Abstract
“Neuronal assemblies” are defined here as coalitions within the brain of millions of neurons extending in space up to 1–2 mm, and lasting for hundreds of milliseconds: as such they could potentially link bottom-up, micro-scale with top-down, macro-scale events. The perspective first compares the features in vitro versus in vivo of this underappreciated “meso-scale” level of brain processing, secondly considers the various diverse functions in which assemblies may play a pivotal part, and thirdly analyses whether the surprisingly spatially extensive and prolonged temporal properties of assemblies can be described exclusively in terms of classic synaptic transmission or whether additional, different types of signaling systems are likely to operate. Based on our own voltage-sensitive dye imaging (VSDI) data acquired in vitro we show how restriction to only one signaling process, i.e., synaptic transmission, is unlikely to be adequate for modeling the full profile of assemblies. Based on observations from VSDI with its protracted spatio-temporal scales, we suggest that two other, distinct processes are likely to play a significant role in assembly dynamics: “volume” transmission (the passive diffusion of diverse bioactive transmitters, hormones, and modulators), as well as electrotonic spread via gap junctions. We hypothesize that a combination of all three processes has the greatest potential for deriving a realistic model of assemblies and hence elucidating the various complex brain functions that they may mediate.
Features of Neuronal Assemblies
“Neuronal assemblies” (; Singer et al., 1997) are large scale coalitions of neurons that operate in collective activity over a spatio-temporal scales of millimeters and milliseconds, i.e., at “meso-scale” level of brain organization thereby linking cellular (micro-scale) and entire neuronal systems (macro-scale) events (). However, this definition is not universally accepted: the same term has been used for different, yet sometimes overlapping entities, such as anatomically defined cortical columns (Krueger et al., 2008): conversely the same phenomenon featured here, namely the dynamic patterns generated by thousands and tens of thousands of co-active cells, has been referred to in alternative terms, such as “ensembles” (Pais-Vieira et al., 2013; Miller et al., 2014; ).
If the defining feature then, is one of dynamism, then one of the most effective ways of studying assemblies is with molecular probes, which anchor within fatty environments of neuronal cell membranes, and are sensitive to real-time changes in membrane electrical potentials (Vm) (Loew, 1996). Voltage-sensitive dyes (VSDs), which possess electrochromic properties that enable them to report ongoing electrical potential changes (), such as di-4-ANEPPS (Tominaga et al., 2000; Petersen et al., 2003a,b), are particularly effective. Di-4-ANEPPS is one of the most useful and versatile VSDs – it is a red dye which produces data with relatively high signal-to-noise ratio and more commonly used in vitro (, ; Preuss and Stein, 2013; ), which has been implicated as a less toxic alternative to blue dyes (themselves originally developed for in vivo applications) and which can also be used in vivo (). Using mathematical analysis software, such as MatLab (Mathworks) or Mathematica (Wolfram), three-dimensional voltage-sensitive dye imaging (VSDI) data sets (fluorescence × space × time) can be processed in any way, shape or form using bespoke analysis scripts and codes. Whilst both electrophysiology and VSDI offer high temporal (millisecond) resolution, only the latter reveals spatial features (micrometers). For example, whilst the spread, time course and amplitude of optical activity signals are among the most popular parameters for describing assemblies, other less obvious yet appropriate measurements for the particular experiment at hand have emerged, such as “summed overall fluorescence” (), “Time-to peak” (; ) or population activity propagation speed (Yuste et al., 1997; Tominaga et al., 2000), to name but a few. Though it remains difficult to unequivocally attribute specific physiological meaning to each of these parameters, they still reflect the summed output of veritable dynamics of population activity.
Assemblies will to some extent feature specific spatio-temporal profiles determined by the network-specific cytoarchitecture of particular brain regions: for example, fast, low amplitude responses are typical of sub-cortical relay structures (such as the thalamus and basal forebrain) compared to those of cortex, which are comparatively more extensive in time and space (). However, additionally to the specific physical network cytoarchitecture, these characteristics can also be much influenced by the experimental preparation and protocol. There is an inevitable trade-off between investigation of assemblies under more holistic and physiological conditions (such as those seen in vivo) compared to the reductionist, albeit more controlled scenario of limited neuronal connectivity, as seen in vitro, which nonetheless gives direct access to brain regions other than cortex: the approaches are complementary and equally necessary.
In vitro experiments are performed on either slices cut in the coronal plane, or in a parasagittal section () preserving thalamo-cortex connectivity: in either case the full depth of the cortex can be investigated, whereas in vivo experiments focus on a top-down dorsal visualization (i.e., looking down onto the pial surface of the cortex once the skull has been removed), where there is an inevitably greater focus on superficial layers. More specifically: in vivo protocols reveal that the blue dye RH-1619 penetrates 350–400 μm into the depth of the cortex from the pial surface, after a 2 h-long staining period, providing information on activity within layers I and II/III (Petersen et al., 2003a). By comparison, for in vitro experiments, VSDs have been reported to penetrate approximately 100 μm within slice tissue after 30 min-long staining periods using the red dye di-4-ANEPPS (), providing fluorescence information originating from sufficient volumes of neuronal tissue in all layers.
An important and general factor could be that the slice preparation removes any influence of more global signaling systems: destruction of the overall organization of the brain and the inevitable disruption of all long-range connections, such as the diffuse monoaminergic ascending systems from midbrain/brainstem nuclei (; ; Levitt and Moore, 1978), which will lead to a substantially reduced tonic neuromodulatory influence of signaling molecules, such as dopamine, noradrenaline, and serotonin. Compounding this lack of neuromodulatory influence, the existence of other neurotransmitter systems (other than monoaminergic) also implicated in neuromodulation of network activity via extra-synaptic receptor (tonic) activation (Nakanishi, 1992; ; ), a mechanism of volume transmission (Vizi et al., 2004), are also lost in slice preparations. Such mechanisms of neurotransmission have been implicated for acetylcholine (), glutamate (; Min et al., 1998; Szapiro and Barbour, 2007), GABA (Kullmann, 2000; Mody, 2001; Olah et al., 2009) and many more less familiar messengers, such as hormones and neuropeptides (; Trueta and De-Miguel, 2012). Hence, the complex population dynamics reported in vivo will show much simplified profiles when recorded in vitro, where sub-cortical systems, long-range connectivity and extra-synaptic volume concentrations of various bioactive molecules will no longer play a decisive role in gating the full processing abilities of cortical networks.
