectories in
low-dimensional space and connectivity graphs (Pérez-Ortega
et al.,2016;Calderón et al.,2022). This simplification allows
going from the cellular-synaptic level to the emergent properties
of recorded networks (Carrillo-Reid et al.,2015): in a “bottom-
up” direction.
Neuronal ensembles as functional
units
The modified Hebbian theoretical framework tries to fill
the gap between the cellular-synaptic level and the network
level at different scales (Grillner and Graybiel,2006;Buzsáki,
2010;Tognoli and Kelso,2014). Beginning with correlated
plasticity that can be extended to other plasticity types, it
is postulated that correlated or timed activity between pre-
and post-synaptic elements reinforce synaptic connections,
while decorrelated or inverse timing between these elements
weaken synaptic connections. These mechanisms generate
long lasting changes in the synaptic weights of the network,
so that some synapses are enforced [long-term potentiation
(LTP)] and other are debilitated or disconnected long-term
depression (LTD), producing preferent paths for the flow
of activity, being this mechanism the basis to make new
circuits for learning and making memory traces (Kandel
et al.,2014;Dringenberg,2020;Stacho and Manahan-Vaughan,
2022). Besides the unique specialized fast acting synapses
that connect neuron groups using postsynaptic ligand-gated
ion channels (Cockcroft et al.,1990;Ortells and Lunt,1995;
Auerbach,2013), alternative communicating pathways - shared
with non-excitable cells - are also present: e.g., volume
transmission (paracrine communication), extra-synaptic and
synaptic receptors coupled with G-proteins (GPCRs) that
can or cannot form heteromeric complexes igniting diverse
intracellular signaling cascades (e.g., Agnati et al.,1994,
2006;Lefkowitz,2000;Rasmussen et al.,2011;Latek et al.,
2012;Fuxe et al.,2013;Tse and Wong,2013), plasticity of
GABAergic inhibitory neurons (Rueda-Orozco et al.,2009;
Castillo et al.,2011;Roth and Draguhn,2012;Barberis,
2020), anti-Hebbian mechanisms, retrograde signaling, the role
of neuromodulators, instructive signals and eligibility traces,
different synaptic receptor types and sub-units, etcetera, have
increased the complexity of synaptic plasticity (Lamsa et al.,
2007;Sjöström et al.,2008;Conde et al.,2013;Piochon
et al.,2013;Johansen et al.,2014;Park et al.,2014;Ruan
et al.,2014;Gerstner et al.,2018;Langille and Brown,2018;
Cingolani et al.,2019;Bannon et al.,2020;Magee and
Grienberger,2020;Speranza et al.,2021). Countless experiments
demonstrate diverse mechanisms for LTP and LTD (e.g.,
Stanton,1996;Citri and Malenka,2007;Kandel et al.,2014;
Berry and Nedivi,2016;Fauth and Tetzlaff,2016;Abraham
et al.,2019;Baltaci et al.,2019;Stampanoni-Bassi et al.,
2019;Magee and Grienberger,2020;Mateos-Aparicio and
Rodríguez-Moreno,2020), modifying the original Hebbian
principle (Markram et al.,1997;Martin and Morris,2002;
Nicoll and Roche,2013;Dringenberg,2020). Whatever the
mechanisms for generating LTP and LTD (Dudek and Bear,
1992;Malenka and Bear,2004;Caporale and Dan,2008;Hawes
et al.,2013;Kandel et al.,2014;Nicoll,2017;Diering and
Huganir,2018;Abraham et al.,2019;Brown and Donald,
2020), preferred paths for the flow of activity form stable
circuits due to changes in synaptic weights. In turn, stable
circuits encode memory traces (Kandel and Schwartz,1982;
Spatz,1996;Sweatt,2016;Andersen et al.,2017). It has
been demonstrated that connections already set and stable,
can be followed along several days (Pérez-Ortega et al.,
2021), and that the flow of activity through the network,
observed as synchronous or correlated neuronal firing in
neuron clusters that alternate their activity in a recurrent
or reverberant fashion forming temporal sequences are their
manifestation (Beggs and Plenz,2003;Carrillo-Reid et al.,2008;
Rabinovich M. et al.,2008;Rabinovich M. I. et al.,2008;
Buonomano and Maass,2009;Buzsáki,2010;Plata et al.,
2013a,b;Lara-González et al.,2019;Serrano-Reyes et al.,
2020). Activity sequenecs, transitions or trajectories between
neuronal ensembles, are similar to computational routines
that can be recorded at different scales using multi-recording
techniques. When recorded in vivo in trained animals,
ensembles sequences can be aligned with the different steps
of a behavioral task (Dombeck et al.,2009;Adler et al.,
2012;Bakhurin et al.,2016;Haegens et al.,2017;Jennings
et al.,2019;Sheng et al.,2019;Carrillo-Reid and Yuste,2020;
Lagache et al.,2021;Coss et al.,2022;Pimentel-Farfan et al.,
2022). Temporal ensembles sequences can be observed during
learning and the action of neuromodulators, by following
particular identified neurons with calcium imaging, and exciting
or inhibiting them with optogenetic techniques (Bliss and
Gardner-Medwin,1973;Carrillo-Reid et al.,2009a,b,2016,
2019;Grewe et al.,2017;Adler et al.,2019;Josselyn and
Tonegawa,2020). One can hypothesize what are the cellular
mechanisms of ensemble alternation, ignition, inhibition,
codification of stimulus, actions or decisions, defining possible
causal relations with behavior (Takehara-Nishiuchi,2022).
