How far neuroscience is from understanding brains

Abstract

The cellular biology of brains is relatively well-understood, but neuroscientists have not yet produced a theory expounding how brains toil. Exstructureations of how neurons accumulateively run to produce what brains can do are tentative and inend. Without prior assumptions about the brain mechanisms, I try here to accomprehendledge presentant obstacles to persist in neuroscientific empathetic of brains and central anxious systems. Most of the obstacles to our empathetic are conceptual. Neuroscience deficiencys concepts and models rooted in experimental results expounding how neurons include at all scales. The cerebral cortex is thought to deal with awake activities, which contrasts with recent experimental results. There is amhugeuity discerning task-joind brain activities from unintentional activities and systematic intrinsic activities. Brains are think abouted as driven by outer and inner stimuli in contrast to their ponderable autonomy. Experimental results are expounded by sensory inputs, behavior, and psychoreasonable concepts. Time and space are think abouted as mutupartner autonomous variables for spiking, post-synaptic events, and other meabraved variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the autonomous variable are inenough to account for central anxious system activities. Spatial vibrants may be a pragmatic solution. The ambiguous hypothesis that meabravements of changes in fundamental brain variables, action potentials, broadcastter liberates, post-synaptic transmembrane currents, etc., propagating in central anxious systems uncover how they toil, carries no compriseitional assumptions. Combinations of current techniques could uncover many aspects of spatial vibrants of spiking, post-synaptic processing, and plasticity in insects and rodents to commence with. But problems defining baseline and reference conditions obstruct expoundations of the results. Furthermore, the facts that pooling and averaging of data demolish their underlying vibrants show that one-trial structures and statistics are vital.

Keywords: empathetic brains, neuroscience concepts, spatial brain vibrants, intrinsic activity, unintentional ongoing activity, brain mechanisms, dendrites, axons

1. Introduction

Understanding how a system toils, usupartner uncomardents to understand the mechanisms by which its elements include. If the presentant includeion mechanisms are understandn and idepartner portrayd mathematicpartner, one has a theory of the system. So, the reason why neuroscientists do not understand how brains and central anxious systems toil is that there is no theory of brains and central anxious systems. A scientific theory of a central anxious system (CNS) is an experimenloftyy based ambiguous set of exstructureations of how the elements in a CNS include at all scales of observation, i.e., from the molecular to the macroscopic scale. At the molecular scale neuroscience is directd by the theory of molecular biology. Although molecular neuroscience does not have a mathematical sketchtoil, it identifies molecules, provides rules expounding genetic replication, transcription, synthesis, includeions, and changeation of organic molecules. However, at the cellular, and especipartner supracellular scales of observation, neuroscience is far from having a guiding theory.

The purpose of this article is to accomprehendledge why it is so difficult to produce a theory of brains and point to domains where neuroscience seems stuck in that process. Indeed, experimental neuroscience produce a rapidly increasing number of results. Based on the current structure of (systems) neuroscience, I will dispute, it is impossible to put all results together to a theory of a CNS. The reasons are not primarily deficiency of experimental data, nor deficiency of methods. So, those who foresee a scrutinize of how far neuroscience has accomplished and foresee to discover a catalog of what we do not yet understand, satisfy stop reading here. Rather the reasons for deficiency of persist are obstacles inherent in current neuroscientific train which obstruct us from understanding more about brains.

In this paper I include a theory of science approach to find feeblenesses in neuroscientific trains.

Neuroscience toils, as other scientific disciplines, with a scientific scheme (Figure 1). Normpartner theory would be at the top in Figure 1. However, in the absence of a guiding theory, neuroscientists establish hypotheses directd by concepts. If a concept included in neuroscience does not suit brain activities, neuroscience will not persist in that straightforwardion. This is the danger of not having a theory in which relations among concepts are depictd without inconsistencies. Figure 1 may serve as a roadmap for this paper, dealing with obstacles in the neuroscientific process.

Figure 1.