For example, neuronal assemblies generated in vivo in various cortical areas (; Rokem et al., 2010; Pinto et al., 2013), show a similar overall activity as their in vitro counterparts, i.e., thalamocortical, somatosensory or visual cortical slices (; Sato et al., 2012; Soma et al., 2012), yet retain more complex profiles, such as large depolarisations accompanied by inhibition that is both spatial (surround inhibition) and temporal (rebound hyperpolarisation), presumably in both cases to enhance the signal-to-noise ratio (Petersen et al., 2003a; ; ). This effect, however, can be abolished by deepening anesthesia, suggesting that it operates a significant physiological function in information processing. Such a notion is further supported by the fact that assemblies can also be dramatically modulated by systemic administration of other bioactive substances, from the silencing effects of anesthetics (; ) to the broad and erratic epileptiform activity induced by agents such as gabazine or bicuculline (Lippert et al., 2007). Moreover, assemblies can become less extensive, in response to identical stimuli, in adult compared with juvenile animals (), further suggesting assemblies are highly dependent on context-specific factors and play a part in on-going functions.
Functions of Neuronal Assemblies
Drawing on data from both in vitro and in vivo studies, a wide range of brain functions can now be better understood by reference to assemblies (von Stein and Sarnthein, 2000; ), from visual processing (Vucinic and Sejnowski, 2007; ; Miller et al., 2014) to impact of depth of anesthesia on evoked sensory responses (), impact of learning-induced plasticity on assembly size and dynamics (), as well as revealing previously unappreciated but basic differences between analgesics, (morphine and gabapentin), and anesthetics, (thiopental and propofol) ().
Yet whilst known functions can be more accurately described in terms of activity patterns, assemblies themselves might be a good starting point for understanding previously elusive functions. Their emergent spatio-temporal profile typically is one of hundreds of milliseconds, a time-course roughly three orders of magnitude greater than the action potential which trigger them – between 0.2 and 0.7 ms (; ): this collective, network-wide output could correspond to one-off, unique brain states, such as eventually a moment of consciousness, for the following reasons. First, neural activity only appears to contribute to a state of consciousness when it is continuous and sustained (): the observed time-windows of activity, lasting several hundred milliseconds, are found to coincide with the time taken for conscious perception of stimuli; which occurs at the crucial threshold of 270 ms (Vogel et al., 1998; Sergent et al., 2005). Secondly anesthetics, which by definition abolish consciousness, significantly retard specific parameters of individual assembly dynamics (such as peak width and termination of activity) both in vitro () and in vivo (). Thirdly, a time window of approximately this length demarcates the earliest spatial differentiation of distinct patterns in assemblies for subjective differentiation of sensory modalities (). Fourthly, the energy will need to be conserved in some chemical, electrical, or thermal form (). In the case of heat, pressure in the neuronal micro-environment will increase, and vice versa: perhaps this could explain why increased pressure and hence an increase in thermal energy, will lead to both the onset of consciousness in anesthetized animals () as well as a significant increase in assembly size (Wlodarczyk et al., 2006). Taken individually, these arguments are each relatively weak for linking neuronal assembly function to consciousness. However, we believe that these findings represent interesting coincidences, which, collectively hint at a possible link.
Rather than single unit activity and isolated synaptic signaling, it is neuronal assemblies that perhaps can be more accurately regarded as the building blocks of the central nervous system (Rinkus, 2010; ; Miller et al., 2014): they provide the all-important link enabling bottom-up cellular events to be realized as top-down functions. Yet little is known about how such translation is possible. A first step will be to understand the mechanisms responsible for the generation and propagation of assemblies themselves; which drive them to spread more extensively in both space and time, thereby granting them much greater informational receptive fields as well as greater time-windows for integration of information and processing, compared to that which traditional neuronal signaling would otherwise permit.
Underlying Mechanisms Governing Assembly Dynamics
Synaptic Transmission
Synaptic transmission is the well-known signature of neuronal signaling with time-frames of information transfer down axons of 0.1–100 m/s, and time-delays for information to cross synapses (at 38°C) of around 150–300 μs (Sabatini and Regehr, 1999). However, some anomalies become immediately evident when comparing assemblies generated in two different, well-established in vitro preparations: first, coronal brain slices where assemblies are evoked using direct electrical stimulation to the cortex (Yuste et al., 1997; Zochowski et al., 2000; Petersen and Sakmann, 2001; ), and secondly, a thalamocortical section (; Takashima et al., 2001; Llinas et al., 2002; ) that enables indirect, remote activation of the cortex region via neuronal innervation resulting from thalamic stimulation. Other studies have also investigated the downstream cortico-cortical connectivity elicited by exogenous activation of the lemniscal pathway, leading for example to active communication between primary somatosensory cortex and motor cortex () or for the purposes of general brain mapping in vivo (Lim et al., 2012).
Direct stimulation with a single electrical pulse evokes activity from an epicenter with fast and efficient recruitment of large numbers of neurons in near-synchronous fashion manifesting as a circular propagating wave of activity spreading outwards from the locus of stimulation (Lopes da Silva, 1991) as seen using VSDI, Figure 1A. This is a stereotypical activation pattern which has been reported in virtually all studied neuronal population systems using VSDI, both in the cortex and sub-cortical structures (), and it is those dynamics which can be modulated with bioactive compounds. Signal propagation via action potentials traveling down axons and activating chemical transmitters at synapses with the neurons it contacts takes just over 1 ms. The speed of action potential propagation varies significantly across circuits in the brain as well as with the distance they travel, but even the slowest signals (traveling through unmyelinated axons) take 0.5 ms to travel 1 mm, while subsequent transmitter release and diffusion across the synaptic cleft is approximated to take just under 0.75 ms. The activation of synapses has been found to decay with time-scales that go from a few ms (e.g., for synapses rich in GABAA and/or AMPA receptors) to 100 ms (for those most influenced by GABAB or NMDA receptors). However, if this was the dominant mechanism at play, given the speed of transmission, the greatest activity would most probably be observed furthest from where the stimulus was received, i.e., at the spreading perimeter (Figure 2A), a configuration which conflicts with the data. Under normal conditions in direct stimulation paradigms, assemblies usually reach maximum lateral spread within 5–6 ms after stimulus delivery (Figure 1B), while by comparison, for remote thalamocortical stimulation (i.e., neuronal activation of cortical tissue), this occurs between 10 and 13 ms after stimulation, as seen in Figure 1C, is delivered to the cortex (i.e., with time delay corrected for impulse conduction time from thalamus to cortex – of the order of 4–5 ms) (Landisman and Connors, 2007); i.e., where assemblies are evoked in a more physiological manner than those triggered with direct electrical stimulation. This scenario suggests that other factors, in addition to traditional synaptic transmission, may be affected by this difference in stimulation paradigm; leading to the emergence of different profiles of activation dynamics and resulting time courses.