Hypotheses can be tested going back to the cellular-synaptic
level and ask for a precise machinery: a guided “top-down”
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direction. For instance, the original Hebbian proposal forming
ensembles (correlative plasticity: “neurons that fire together wire
together” – Löwel and Singer,1992) has received definitive
experimental demonstration in the primary visual cortex with
optogenetic techniques (Carrillo-Reid et al.,2016). On the
other hand, it has been demonstrated that the inhibition of
a targeted ensemble with chemogenetic techniques, at the
time it enters into the behavioral sequence, does interrupt the
learned task (Sheng et al.,2019). Going “blind” at the cellular-
synaptic level may discover many interesting phenomena whose
importance is only speculative since their actions on the
neuronal population are unknown.
Neuronal ensembles in brain
slices: Asking brain tissue what it
can do
In vitro preparations were originally designed to ask
questions at the cellular-synaptic level, however, given the
simplifications stated above, one can assume that stable modules
can be seen in a piece of tissue isolated from the rest of
the system as long as there is a way to turn them on.
This was first achieved in the lamprey (Grillner and El
Manira,2020) using targeted and population recordings to
observe that the same circuit recorded in vivo could be
recorded in vitro, where drugs concentrations and experimental
manipulations are at hand (Grillner and Zangger,1979;
Kiehn,2006;Carrillo-Reid et al.,2008;Plenz,2012;Guzulaitis
and Hounsgaard,2018). In many cases, the “switch” to
turn on ensemble dynamics was NMDA added to the bath
saline, although other chemicals and procedures can be
used (Fellous and Sejnowski,2000;Aparicio-Juárez et al.,
2019). In slices, low NMDA concentrations “titrate” the
most enforced connections within and between neuronal
ensembles and induce their activation (Carrillo-Reid et al.,
2008) with the help of the “driving force” given by extra-
synaptic receptors (Garcia-Munoz et al.,2015). In this way,
recurrent transitions between ensembles that alternate their
activity become evident (Carrillo-Reid et al.,2009a). The sole
presence of these sequences (once their stochastic appearance
has been discarded), that resemble computational routines,
suggests that the tissue is storing these trajectories in the
absence of long-range connections. In fact, they can be
recovered in ex vivo slices after learning a task in vivo
(Yin et al.,2009). They can be identified, quantified and
followed through several methods to show the action of
neuromodulators (Carrillo-Reid et al.,2009a,2011) and
pathological states (Jáidar et al.,2010). But many more issues
need investigation, for example: what happens with stable
sequences and networks after LTP and LTD protocols usually
applied to individual synapses.