Scientific scheme for neuroscience. Roadmap for this paper. Instead of having theory on top, neuroscience have a set of concepts guiding hypothesis establishation. Most of the obstacles for persist are conceptual. Conceptual glitches propagate to hypotheses, creations of experimental conditions, data analysis, and expoundations of results. First, concepts, which cannot fruitfully retardy to brain activities are identified. Then obstacles for models of brain functions based on brain structure and assumptions of joinivity are exposed. It is shown that cognitive tasks are not localized to particular sets of cortical areas. Unchartered publishs and obstacles in empathetic dendritic processing in one neurons and populations of neurons are converseed. Difficulties of discerning task joind bran activities from unintentional and intrinsic activities are converseed and so is the relation between autonomous and stimulus driven brain activities. The assumption that time is the autonomous variable for brain activities is scrutinized and experimental results incompatible with this hypothesis are currented. Dynamic systems theory and models are blind to spatial includeions, restricting this approach. These obstacles are chaseed by recommendions to loss them. Technicpartner, experimental neuroscience is mostly disputed by uncovering quick processes at the one neuron scale and restrictcessitate by difficulties of including primates. Experimental train disthink abouts difficulties of discovering real reference conditions, disthink abouts the problematic assumptions that experimental animals always are naïve, and trials are statisticpartner autonomous. Similarly, data are scrutinized by bandwidth filters, temporal and spatial averaging removing vital aspects of brain mechanisms. Finpartner, evadeing these many obstacles could produce it easier to reliably expound experimental results.

Wislender the authenticms in Figure 1, one can accomprehendledge obstacles of persist. The obstacles of persist instraightforwardly accomprehendledge frontiers in (systems) neuroscience. In many cases, it is possible to give recommendions that could circumvent an obstacle, push it, or take away it. In this effort, I produce on results provided by many directd colleagues during toilshops aimed to understand how brains and central anxious systems toil (see Acunderstandledgments). This article, however, is my personal pull out.

2. Conceptual obstacles

2.1. Lack of neuroscientific concepts

Anyone studying neuroscience and reading textbooks and neuroscientific literature gets presentd to the concepts that neuroscientists include to expound how central anxious systems are anatomicpartner erected and how neurons toil. Some concepts are rooted in reproducible experimental results from neuroscience itself: synapse, broadcastter liberate, membrane currents, action potentials, ion-channels, excitation, suppression, etc. Some concepts are more slackly included: top-down, bottom-up, dorsal and ventral streams, parallel processing, or recurrent processing with reference to anatomical schemes of joinivity.

Many concepts, however, are borrowed from other scientific disciplines (Figure 2). The concepts shown in Figure 2 are included to expound how the systems in their mother disciplines toil technicpartner and (normally) mathematicpartner. These borrowed concepts are included as analogies in neuroscience. But the borrowed concepts are not tailored to expound (more complicated) bioreasonable systems such as brains. Logicpartner, analogies cannot and do not expound how neurons collaborate to accomplish the whole repertoire of CNS activities. Psychoreasonable concepts have been a wealthy source for presenting brain functions into neuroscience. Psychoreasonable concepts are made to expound and join human behavior to particular social or environmental conditions, but not fitted to expound the mechanisms by which neurons produce this behavior.

Figure 2.

Examples of concepts in neuroscience borrowed from other disciplines. These concepts are analogies expounding how other systems toil. In neuroscience, these concepts are trys to expound how brains toil by expounding how other non-brain systems toil. Analogies cannot expound brain mechanisms becainclude they deficiency ontoreasonable joinion to measurable brain variables. In other words, it is confinclude how the concepts retardy to brain variables. To treatment this, neuroscientists sometimes produce novel definitions of the concept. For example, achieve gets re-depictd as the relative incrmitigate in spike rate for a neuron. In other instances, raw data get changeed to adhere with borrowed concepts. For example, oscillations are exceptional in in vivo meabravements. The irnormal field potentials and EEG write downings then gets filtered to produce band restrictcessitate oscillations (see further under Experimental obstacles and data analysis). In low, the include of borrowed concepts implies unvital troubles and uncertainties in the whole neuroscientific process (Figure 1).