FIGURE 1
FIGURE 2
In terms of time, it takes some 300 ms for decay of assembly activity to fall to even 20% of its maximum strength (). The dye used to visualize voltage changes has a latency and decay time of the order of a millisecond or less (Tominaga et al., 2000): hence, the persistent activity observed must be a genuine physiological phenomenon. This continuation of activity over several hundred milliseconds could be attributable to the prolonged duration of signal decay time-scale of synapse operations that can last up to 100 ms (). However, if synaptic transmission were the sole mechanism, the greatest activity would be observed furthest from where the stimulus was received, i.e., at the spreading perimeter (Figure 2A): yet this configuration conflicts with the experimental data (Figure 1). Alternatively, the typically lengthy time frame of an assembly could represent the summation of thousands of sequential synaptic connections whereby the cumbersome process of neurotransmission will impede the signal speed. However, this scenario too can potentially be discounted if spatial features are now considered. There are two possible scenarios which could account for the characteristic and extensive assembly spatial spread. One option is that, most probably at the locus of stimulation, a small proportion of directly activated neurons fire action potentials, which in turn propagate potentials affecting the resting membrane potential of target neurons with which they form synapses. In this case, the length-scale of interaction will be a function of synaptic connectivity, network coupling strength (balance between inhibition and excitation) and synaptic dynamics where the firing of a neuron at any given point will affect, above the reference threshold, neurons about 50% further than the connectivity length-scale (). Typically (Perin et al., 2011), this metric implies a spatial spread that will still be, nonetheless, two to five times less than actually observed, and can thus be discounted.
The second and more plausible option as the underlying dominant process, and one in any case that is preferentially detectable with voltage sensitive dyes (): a widespread sub-threshold depolarisation. If so, a second signaling mechanism is needed that could accommodate the extensive spatial spread. Just such a mechanism is also suggested by a discrepancy between predictions from synaptic transmission alone, and what is empirically observed in time frames (Figure 1C): whilst the transmission of a signal from thalamus to cortex, via classic synaptic transmission, takes only 5 ms to travel some 1–2 mm, a further 20 ms is required for the assembly to spread within the cortex, to its full extent.
Volume Transmission
Volume transmission enables interaction between neurons in a way that is much less specific and significantly slower, yet with the pay-off that it involves far more cells at any one time: it is considered a complementary counterpart to classic synaptic “wired” transmission (; Taber and Hurley, 2014). In fact, it has been known since the 1970s that classic transmitters such as dopamine can be released (Nedergaard et al., 1989) as can protein () from a part of the neuron dendrites that typically has a very different role. Normally, dendrites, traditionally regarded as being the target for incoming connections, can actually release substances in their own right, and do so independent of the action potentials generated at the cell body (). Moreover, this dendritic release also affects a much wider area than standard synaptic transmission, is far less precise and uses different ionic mechanisms and cellular storage, – all suggesting a contrasting yet complementary modulatory process. In addition to neuro-active chemical release from dendrites, there are a wide range of afferent fibers originating from sub-cortical structures, specialized in releasing neuromodulatory chemicals, such as acetylcholine, dopamine, noradrenaline, and serotonin (to name but a few), which do not form traditional synapses, but instead are specialized in releasing large amounts of modulatory transmitters into the extracellular space for tonic influence of population activity (Seguela et al., 1987; Soghomonian et al., 1989; ). Every neurotransmitter system potentially influences neuronal networks in a tonic manner (Zoli et al., 1999; Kullmann, 2000), i.e., via volume transmission, as would the presence of extra-synaptic glutamatergic and GABAergic receptors suggest (), and as has already been widely reported for monoaminergic and cholinergic systems (; ). In addition to these well known transmitters, a wide range of other endogenous signaling molecules act exclusively via volume transmission: growth factors (), hormones (Mody, 2008), and peptides (), confirming that volume transmission is an essential and effective mechanism of cell signaling, operating on both high time- and spatial-scales. But whilst synaptic transmission is too local and too fast, the passive diffusion of bioactive agents beyond the synapse, volume transmission, is too slow a mechanism for the generation of assemblies (Figure 2B), In order to counterbalance the slow speed of the extensive extra-synaptic outreach of volume transmission, what is required now to accomodate the characteristic dynamics of an assembly, is an ultra fast signaling system (Steriade, 2005).