Neuronal ensembles in
pathological brain tissue
This work emphasizes the histological scale since biopsies
have been a main tool in clinical practice to reach diagnosis
and prognosis. This makes brain slices a potential translational
preparation: bringing the possibility to have living tissue
to do functional histology benchside, or even besides the
surgery room. Living tissue can be asked whether it can
show ensemble sequences or networks that correlate with
pathology (Cardin et al.,2010;Rickgauer et al.,2014;
Emiliani et al.,2015;Carrillo-Reid et al.,2016;Parker et al.,
2018;Peña-Rangel et al.,2021). For instance, pre-clinical
investigation shows that while control striatum exhibits a
definite sequence of neuronal ensembles (Carrillo-Reid et al.,
2008), the parkinsonian striatum (dopamine depleted) shows
ensembles stuck in highly recurrent sequences becoming
an observational metaphor of patients‘ immobility: akinesia,
hypokinesia and rigidity (Jáidar et al.,2010,2019;Plata et al.,
2013a,b;Pérez-Ortega et al.,2016). L-DOPA induced dyskinesia
showed more frequent and complex ensemble transitions
than in the control, and a persistent recurrence due to the
underlying parkinsonism (Pérez-Ortega et al.,2016;Calderón
et al.,2022), again, being a tissue “fingerprint” of patients
with abnormal involuntary movements. In the motor cortex
ensemble sequences accompanied by interneurons in the
control, get reduced to two main recurrent ensembles with
pyramidal cells and interneurons separated during epileptiform
discharges (Serrano-Reyes et al.,2020). After identifying
characteristic changes in pathological preparations, it was logical
to ask the tissue whether it can show circuit changes induced by
drugs of clinical use to treat diseased states.
Testing drugs with therapeutic
potential
In vitro preparations can be used as bioassays to test
clinical useful drugs using the neuronal ensembles framework.
For example, dopamine agonists have been tried to replace
or be adjuvants to L-DOPA in the treatment of Parkinson’s
disease. Many ligand-binding and biochemical studies have been
summarized in an encompassing meta-analysis that classified
these drugs in order of efficiency (Millan,2010). It was found
that basically the same order of efficiency is encountered
when testing dopamine agonists by using the analyses of
ensemble sequences (Lara-González et al.,2019). Vectorizing
the properties of ensembles sequences and networks, principal
component analysis revealed differences between two widely
used anti-dyskinetic drugs (Calderón et al.,2022). Other
clinically relevant drugs and disease states need to be tested to
standardize this methodology. Standardization may lead to the
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testing of novel drugs with potential therapeutic use with greater
efficacy and in lesser time.
Concluding remarks
Multi-recording techniques have confirmed that neuronal
circuits in many brain areas are composed by neuronal
ensembles that alternate their activity forming defined and
recurrent temporal sequences. By assuming that the complexity
of neuronal connectivity can be represented by functional
connections and that the synaptic weights that instantiate these
circuits are relatively stable, one can observe these temporal
sequences as the manifestations of memory traces following
computational routines that can be recorded in vitro and
in vivo. Techniques of neuronal populations analysis are being
developed to quantify, follow and decodify these computations.
Recordings in vivo can be aligned with behavioral tasks and can
be manipulated opto- and chemo-genetically to alter behavior
showing causality. Recordings in vitro can show pathological
changes from animal models as well as the actions of clinically or
potentially useful drugs and therapeutical procedures. Because
this experimental paradigm is at its beginnings, standardization
and consensual definitions are still lacking. However, the sole
observation of neuronal ensembles trajectories, formation and
dissolution, opens many questions for future research: what are
the precise mechanisms at the cellular-synaptic level, how these
circuits can be manipulated, how can be simulated or modeled
by artificial neural networks of diverse complexity, what are the
limits of scalability up and down, what is the relation of these
phenomena with larger scale observations, e.g., fMRI, what are
the brain areas that use this class of population code and what
are the areas that do not.
Author contributions
MD and EL-G wrote the original draft. MP-O and AF-S
compilated literature and worked on the first draft. JB revised
the drafts and wrote the final version. All authors contributed to
the article and approved the submitted version.
Funding
This work was supported by grants from DGAPA-UNAM
IN 202920 and CONACyT (México) F003-154039 to JB. MP-O
receives scholarship 824264, AF-S receives scholarship 1036962
and EL-G received fellowship 770669 all from CONACyT
(México). MD was a Latin American Fellow in the Biomedical
Sciences, supported by The Pew Charitable Trusts and
SECTEI/154/2021.
Acknowledgments
We thank Antonio Laville and Dagoberto Tapia for
technical support.
Conflict of interest
The handling editor LC-R declared a shared affiliation with
the authors MP, AF-S and JB at the time of review.
The remaining authors declare that the research was
conducted in absence of any commercial or financial
relationships that could be construed as a potential conflict
of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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