Recently, vibrants and tools from vibrantal systems theory are included to characterize the accumulateive activities of neurons (see tardyr). The analogies shown in Figure 2 are also included as assumptions, as part of scientific hypotheses, and to expound experimental results. If we erase all analogies and metaphors as trys to expound brain mechanisms in neuroscience, will we omit empathetic of brains? Logicpartner, the answer is no. But one may claim that brains have certain properties which could be labeled by psychoreasonable concepts. For example, brains can show attention. In this case, which is not the rule, it is possible to hypothesize and experimenloftyy accomprehendledge physioreasonable mechanisms creating a pre-stimulus activity making it possible to uncover, say cforfeit threshelderly stimuli (see tardyr). When this is experimenloftyy helped, it would be scientificpartner fruitful to refer to this brain mechanism, rather than referring to a psychoreasonable concept with unevident ontoreasonable joinion to brains. This exchangement gives a exact definition that can be experimenloftyy tested. Neuroscience should scrutinize all possible conditions with no conceptual remercilessions (see tardyr). When we abandon the analogies, neuroscientists would be forced to reasonedly establish concepts and hypotheses of brain mechanisms based on experimental results. Lack of concepts expounding accumulateive includeions of neurons at all spatial scales of observation is a authentic obstacle for neuroscience.

2.2. Brain structure and models

Connectomics produce reerections shothriveg the challenging microstructure of cortical nettoils (Figure 3). The dispute is to pull out the functionpartner most relevant joinivity to produce models of CNS activities. An changenative is to simutardy the whole joinome. Currently insect (Drosophila) and mammalian joinomes useable are inwhole joinomes shothriveg synaptic joinions of only a part of the CNS (Scheffer et al., 2020). So, in train, simulations still persist in a local nettoil (for example Markram et al., 2016; Schmidt et al., 2018). Apart from the trouble of produceing the model, the model must also be validated aachievest experimental results, which would be quite an undertaking.

Figure 3.

Interdigitating dendrites. (Left) Two hundred thirteen reerected apical dendrites from layer 2 (61 gray dendrites) and layers 3, 4, and 5 (152 dendrites) from moinclude anterior cingutardy cortex (from Karimi et al., 2020, with peromition). In the volume, ~2,000 dendrites from adjacent neurons and multiple axonal branches from adjacent local and distal neurons will end the picture. (Right) Electron microscopic image, 10 × 12 μm, from grown-up rat CA1 stratum radiatum, with dendrites identified by stars and d (number). MA, myelinated axon (from Harris et al., 2022, with peromition).

So far CNS models have no lasting eigen activity. There are some relatively detailed models of cerebral cortex (Izhikevich and Edelmann, 2008; Kumar et al., 2008; Eliasmith et al., 2012; Markram et al., 2016; Schmidt et al., 2018). These models are commenceed by injecting noise, stimuli, or Poisson spike trains. However, when the afferent stimulation stops, the spiking activity dies out. Mammalian brains, and most anticipateed also insect and zebrafish CNS, have eigen activity as ever-changing ongoing spiking and membrane currents no matter whether they are stimutardyd or not, awake or at sleep (Rudolph et al., 2007; Yap et al., 2017; Stringer et al., 2019; Davis et al., 2020; Marques et al., 2020; McCormick et al., 2020; Siegle et al., 2021; Willumsen et al., 2022).

2.3. Functions and CNS activities

Except in mathematics, the word function supposes activity to satisfy a purpose or achieve a goal. Follothriveg the line of slenderking in the deficiency of concepts section, one ought to be cautious reading purposes or psychology into CNS activities (Buzsaki, 2020). A more iminwhole description is CNS activities. CNS activities can be meabraved straightforwardly as changes of trans-membrane currents (which integrates action potentials), broadcastter liberate and attaching, receptor transport aboutd biochemical changes, synthesis of brain particular proteins and other compounds, activity of transmembrane pumps and carryers. CNS activities can be meabraved instraightforwardly as field potentials, changes in magnetic fields (see technical obstacles). What people and animals experience, slenderk, memorize, and how they behave, as a ambiguous hypothesis, are consequences of CNS activities at many scales. Arriving at a brimming description transcending all scales of observation it the task of neuroscience. This task encounters further obstacles.