Electromagnetic Transmission
Electrotonic spread via gap junctions (; ) is a widespread mode of close-range and high-speed neuronal signaling and has been reported to exist both in inhibitory and excitatory networks of coupled neurons (Tamas et al., 2000; Traub et al., 2001a). Gap junctions are a form of intercellular connection, where the trans-membrane pores formed by a congregation of connexin proteins in the membrane of two neighboring cells creates an open channel, such that the cytoplasm of two cells are effectively continuous, allowing free flow of ions and therefore: electrical pulses. In addition to neurons, connexin proteins are expressed in a range of cells involved in neuronal communication, including glial cells, where gap junctions also mediate substantial parts of their communication (Nagy and Rash, 2000; Orthmann-Murphy et al., 2008), allowing them to carry out essential roles in the maintenance of network health for appropriate functioning (Watkins and Maier, 2002). These low resistance connections have been mapped throughout the brain (Nagy et al., 2001), highlighting their ubiquitous expression and function throughout the CNS in inhibitory (; Tamas et al., 2000; Krook-Magnuson and Huntsman, 2005; ) and excitatory neurons (Spray and Bennett, 1985; Traub et al., 2001b). Gap junctions have been found to play a key role in operating a range of functions including neuronal differentiation (), synaptogenesis and circuit formation (; Peinado et al., 1993; ), yet the role for which they are considered here as a key method of cell signaling in assembly generation is for their permissive capacity in transmembrane impulse propagation (Spray and Bennett, 1985; ), allowing signaling independent of transmitter release (; Leybaert and Sanderson, 2012; ). In accordance with these findings, it has been found that in neuronal networks, very fast oscillations (200 Hz), that could underlie the sustained activity seen here in assemblies, are mediated not via synapses but by these gap junctions (). So if fast oscillations underlie the sustained activity seen here in assemblies (van den Pol, 2012), although they will take longer to effectively reach a maximum, once underway (Petersen et al., 2003a), the spread of activity will reach further than any signal from a synapse ever could. Hence, assemblies will be a very appropriate neuronal correlate for the space-time requirements for consciousness since, unlike localized neuronal circuitry, they are neither hard-wired in time, nor spatially restricted.
The existence of an electrochemical gradient across neuronal cell membranes generates a small electric field (), and the diffusion of ions contributes to the time-variability of this electric field, possibly representing per se a further form of signaling: electromagnetic transmission. Time-variable electric fields induce electromagnetic fields and vice versa (), thus allowing for electromagnetic energy to propagate directly via gap junctions and in the form of electromagnetic waves from the source of the signal, exciting neighboring neurons. Energy conservation will result in the intensity of the energy being transferred to decrease proportionally with the inverse square of the distance from the source. Integrated over the time window necessary to observe a depolarization in the receiving neurons, this mechanism predicts a drop in assembly-spread velocity proportional to the inverse of the distance. Wave-like patterns have been observed throughout the brain, though so far mostly attributed to feed-forward networks (Meijer and Coombes, 2014): we found that the velocity profile as a function of distance for assemblies is consistent with a spatiotemporal spread due to self-sustained electromagnetic waves (Figure 2C).
The mechanism by which radiation can excite neuronal activity, to the point of remotely triggering action potentials, is not unfamiliar (); the most obvious candidate vehicle to host the magnetic field outside the neuron are glial cells, and/or the extracellular matrix: packed with ions, they are available in large quantities and continuously surrounding the network of neurons, providing a coherent medium where electromagnetic waves can propagate self-substainedly. Indeed, the cooperative action of neurons and glia has already been proven capable of influencing the timing of neuronal activity ().
In conclusion, disparate empirical findings both in vivo and in vitro could most readily be accommodated theoretically in the integration of three distinct signaling mechanisms over an epoch of some 250–300 ms (Figure 2D). As such, this approach to analyzing brain operations at the meso-scale would have the potential for a more accurate modeling of drug action and more generally a quantification of holistic brain states with a temporal and spatial resolution commensurate with neurophysiological and neurochemical events.
Statements
Author contributions
A-SB is responsible for the data of Figure 1 and presentation of Figures 1 and 2; FF is responsible for the data of Figure 2. SG provided the original idea as well as background material. All authors contributed to the preparation, writing, and proof-reading of the manuscript.
Funding
The work presented here was funded by the Mind Science Foundation and the European Society of Anaesthesiology (ESA).
Acknowledgments
The authors would like to thank Magnus Richardson for valuable discussion regarding the EIF model.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewers NM and AS and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.
Supplementary material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fncir.2016.00114/full#supplementary-material
References
1
AgmonA.ConnorsB. W. (1991). Thalamocortical responses of mouse somatosensory (barrel) cortex in vitro.Neuroscience41365–379. 10.1016/0306-4522(91)90333-J
2
AgnatiL. F.FuxeK. (2014). Extracellular-vesicle type of volume transmission and tunnelling-nanotube type of wiring transmission add a new dimension to brain neuro-glial networks.Philos. Trans. R. Soc. Lond. B Biol. Sci.36920130505. 10.1098/rstb.2013.0505
3
AnastassiouC. A.MontgomeryS. M.BarahonaM.BuzsakiG.KochC. (2010). The effect of spatially inhomogeneous extracellular electric fields on neurones.J. Neurosci.301925–1936. 10.1523/JNEUROSCI.3635-09.2010
4
AnavaS.SaadY.AyaliA. (2013). The role of gap junction proteins in the development of neural network functional topology.Insect Mol. Biol.22457–472. 10.1111/imb.12036
5
AzmitiaE. C.SegalM. (1978). An autoradiographic analysis of the differential ascending projections of the dorsal and median raphe nuclei in the rat.J. Comp. Neurol.179641–667. 10.1002/cne.901790311
6
BachmannT. (2000). Microgenetic Approach to the Conscious Mind.Amsterdam: John Benjamins Publishing.
