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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.
fnsys-16-979680 August 20, 2022 Time: 10:56 # 1
TYPE Mini Review
PUBLISHED 24 August 2022
DOI 10.3389/fnsys.2022.979680
Luis Carrillo-Reid,
National Autonomous University
of Mexico, Mexico
Osvaldo Ibáñez-Sandoval,
Autonomous the University of San Luis
Potosi, Mexico
José Bargas
Mariana Duhne
RECEIVED 27 June 2022
ACCEPTED 27 July 2022
PUBLISHED 24 August 2022
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
© 2022 Lara-González,
Padilla-Orozco, Fuentes-Serrano,
Bargas and Duhne. This is an
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Translational neuronal
ensembles: Neuronal
microcircuits in psychology,
physiology, pharmacology and
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
neuronal ensembles, neuronal networks, functional connections, synaptic weights,
population coding, Parkinson’s disease, L-DOPA induced dyskinesia, epileptiform
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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
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
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
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.
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
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
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authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
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claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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Frontiers in Systems Neuroscience 08
... Recently developed technologies for numerous simultaneous recordings and the computing power to evaluate them have led to a controversy over approaches that attempt to comprehend multicellular recordings and neuronal populations without sacrificing or omitting single cell resolution. The discovery that brain neurons do not act alone but rather collaborate to form groupings known as neuronal ensembles, which exhibit spatiotemporal coactivation, is significant (Yuste, 2015;Lara-González et al., 2022). When neurons in an ensemble are engaged in spontaneous, stimulated, diseased, or task-related activity, they fire in a coordinated manner (Ikegaya et al., 2004;Harris et al., 2011;Pérez-Ortega et al., 2016;Hamm et al., 2017;Sheng et al., 2019;Siniscalchi et al., 2019). ...
... The neural networks with emergent populational features have connections made between neuron groups rather than between individual neurons (Figure 1; Hopfield, 1982;Buzsáki, 2010;Kampa et al., 2011;Semedo et al., 2019;Rossi-Pool et al., 2021). Hebb (1949) proposed the neuronal ensembles theory as cell assemblies, and many research teams have recently refined it to propose them as the fundamental nervous system processing units (Figure 1; Buzsáki, 2010;Carrillo-Reid, 2021;Grillner, 2020Grillner, , 2021Lara-González et al., 2022). Although they may be referred to by different names, they commonly share several characteristics (Carrillo-Reid, 2021) and can be identified depending on context, i.e., responding together to stimulus (e.g., sensory inputs), causing an output (e.g., behavior), or representing experimental conditions (e.g., pathological states). ...
... (B) A low-dimensional UMAP projection of the significant peaks of coactivity (V P ) that identify the significant coactive matrices from the raster plot. The colors denote identified neuronal ensembles, each in a specific niche in the UMAP space, several alternative trajectories (Figure 1) have historically been associated with underlying mechanisms of brain functions (Hebb, 1949;Buzsáki, 2010;Carrillo-Reid, 2021;Lara-González et al., 2022). Next, each experimental condition is described. ...
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A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: “fingerprints” of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.
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Long-term potentiation (LTP) and long-term depression (LTD) comprise the principal cellular mechanisms that fulfill established criteria for the physiological correlates of learning and memory. Traditionally LTP, that increases synaptic weights, has been ascribed a prominent role in learning and memory whereas LTD, that decreases them, has often been relegated to the category of “counterpart to LTP” that serves to prevent saturation of synapses. In contradiction of these assumptions, studies over the last several years have provided functional evidence for distinct roles of LTD in specific aspects of hippocampus-dependent associative learning and information encoding. Furthermore, evidence of the experience-dependent “pruning” of excitatory synapses, the majority of which are located on dendritic spines, by means of LTD has been provided. In addition, reports exist of the temporal and physical restriction of LTP in dendritic compartments by means of LTD. Here, we discuss the role of LTD and LTP in experience-dependent information encoding based on empirical evidence derived from conjoint behavioral and electrophysiological studies conducted in behaving rodents. We pinpoint the close interrelation between structural modifications of dendritic spines and the occurrence of LTP and LTD. We report on findings that support that whereas LTP serves to acquire the general scheme of a spatial representation, LTD enables retention of content details. We argue that LTD contributes to learning by engaging in a functional interplay with LTP, rather than serving as its simple counterpart, or negator. We propose that similar spatial experiences that share elements of neuronal representations can be modified by means of LTD to enable pattern separation. Therewith, LTD plays a crucial role in the disambiguation of similar spatial representations and the prevention of generalization.
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Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. As a demonstration of the utility of the algorithm, we monitor for several days calcium activity of the same neurons in layer 2/3 of mouse visual cortex in vivo, finding significant turnover within the active neurons across days, with only few neurons that remained active across days. Also, combining automatic tracking of single neuron activity with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra, finding three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results show that the EMC2 algorithm can be used as a robust and versatile platform for neuronal tracking in behaving animals.