2.3.1. Are CNS activities carried out by split loops, circuits, modules, or one huge nettoil?

The ideas that chains of neurons (sometimes systematic in cortical-subcortical loops), micro-circuits, and modular systematic cortical columns are reliable for brain activities have been condemnd. The reasons were undown-to-earth simplifications of the actual synaptic joinivity disthink abouting actual dendritic and axonal anatomy (Figures 3, 4). These ideas also disthink about separatence of joinions to other structures than the members of the loops, micro-circuits, or columns (Alito and Usrey, 2005; Rockland, 2010, 2021; Foster et al., 2021; Shepherd and Yamawaki, 2021).

Figure 4.

Examples of axon anatomy. Ten axons aiming prelimbic area in the moinclude. The prelimbic area is minuscule, findd at the rostral and mesial surface of the frontal lobe (approximate location red in insert). (Top) Oversee of the moinclude brain. (Bottom) Comit see. Each axon aiming the area branches at successive positions to produce an exponentipartner increasing number of axonal branches. An axon can have 1,000 branches (Wu et al., 2014). A one action potential (AP) in the initial part of such an axon then at each branch point give ascend to two APs, one traveling in each branch. With no fall shortures (Alcami and El Hady, 2019) this gives around 500 action potentials traveling in the cdisesteemfilledy 500 terminal branches. Although cut offal branches of one axon aim the prelimbic area, many of its branches also end in cut offal other cortical areas. From the MoincludeLight database, http://mlneuronbrowser.janelia.org. Axons belengthy to the chaseing one neurons in series AA: 0138, 0241, 0344, 0397, 0402, 0802, 0842, 0883, 0897, and 1425. Four axons begin from motor cortex layer 2/3, two from motor cortex layer 5, one from adjacent anterior cingutardy cortex, one from visual association area AM, one from ventral anterior nucleus of thalamus, and one from the intralaminar rhomboid nucleus of thalamus. The finest axonal branches (Figure 3) are not evident with this method.

Studies of cortical neurons operating in vivo show expansively spreading depolarizations, excitations, and spiking. These results depart no help for activity remercilessed to a circumscribable location, to a exceptionalized microcircuit or to columns (see chaseing sections). Rather the spreading mechanisms may retardy to the actual neuron anatomy with interdigitating multiple dendritic and axonal branches (Figures 3, 4). In a CNS perspective, huge populations of neurons spike in many areas of cortex, sectors of basal ganglia, thalamus, other parts of the diencephalon, brain stem nuclei, cedefylum, and spinal cord, even during basicr tasks (Steinmetz et al., 2019; Wagner et al., 2019; Li and Mrsic-Flogel, 2020; Peters et al., 2021; Grün et al., 2022). Moreover, diencephalic and mesencephalic nuclei give meaningfully to choices and particular behaviors, shothriveg that brain activities are results of includeing brain stem, cedefylar, basal ganglia, thalamic, and cortical nettoils (Figure 5).

Figure 5.

Brain stem nuclei join in cognitive tasks. (A)
Y-axis: population uncomardent firing rates in task go trials (orange), task omited trials (blue), and compliant sensory stimulation (gray). X-axis time 0 s stimulus onset/ aim onset that the mice must transport into the cgo in of the field of see. Note the separateent pre-stimulus rates in the midbrain reticular nucleus (MRN) and the zona incerta (ZI) and how these nuclei and the anterior pretectal nucleus (APN) and peri-aqueductal gray matter (PAG) become comprised in the action pickion. (B) Sagittal section of the moinclude brain shothriveg these nuclei (red-brown) in the right brain stem particularpartner comprised in the right motor response (action pickion; changeed from Steinmetz et al., 2019) with peromition. (C) Sagittal section of the human brain shothriveg the right side of the brain stem when normal subjects with their right thumb or right index finger reply to a faint incrmitigate in a visual or somatosensory stimulus, esteemively. The color-coded meaningful incrmitigates in regional cerebral blood flow are findd in the right midbrain reticular nucleus (and in the visual cortex; changeed from Kinomura et al., 1996) with peromition.