7
BadinA. S.EraifejJ.GreenfieldS. (2013). High-resolution spatio-temporal bioactivity of a novel peptide revealed by optical imaging in rat orbitofrontal cortex in vitro: possible implications for neurodegenerative diseases.Neuropharmacology73C10–18. 10.1016/j.neuropharm.2013.05.019
8
BadinA. S.MorrillP.DevonshireI. M.GreenfieldS. A. (2016). (II) Physiological profiling of an endogenous peptide in the basal forebrain: age-related bioactivity and blockade with a novel modulator.Neuropharmacology10547–60. 10.1016/j.neuropharm.2016.01.012
9
Bani-YaghoubM.UnderhillT. M.NausC. C. (1999). Gap junction blockage interferes with neuronal and astroglial differentiation of mouse P19 embryonal carcinoma cells.Dev. Genet.2469–81. 10.1002/(SICI)1520-6408199924:1/2<69::AID-DVG8>3.3.CO;2-D
10
BeaudetA.DescarriesL. (1978). The monoamine innervation of rat cerebral cortex: synaptic and nonsynaptic axon terminals.Neuroscience3851–860. 10.1016/0306-4522(78)90115-X
11
BeierleinM.GibsonJ. R.ConnorsB. W. (2000). A network of electrically coupled interneurons drives synchronized inhibition in neocortex.Nat. Neurosci.3904–910. 10.1038/78809
12
BelelliD.HarrisonN. L.MaguireJ.MacdonaldR. L.WalkerM. C.CopeD. W. (2009). Extrasynaptic GABAA receptors: form, pharmacology, and function.J. Neurosci.2912757–12763. 10.1523/JNEUROSCI.3340-09.2009
13
BelousovA. B.FontesJ. D. (2014). Neuronal gap junction coupling as the primary determinant of the extent of glutamate-mediated excitotoxicity.J. Neural. Transm.121837–846. 10.1007/s00702-013-1109-7
14
BennettM. V. (1997). Gap junctions as electrical synapses.J. Neurocytol.26349–366. 10.1023/A:1018560803261
15
BergerT.BorgdorffA.CrochetS.NeubauerF. B.LefortS.FauvetB.et al (2007). Combined voltage and calcium epifluorescence imaging in vitro and in vivo reveals subthreshold and suprathreshold dynamics of mouse barrel cortex.J. Neurophysiol.973751–3762. 10.1152/jn.01178.2006
16
BernasconiC.von SteinA.ChiangC.KonigP. (2000). Bi-directional interactions between visual areas in the awake behaving cat.Neuroreport11689–692. 10.1097/00001756-200003200-00007
17
BeulS. F.HilgetagC. C. (2014). Towards a ”canonical” agranular cortical microcircuit.Front. Neuroanat.8:165. 10.3389/fnana.2014.00165
18
BlundellS. J.BlundellK. M. (2009). Concepts in Thermal Physics.Oxford: Oxford University Press.
19
BorgdorffA. J.PouletJ. F.PetersenC. C. (2007). Facilitating sensory responses in developing mouse somatosensory barrel cortex.J. Neurophysiol.972992–3003. 10.1152/jn.00013.2007
20
BorstJ. G.HelmchenF.SakmannB. (1995). Pre- and postsynaptic whole-cell recordings in the medial nucleus of the trapezoid body of the rat.J. Physiol.489(Pt 3)825–840. 10.1113/jphysiol.1995.sp021095
21
BourgeoisE. B.JohnsonB. N.McCoyA. J.TrippaL.CohenA. S.MarshE. D. (2014). A toolbox for spatiotemporal analysis of voltage-sensitive dye imaging data in brain slices.PLoS ONE9:e108686. 10.1371/journal.pone.0108686
22
BuzsakiG.DraguhnA. (2004). Neuronal oscillations in cortical networks.Science3041926–1929. 10.1126/science.1099745
23
Carrillo-ReidL.MillerJ. E.HammJ. P.JacksonJ.YusteR. (2015). Endogenous sequential cortical activity evoked by visual stimuli.J. Neurosci.358813–8828. 10.1523/JNEUROSCI.5214-14.2015
24
CarroE.NunezA.BusiguinaS.Torres-AlemanI. (2000). Circulating insulin-like growth factor I mediates effects of exercise on the brain.J. Neurosci.202926–2933.
25
CarterD. A.FibigerH. C. (1977). Ascending projections of presumed dopamine-containing neurons in the ventral tegmentum of the rat as demonstrated by horseradish peroxidase.Neuroscience2569–576. 10.1016/0306-4522(77)90052-5
26
ChakrabortyS.SandbergA.GreenfieldS. A. (2007). Differential dynamics of transient neuronal assemblies in visual compared to auditory cortex.Exp. Brain Res.182491–498. 10.1007/s00221-007-1008-y
27
ChenY.PalmerC. R.SeidemannE. (2012). The relationship between voltage-sensitive dye imaging signals and spiking activity of neural populations in primate V1.J. Neurophysiol.1073281–3295. 10.1152/jn.00977.2011
28
CollinsT. F.MannE. O.HillM. R.DommettE. J.GreenfieldS. A. (2007). Dynamics of neuronal assemblies are modulated by anaesthetics but not analgesics.Eur. J. Anaesthesiol.24609–614. 10.1017/S026502150700004X
29
ConnorsB. W.BenardoL. S.PrinceD. A. (1983). Coupling between neurons of the developing rat neocortex.J. Neurosci.3773–782.
30
de WiedD.DiamantM.FodorM. (1993). Central nervous system effects of the neurohypophyseal hormones and related peptides.Front. Neuroendocrinol.14:251–302. 10.1006/frne.1993.1009
31
DescarriesL.GisigerV.SteriadeM. (1997). Diffuse transmission by acetylcholine in the CNS.Prog. Neurobiol.53603–625. 10.1016/S0301-0082(97)00050-6
32
DescarriesL.MechawarN. (2000). Ultrastructural evidence for diffuse transmission by monoamine and acetylcholine neurons of the central nervous system.Prog. Brain Res.12527–47. 10.1016/S0079-6123(00)25005-X
33
DescarriesL.MechawarN. (2008). Structural Organization of Monoamine and Acetylcholine Neuron Systems in the rat CNS.Berlin: Springer.