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In the mammalian brain, cortical interneurons (INs) are a highly diverse group of cells. A key neurophysiological question concerns how each class of INs contributes to cortical circuit function and whether specific roles can be attributed to a selective cell type. To address this question, researchers are integrating knowledge derived from transcriptomic, histological, electrophysiological, developmental, and functional experiments to extensively characterise the different classes of INs. Our hope is that such knowledge permits the selective targeting of cell types for therapeutic endeavours. This review will focus on two of the main types of INs, namely the parvalbumin (PV+) or somatostatin (SOM+)-containing cells, and summarise the research to date on these classes.
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Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons were commonly active across any two imaging sessions. These 'common neurons' formed stable ensembles lasting weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved ~68 % of their neurons up to 46 days, our longest imaged period, and these 'core' cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.
Microcircuits are the specific arrangements of cells and their connections that carry out the operations unique to each brain region. This resource summarizes succinctly these circuits in over 40 regions - enabling comparisons of principles across both vertebrates and invertebrates. It provides a new foundation for understanding brain function that will be of interest to all neuroscientists.
The palatability and concentration of sweet foods promote hedonic feeding beyond homeostatic need. Understanding how neurons respond to sweet taste is thus of great importance. The dorsomedial nucleus accumbens shell (dNAcMed) is considered a “sensory sentinel,” promoting hedonic feeding. However, it is unknown how neurons in the lateral part (NAcLat) respond to oral sucrose stimulation. Using in vivo calcium imaging of individual D1 and D2 cells in NAcLat of mice performing behavioral licking tasks, we find that D1 and D2 neurons do not act as single homogeneous populations. Instead, their responses are organized into ensembles with context-dependent temporal dynamics around licking sucrose. At the macrostructure of licking (meals), D1 and D2 population activity recorded on the first day predict the licking behavior on subsequent days. However, at the level of the microstructure of licking (bouts), calcium activity increased concurrently in D1 and D2 neurons prior to licking bouts, whereas during licking, calcium signals decreased. Importantly, in a brief access taste task, calcium responses for D1 and D2 exhibit much more heterogeneity than during a freely licking task. Specifically, D1 and D2 neurons form distinct ensembles: some ramp up in anticipation of the first lick, some respond at the end of the taste-access period, and some categorize sucrose concentrations as low or high. Collectively, NAcLat D1 and D2 neurons are organized in ensembles that adapt to the behavioral context to monitor task-relevant events and sucrose concentrations.
Associative learning restructures the activity of numerous neurons distributed across cortical and subcortical regions. Individual neurons change the rate or timing of spiking patterns in response to environmental stimuli as they become associated with salient outcomes. Recent large–scale activity monitoring in rodents has uncovered that these learning-related changes occur concertedly across groups of neurons within and between brain regions. These changes yield neuronal representations of learned associations in three types of ensemble dynamics: ensemble firing rates, multineuron coactivity, and sequential activity. Here, I review some of the most robust demonstrations of these dynamics in the rodent neocortex and hippocampus and discuss their potential function in memory encoding, consolidation, and retrieval.
Amantadine and clozapine have proved to reduce abnormal involuntary movements (AIMs) in preclinical and clinical studies of L-DOPA-Induced Dyskinesias (LID). Even though both drugs decrease AIMs, they may have different action mechanisms by using different receptors and signaling profiles. Here we asked whether there are differences in how they modulate neuronal activity of multiple striatal neurons within the striatal microcircuit at histological level during the dose-peak of L-DOPA in ex-vivo brain slices obtained from dyskinetic mice. To answer this question, we used calcium imaging to record the activity of dozens of neurons of the dorsolateral striatum before and after drugs administration in vitro. We also developed an analysis framework to extract encoding insights from calcium imaging data by quantifying neuronal activity, identifying neuronal ensembles by linking neurons that coactivate using hierarchical cluster analysis and extracting network parameters using Graph Theory. The results show that while both drugs reduce LIDs scores behaviorally in a similar way, they have several different and specific actions on modulating the dyskinetic striatal microcircuit. The extracted features were highly accurate in separating amantadine and clozapine effects by means of principal components analysis (PCA) and support vector machine (SVM) algorithms. These results predict possible synergistic actions of amantadine and clozapine on the dyskinetic striatal microcircuit establishing a framework for a bioassay to test novel antidyskinetic drugs or treatments in vitro.
Movement initiation and control require the orchestrated activity of sensorimotor cortical and subcortical regions. However, the exact contribution of specific pathways and interactions to the final behavioral outcome are still under debate. Here, by combining structural lesions, pathway-specific optogenetic manipulations and freely moving electrophysiological recordings in rats, we studied cortico-striatal interactions in the context of forelimb bilaterally coordinated movements. We provide evidence indicating that bilateral actions are initiated by motor cortical regions where intratelencephalic bilateral cortico-striatal (bcs-IT) projections recruit the sensorimotor striatum to provide stability and duration to already commanded bilateral movements. Furthermore, striatal spiking activity was correlated with movement duration and kinematic parameters of the execution. bcs-IT stimulation affected only the representation of movement duration but spared that of kinematics. Our findings confirm the modular organization of information processing in the striatum and its involvement in moment-to-moment movement control but not initiation or selection.
A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population-level thinking holds for cognitive neuroscience—for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.