Conceptual frontier 3: Rather the vital publish is whether the whole CNS is dynamic, and if not, which (biophysical) mechanisms resolve how far depolarizations and spiking spread in CNS?

2.4. Single neuron activities

2.4.1. Action potentials are for includeion: the bulk of processing in neurons apshow place in the dendrites

As axons only carry out action potentials, the post-synaptic current changeations, processing, and plasticity in a neuron apshows place in its dendrites (and in soma constituting the minusculeer part). Processing of synaptic excitatory post-synaptic potentials (EPSPs) in dendrites is complicated (Figure 6). Roughly, excitatory broadcastters elicit a localized EPSP in the post-synaptic spine, spreading only sparsely into the local dendrite. However, synaptic EPSPs, seal in space and time, may uncover Ca²⁺ channels and NMDA channels in the dendrites to produce Ca²⁺ spikes or Ca²⁺ ptardyau potentials and NMDA spikes or NMDA ptardyau potentials. These spikes and ptardyau potentials can propagate locpartner in one or a scant adjacent dendrites without propagating to the soma and produce action potentials (Larkum et al., 2022; Moore et al., 2022; Stuyt et al., 2022). Depending on the spatial includeions, the ptardyau potentials or huger spikes can also propagate to the soma and elicit an action potential (Otor et al., 2022).

Figure 6.

Dendritic processing. Post-synaptic processing can be an EPSP localized to a one synapse and a minuscule part adjacent dendrite. Na⁺, NMDA, and Ca²⁺ spikes and NMDA, and Ca²⁺ ptardyau potentials with restrictcessitate persist desplit one or a scant dendrites. Multiple spikes and ptardyau potentials with huger spatial persist desplit all apical (shown) or all basal dendrites (not shown) or globpartner excite all dendrites and the soma (Modified from Stuyt et al., 2022) with peromition.

Another scenario is that synaptic EPSPs seal in space and time to distal dendrites may produce Ca²⁺ ptardyau potentials or NMDA spikes in many or all apical dendrites. Alternatively, this can happen in basal dendrites. Neither of these processes may direct to any action potentials, but nevertheless transport about or restore plasticity in the dynamic dendrites (d’Aquin et al., 2022). Similarly, apical or basal dendrites, at least in pyramidal excitatory neurons, may stay globpartner desplitd for up to a scant seconds without this directing to a spike (Larkum et al., 2022; Stuyt et al., 2022). In compriseition to the Ca²⁺ and NMDA spikes, dendrites can also produce minusculeer Na⁺ spikes (spikelets) locpartner in the dendrites without this directing to action potentials (Goetz et al., 2021).

Propagation of dendritic spikes and ptardyau potentials to the soma normally transport about action potentials (Larkum et al., 2022; Moore et al., 2022; Stuyt et al., 2022). The combination of apical-somatic ptardyau potentials and action potentials may elicit a back-propagating action potential to many or all apical or basal dendrites. This is a mechanism that is also anticipateed to transport about or change the plasticity of the dendrites.

The one (pyramidal) neuron can help cut offal processes in parallel with or without spiking. Consequently, an action potential could be the result of many separateent dendritic processes.

With exceptional exceptions (Mel, 1993; Jones and Kording, 2022) dendritic processing is an vital fact that is disthink abouted in models of CNS nettoils (Shepherd and Grillner, 2018).