34
DevonshireI. M.DommettE. J.GrandyT. H.HallidayA. C.GreenfieldS. A. (2010a). Environmental enrichment differentially modifies specific components of sensory-evoked activity in rat barrel cortex as revealed by simultaneous electrophysiological recordings and optical imaging in vivo.Neuroscience170662–669. 10.1016/j.neuroscience.2010.07.029
35
DevonshireI. M.GrandyT. H.DommettE. J.GreenfieldS. A. (2010b). Effects of urethane anaesthesia on sensory processing in the rat barrel cortex revealed by combined optical imaging and electrophysiology.Eur. J. Neurosci.32786–797. 10.1111/j.1460-9568.2010.07322.x
36
DraguhnA.TraubR. D.SchmitzD.JefferysJ. G. (1998). Electrical coupling underlies high-frequency oscillations in the hippocampus in vitro.Nature394189–192. 10.1038/28184
37
EugeninE. A.BasilioD.SaezJ. C.OrellanaJ. A.RaineC. S.BukauskasF.et al (2012). The role of gap junction channels during physiologic and pathologic conditions of the human central nervous system.J. Neuroimmune Pharmacol.7499–518. 10.1007/s11481-012-9352-5
38
EytanD.MaromS. (2006). Dynamics and effective topology underlying synchronization in networks of cortical neurons.J. Neurosci.268465–8476. 10.1523/JNEUROSCI.1627-06.2006
39
FerezouI.HaissF.GentetL. J.AronoffR.WeberB.PetersenC. C. (2007). Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice.Neuron56907–923. 10.1016/j.neuron.2007.10.007
40
FukudaT.KosakaT.SingerW.GaluskeR. A. (2006). Gap junctions among dendrites of cortical GABAergic neurons establish a dense and widespread intercolumnar network.J. Neurosci.263434–3443. 10.1523/JNEUROSCI.4076-05.2006
41
GandolfiD.MapelliJ.D’AngeloE. (2015). Long-term spatiotemporal reconfiguration of neuronal activity revealed by voltage-sensitive dye imaging in the cerebellar granular layer.Neural Plast.2015:284986. 10.1155/2015/284986
42
GersteinG. L.BedenbaughP.AertsenM. H. (1989). Neuronal assemblies.IEEE Trans. Biomed. Eng.364–14. 10.1109/10.16444
43
GrandyT. H.GreenfieldS. A.DevonshireI. M. (2012). An evaluation of in vivo voltage-sensitive dyes: pharmacological side effects and signal-to-noise ratios after effective removal of brain-pulsation artifacts.J. Neurophysiol.1082931–2945. 10.1152/jn.00512.2011
44
GrayC. M.McCormickD. A. (1996). Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex.Science (New York, N.Y.274109–113. 10.1126/science.274.5284.109
45
GreenbergD. S.HouwelingA. R.KerrJ. N. (2008). Population imaging of ongoing neuronal activity in the visual cortex of awake rats.Nat. Neurosci.11749–751. 10.1038/nn.2140
46
GreenfieldS.CheramyA.LevielV.GlowinskiJ. (1980). In vivo release of acetylcholinesterase in cat substantia nigra and caudate nucleus.Nature284355–357. 10.1038/284355a0
47
GreenfieldS. A. (1985). The significance of dendritic release of transmitter and protein in the substantia nigra.Neurochem. Int.7887–901. 10.1016/0197-0186(85)90136-6
48
GrinvaldA.MankerA.SegalM. (1982). Visualization of the spread of electrical activity in rat hippocampal slices by voltage-sensitive optical probes.J. Physiol.333269–291. 10.1113/jphysiol.1982.sp014453
49
HamaN.ItoS. I.HirotaA. (2015). Optical imaging of the propagation patterns of neural responses in the rat sensory cortex: comparison under two different anesthetic conditions.Neuroscience284125–133. 10.1016/j.neuroscience.2014.08.059
50
HestrinS.SahP.NicollR. A. (1990). Mechanisms generating the time course of dual component excitatory synaptic currents recorded in hippocampal slices.Neuron5247–253. 10.1016/0896-6273(90)90162-9
51
HillM. R.GreenfieldS. A. (2014). Characterization of early cortical population response to thalamocortical input in vitro.Front. Neurosci.7:273. 10.3389/fnins.2013.00273
52
HuangE. P. (1998). Synaptic transmission: spillover at central synapses.Curr. Biol.8R613–R615. 10.1016/S0960-9822(98)70389-6
53
HuangH.DelikanliS.ZengH.FerkeyD. M.PralleA. (2010). Remote control of ion channels and neurons through magnetic-field heating of nanoparticles.Nat. Nanotechnol.5602–606. 10.1038/nnano.2010.125
54
JacksonJ. D. (1962). Classical Electrodynamics.Hoboken, NJ: John Wiley & Sons.
55
JirsaV. K.HakenH. (1996). Field theory of electromagnetic brain activity.Phys. Rev. Lett.77960–963. 10.1103/PhysRevLett.77.960
56
KandelE.SchwartzJ. H.JessellT. M. (2000). Principles of Neuroal Science.New York, NY: McGraw-Hill Medical.
57
KandlerK.KatzL. C. (1995). Neuronal coupling and uncoupling in the developing nervous system.Curr. Opin. Neurobiol598–105. 10.1016/0959-4388(95)80093-X
58
KendigJ. J.GrossmanY.MacIverM. B. (1988). Pressure reversal of anaesthesia: a synaptic mechanism.Br. J. Anaesth.60806–816. 10.1093/bja/60.7.806
59
KercelS. W. (2004). The role of volume transmission in an endogenous brain.J. Integr. Neurosci.37–18. 10.1142/S0219635204000348
60
Krook-MagnusonE.HuntsmanM. M. (2005). Excitability of cortical neurons depends upon a powerful tonic conductance in inhibitory networks.Thalamus Relat. Syst.3115–120. 10.1017/S1472928807000192
61
KruegerJ. M.RectorD. M.RoyS.Van DongenH. P. A.BelenkyG.PankseppJ. (2008). Sleep as a fundamental property of neuronal assemblies.Nat. Rev. Neurosci.9910–919. 10.1038/nrn2521
62
KullmannD. M. (2000). Spillover and synaptic cross talk mediated by glutamate and GABA in the mammalian brain.Prog. Brain Res.125339–351. 10.1016/S0079-6123(00)25023-1
63
LandismanC. E.ConnorsB. W. (2007). VPM and PoM nuclei of the rat somatosensory thalamus: intrinsic neuronal properties and corticothalamic feedback.Cereb. Cortex172853–2865. 10.1093/cercor/bhm025
64
LevittP.MooreR. Y. (1978). Noradrenaline neuron innervation of the neocortex in the rat.Brain Res.139219–231. 10.1016/0006-8993(78)90925-3
65
LeybaertL.SandersonM. J. (2012). Intercellular Ca(2+) waves: mechanisms and function.Physiol. Rev.921359–1392. 10.1152/physrev.00029.2011
66
LimD. H.MohajeraniM. H.LedueJ.BoydJ.ChenS.MurphyT. H. (2012). In vivo large-scale cortical mapping using channelrhodopsin-2 stimulation in transgenic mice reveals asymmetric and reciprocal relationships between cortical areas.Front. Neural Circuits6:11. 10.3389/fncir.2012.00011
67
LippertM. T.TakagakiK.XuW.HuangX.WuJ. Y. (2007). Methods for voltage-sensitive dye imaging of rat cortical activity with high signal-to-noise ratio.J. Neurophysiol.98502–512. 10.1152/jn.01169.2006
68
LlinasR. R.LeznikE.UrbanoF. J. (2002). Temporal binding via cortical coincidence detection of specific and nonspecific thalamocortical inputs: a voltage-dependent dye-imaging study in mouse brain slices.Proc. Natl. Acad. Sci. U.S.A.99449–454. 10.1073/pnas.012604899
69
LoewL. (1996). Potentiometric dyes: imaging electrical activity of cell membranes.Pure Appl. Chem.681405–1409. 10.1351/pac199668071405
70
Lopes da SilvaF. (1991). Neural mechanisms underlying brain waves: from neural membranes to networks.Electroencephalogr. Clin. Neurophysiol.7981–93. 10.1016/0013-4694(91)90044-5
71
MeijerH. G.CoombesS. (2014). Travelling waves in a neural field model with refractoriness.J. Math. Biol.681249–1268. 10.1007/s00285-013-0670-x
72
MillerJ. E.AyzenshtatI.Carrillo-ReidL.YusteR. (2014). Visual stimuli recruit intrinsically generated cortical ensembles.Proc. Natl. Acad. Sci. U.S.A.111E4053–E4061. 10.1073/pnas.1406077111
73
MinM. Y.RusakovD. A.KullmannD. M. (1998). Activation of AMPA, kainate, and metabotropic receptors at hippocampal mossy fiber synapses: role of glutamate diffusion.Neuron21561–570. 10.1016/S0896-6273(00)80566-8
74
ModyI. (2001). Distinguishing between GABA(A) receptors responsible for tonic and phasic conductances.Neurochem. Res.26907–913. 10.1023/A:1012376215967
75
ModyI. (2008). Extrasynaptic GABAA receptors in the crosshairs of hormones and ethanol.Neurochem. Int.5260–64. 10.1016/j.neuint.2007.07.010
76
NagyJ. I.LiX.RempelJ.StelmackG.PatelD.StainesW. A.et al (2001). Connexin26 in adult rodent central nervous system: demonstration at astrocytic gap junctions and colocalization with connexin30 and connexin43.J. Comp. Neurol.441302–323. 10.1002/cne.1414
77
NagyJ. I.RashJ. E. (2000). Connexins and gap junctions of astrocytes and oligodendrocytes in the CNS.Brain Res. Brain Res. Rev.3229–44. 10.1016/S0165-0173(99)00066-1
78
NakanishiS. (1992). Molecular diversity of glutamate receptors and implications for brain function.Science258597–603. 10.1126/science.1329206
79
NedergaardS.WebbC.GreenfieldS. A. (1989). A possible ionic basis for dendritic release of dopamine in the guinea-pig substantia nigra.Acta Physiol. Scand.13567–68. 10.1111/j.1748-1716.1989.tb08552.x
80
OlahS.FuleM.KomlosiG.VargaC.BaldiR.BarzoP.et al (2009). Regulation of cortical microcircuits by unitary GABA-mediated volume transmission.Nature4611278–1281. 10.1038/nature08503
81
Orthmann-MurphyJ. L.AbramsC. K.SchererS. S. (2008). Gap junctions couple astrocytes and oligodendrocytes.J. Mol. Neurosci.35101–116. 10.1007/s12031-007-9027-5
82
Pais-VieiraM.LebedevM. A.WiestM. C.NicolelisM. A. (2013). Simultaneous top-down modulation of the primary somatosensory cortex and thalamic nuclei during active tactile discrimination.J. Neurosci.334076–4093. 10.1523/JNEUROSCI.1659-12.2013
83
PeinadoA.YusteR.KatzL. C. (1993). Extensive dye coupling between rat neocortical neurons during the period of circuit formation.Neuron10103–114. 10.1016/0896-6273(93)90246-N
84
PerinR.BergerT. K.MarkramH. (2011). A synaptic organizing principle for cortical neuronal groups.Proc. Natl. Acad. Sci. U.S.A.1085419–5424. 10.1073/pnas.1016051108
85
PetersenC. C.GrinvaldA.SakmannB. (2003a). Spatiotemporal dynamics of sensory responses in layer 2/3 of rat barrel cortex measured in vivo by voltage-sensitive dye imaging combined with whole-cell voltage recordings and neuron reconstructions.J. Neurosci.231298–1309.
86
PetersenC. C.HahnT. T.MehtaM.GrinvaldA.SakmannB. (2003b). Interaction of sensory responses with spontaneous depolarization in layer 2/3 barrel cortex.Proc. Natl. Acad. Sci. U.S.A.10013638–13643. 10.1073/pnas.2235811100
87
PetersenC. C.SakmannB. (2001). Functionally independent columns of rat somatosensory barrel cortex revealed with voltage-sensitive dye imaging.J. Neurosci.218435–8446.
88
PintoL.GoardM. J.EstandianD.XuM.KwanA. C.LeeS. H.et al (2013). Fast modulation of visual perception by basal forebrain cholinergic neurons.Nat. Neurosci.161857–1863. 10.1038/nn.3552
89
PreussS.SteinW. (2013). Comparison of two voltage-sensitive dyes and their suitability for long-term imaging of neuronal activity.PLoS ONE8:e75678. 10.1371/journal.pone.0075678
90
RinkusG. J. (2010). A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality.Front. Neuroanat.4:17. 10.3389/fnana.2010.00017
91
RokemA.LandauA. N.GargD.PrinzmetalW.SilverM. A. (2010). Cholinergic enhancement increases the effects of voluntary attention but does not affect involuntary attention.Neuropsychopharmacology352538–2544. 10.1038/npp.2010.118
92
SabatiniB. L.RegehrW. G. (1999). Timing of synaptic transmission.Annu. Rev. Physiol.61521–542. 10.1146/annurev.physiol.61.1.521
93
SatoT. K.NauhausI.CarandiniM. (2012). Traveling waves in visual cortex.Neuron75218–229. 10.1016/j.neuron.2012.06.029
94
SeguelaP.WatkinsK. C.descarriesL. (1987). Ultrastructural features of dopamine axon terminals in the anteromedial and the suprarhinal cortex of adult rat.Brain Res.42211–22.