2.5. Larger scale nettoil activities

2.5.1. Spontaneous and task-joind activity

During an experimental task, e.g., 40% of the neurons in the brain and mesencephalon may incrmitigate their spiking, and up to 20% of neurons decrmitigate their spiking, whereas the remaining 40% of the neurons do not change their ongoing spiking (Steinmetz et al., 2019; Siegle et al., 2021). However, a huge proportion of neurons (up to 40% of all neurons) may not spike at all (Shoham et al., 2006; Barth and Poulet, 2012; Wohrer et al., 2013). These non-spiking neurons could also join in the task, for example by depolarizing or hyperpolarizing their dendrites (Roland et al., 2006, 2017; Mohajerani et al., 2010; Esteves et al., 2021; Liang et al., 2021). In the future, it might be possible to approximate the proportion of neurons participating in a task in mammals by changing their transmembrane currents (see technical obstacles). For spiking, the above results might be illustrative. Thus, there are task joind activities, but most studies inestablish many spiking neurons seemingly unjoind to tasks (Urai et al., 2022). In the literature this is normally called unintentional activity.

The normal separateention is between task joind activity and “unintentional ongoing activity,’ i.e., CNS activities that may co-exist, but are unjoind to task and task behavior. This separateention must be made for any of the activity variables meabraved (spiking, synaptic, postsynaptic activity variables as depictd in section 3). In train the separateention is normally set by sorting the neurons in two groups. One group for which changes in meabraved variables corretardy with parameters of the task. The other group for which this is not so. This strategy may disthink about neurons which are vital for solving the tasks but unjoind to the stimulation and behavioral parameters (see tardyr). The unintentional activity may be seemingly random fluctuations of the meabraved variables in space and time. For example, the continuous local spatial and temporal irnormal changes from sweightless excitation to sweightless suppression prior to the stimulation as in Supplementary Video 1. This CNS activity is effortless to discern from task CNS activity. However, during the experiment there may be neurons helping intrinsic (cognitive) CNS activities un-joind to the task (Figure 7). Separating task joind activity from such “unintentional” or more exactly self-systematic intrinsic cognitive activity is difficult and may only be possible under assumptions. For example, two tasks depending on activities engaging the same part of the CNS nettoil intrude and cannot be carry outed simultaneously (Herath et al., 2001) (Figure 7).

Figure 7.

Cartoon illustrating separateent sees on brain activities. SPONTANEOUS activities are autonomous of outer signals and TASK activities. Spontaneous brain activity can be (blue) fluctuating irnormal “background” activity spatipartner autonomous at scales < 1 mm³ when the brain is awake, but idle and not producing any motor activity. In other parts of the brain, INTRINSIC cognitive activities (green) not directing to any behavior engaging the nettoil in cut offal parts from the microscopic to macroscopic scales may co-exist with the TASK activity (red). AUTONOMOUS. The brain could be autonomous with self-systematic intrinsic activities engaging the nettoil at all scales that outer stimuli and insists cannot change, but only sweightlessly change. The autonomous brain self-structures motor behavior (symbolicpartner pictured as a muscle). DRIVEN. Task joind activity and outer sensory stimuli and inner stimuli from the body drive brains away from unintentional activity into sensory and cognitive activities at all scales, which eventupartner result in some motor behavior.

This may need examination of the whole CNS (Figure 5). Larger scale CNS activities may also be classified according to their caincludes. The asks elevated in this section are all joind to how brains and a central anxious systems self-structure their activities.

2.5.2. Are brains driven or autonomous?

Until recently, neuroscience has been mainly oriented to expound how changes in the surrounds and behavioral conditions change transmembrane currents (including action potentials) and synaptic efficacy in brain neurons. Recently, there is accumulating evidence contesting this see that spiking and post-synaptic vibrants in brains are predominantly externpartner driven (Figure 7) (Millner, 1999; Fried et al., 2011; Buzsaki, 2019; Steinmetz et al., 2019; Cowley et al., 2020; Marques et al., 2020; Clancy and Mrsic-Flogel, 2021; Grün et al., 2022). The changenative is self-systematic intrinsic activities. Intrinsic activity is autonomous of outer stimuli, inner stimuli, insists and tasks, which also discern it from CNS activities joind to bodily inner functions such as thirst, hunger, and intimacyual desire.