95
SergentC.BailletS.DehaeneS. (2005). Timing of the brain events underlying access to consciousness during the attentional blink.Nat. Neurosci.81391–1400. 10.1038/nn1549
96
SingerW.EngelA. K.KreiterA. K.MunkM. H.NeuenschwanderS.RoelfsemaP. R. (1997). Neuronal assemblies: necessity, signature and detectability.Trends Cogn. Sci.1252–261. 10.1016/S1364-6613(97)01079-6
97
SoghomonianJ. J.DescarriesL.WatkinsK. C. (1989). Serotonin innervation in adult rat neostriatum. II. Ultrastructural features: a radioautographic and immunocytochemical study.Brain Res.48167–86. 10.1016/0006-8993(89)90486-1
98
SomaS.ShimegiS.OsakiH.SatoH. (2012). Cholinergic modulation of response gain in the primary visual cortex of the macaque.J. Neurophysiol.107283–291. 10.1152/jn.00330.2011
99
SprayD. C.BennettM. V. (1985). Physiology and pharmacology of gap junctions.Annu. Rev. Physiol.47281–303. 10.1146/annurev.ph.47.030185.001433
100
SteriadeM. (2005). Sleep, epilepsy and thalamic reticular inhibitory neurons.Trends Neurosci.28317–324. 10.1016/j.tins.2005.03.007
101
SzapiroG.BarbourB. (2007). Multiple climbing fibers signal to molecular layer interneurons exclusively via glutamate spillover.Nat. Neurosci.10735–742. 10.1038/nn1907
102
TaberK. H.HurleyR. A. (2014). Volume transmission in the brain: beyond the synapse.J. Neuropsychiatry Clin. Neurosci.26 iv, 1–4. 10.1176/appi.neuropsych.13110351
103
TakashimaI.KajiwaraR.IijimaT. (2001). Voltage-sensitive dye versus intrinsic signal optical imaging: comparison of optically determined functional maps from rat barrel cortex.Neuroreport122889–2894. 10.1097/00001756-200109170-00027
104
TamasG.BuhlE. H.LorinczA.SomogyiP. (2000). Proximally targeted GABAergic synapses and gap junctions synchronize cortical interneurons.Nat. Neurosci.3366–371. 10.1038/73936
105
TominagaT.TominagaY.YamadaH.MatsumotoG.IchikawaM. (2000). Quantification of optical signals with electrophysiological signals in neural activities of Di-4-ANEPPS stained rat hippocampal slices.J. Neurosci. Methods10211–23. 10.1016/S0165-0270(00)00270-3
106
TraubR. D.KopellN.BibbigA.BuhlE. H.LeBeauF. E.WhittingtonM. A.et al (2001a). Gap junctions between interneuron dendrites can enhance synchrony of gamma oscillations in distributed networks.J. Neurosci.219478–9486.
107
TraubR. D.WhittingtonM. A.BuhlE. H.LeBeauF. E.BibbigA.BoydS.et al (2001b). A possible role for gap junctions in generation of very fast EEG oscillations preceding the onset of, and perhaps initiating, seizures.Epilepsia42153–170. 10.1046/j.1528-1157.2001.4220153.x
108
TruetaC.De-MiguelF. F. (2012). Extrasynaptic exocytosis and its mechanisms: a source of molecules mediating volume transmission in the nervous system.Front. Physiol.3:319. 10.3389/fphys.2012.00319
109
van den PolA. N. (2012). Neuropeptide transmission in brain circuits.Neuron7698–115. 10.1016/j.neuron.2012.09.014
110
ViziE. S.KissJ. P.LendvaiB. (2004). Nonsynaptic communication in the central nervous system.Neurochem. Int.45443–451. 10.1016/j.neuint.2003.11.016
111
VogelE. K.LuckS. J.ShapiroK. L. (1998). Electrophysiological evidence for a postperceptual locus of suppression during the attentional blink.J. Exp. Psychol. Hum. Percept. Perform.241656–1674. 10.1037/0096-1523.24.6.1656
112
von SteinA.SarntheinJ. (2000). Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization.Int. J. Psychophysiol.38301–313. 10.1016/S0167-8760(00)00172-0
113
VucinicD.SejnowskiT. J. (2007). A compact multiphoton 3D imaging system for recording fast neuronal activity.PLoS ONE2:e699. 10.1371/journal.pone.0000699
114
WatkinsL. R.MaierS. F. (2002). Beyond neurons: evidence that immune and glial cells contribute to pathological pain states.Physiol. Rev.82981–1011. 10.1152/physrev.00011.2002
115
WlodarczykA.McMillanP. F.GreenfieldS. A. (2006). High pressure effects in anaesthesia and narcosis.Chem. Soc. Rev.35890–898. 10.1039/b517771p
116
YusteR.TankD. W.KleinfeldD. (1997). Functional study of the rat cortical microcircuitry with voltage-sensitive dye imaging of neocortical slices.Cereb. Cortex7546–558. 10.1093/cercor/7.6.546
117
ZochowskiM.WachowiakM.FalkC. X.CohenL. B.LamY. W.AnticS.et al (2000). Imaging membrane potential with voltage-sensitive dyes.Biol. Bull.1981–21. 10.2307/1542798
118
ZoliM.JanssonA.SykovaE.AgnatiL. F.FuxeK. (1999). Volume transmission in the CNS and its relevance for neuropsychopharmacology.Trends Pharmacol. Sci.20142–150. 10.1016/S0165-6147(99)01343-7
Summary
Keywords
neuronal assemblies, synaptic transmission, volume transmission, gap junctions
Citation
Badin A-S, Fermani F and Greenfield SA (2017) The Features and Functions of Neuronal Assemblies: Possible Dependency on Mechanisms beyond Synaptic Transmission. Front. Neural Circuits 10:114. doi: 10.3389/fncir.2016.00114
Received
03 August 2016
Accepted
22 December 2016
Published
10 January 2017
Volume
10 - 2016
Edited by
Edward S. Ruthazer, McGill University, Canada
Reviewed by
Amir Shmuel, McGill University, Canada; Naguib Mechawar, Douglas Mental Health University Institute, Canada
Updates
Copyright
© 2017 Badin, Fermani and Greenfield.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Antoine-Scott Badin, scott.badin@neuro-bio.com
Disclaimer
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.