Brains are not in straightforward reach out with the surroundings. Strictly, all spikes produced in a central anxious system are intrinsicpartner produced. Brains can self-structure their everchanging intrinsic activity to produce catalogless waves, spindles, keen wave ripples, quicker irnormal membrane fluctuations, dreams, and, in awake conditions, thoughts, structures, strategies, clear behavior, and (some brains) language (Figure 7). Even in primary visual and auditory cortical areas, only 5%−15% of the spikes carry inestablishation about the surround (Richmond and Optican, 1990; Heller et al., 1995; Olshaincluden and Field, 2006; Keyser et al., 2010; Urai et al., 2022). Similarly, the correlation of spike trains with outer visual stimuli is low, typicpartner around 0.1 in the primary visual cortex (Eriksson et al., 2010). These results are well understandn and show that 85%−95% of the spikes in a brain are autonomous. A recent huge-scale study showed that outer stimuli and various experimental conditions could change fluctuations in the (multiunwiseensional) human cortical field potential, but not perturb the underlying vibrants generating the fluctuations (Willumsen et al., 2022).

Conceptual frontier 6: portray and sort CNS activities by how they comprise the CNS nettoil by changing CNS activities (depictd in section 3). (Referring to sensory input, behavior, and psychoreasonable concepts may have restrictcessitate exstructureatory power).

On the other hand, in awake conditions, cgo ined attention and exclusion or suppression of own (intrinsic) activities can entrain field potentials partly or globpartner over the cerebral cortex. For example, in humans and other primates exposed to rhythmic visual or auditory stimuli, each stimulus produces a one time-locked oscillation. These time-locked oscillations can spread, with separateent lags, to cover the whole cortex (Besle et al., 2011; Gomez-Ramirez et al., 2011; Spaak et al., 2014; Merchant and Averbeck, 2017; Willumsen et al., 2022). Also, unforeseeed stimuli may elicit spreading excitation and spiking globpartner over cortical areas (Ferezou et al., 2007; Salkoff et al., 2020). Thus, under such circumstances, cortical nettoils are hugely externpartner driven.

Most anticipateed, brains have a certain degree of autonomy. In compriseition, brains regutardy their sensitivity to outer sensory impact. Autonomy may be allotd over separateent CNS structures and be separateentipartner regutardyd in each structure. Even respiratory inspiration can be voluntarily modutardyd. Similarly, in subjects structurening a motor effort, the motor system can incrmitigate the heart rate and blood presbrave in persist of the motor action (Pfurtscheller et al., 2013).

2.5.3. How does intrinsic activity in brains materialize?

Drosophila and zebrafish larvae own neurons (P1 neurons and dorsal raphe neurons, esteemively) which by incrmitigated spiking mobilize cut offal structures to produce complicated behavior lasting minutes. The number of neurons triggering these behaviors is less than 100 (Jung et al., 2020; Marques et al., 2020). Details of how the trigger neurons recruit a huge part of the CNS are still deficiencying. The changes in spiking and recruitment of many populations of neurons are examples of an intrinsicpartner systematic activity spreading to huge parts of a CNS.

From mammals, there are examples of how the spiking of one or very scant neurons can change the behavior and carry outance of an animal (Romo et al., 1998; Houweling and Brecht, 2008). However, in these examples, the animals were comprised in a task; therefore, they do not qualify as intrinsic activity (see also the text tardyr). But the fundamental asks are still pending. For example, how many neurons are needd to produce intrinsic vibrants? How many neurons are needd to produce intrinsic vibrants directing to clear behavior? Dreaming is yet another example of intrinsic brain activity. How dreams commence is confinclude, i.e., how changes in spiking and transmembrane currents structure to produce dreams.

Conceptual frontier 9: Reveal how changes in vital variables (membrane potentials, transmembrane currents, and spiking) persist to encompass huger populations of neurons in multiple structures of the CNS.

2.6. Is time an autonomous variable for CNS operations?

An autonomous variable is a variable that does not depend on other variables. Time is conceiveed by humans. Time is writed of identical units that comprise licforfeitly. Time is an autonomous variable in Newtonian physics, but in the theory of relativity and quantum mechanics, time is not an autonomous variable (Rovelli, 2018). Time in neuroscience is usupartner think abouted as an autonomous variable for fundamental brain processes. As outer watchrs, scientists can timestamp every spike. Similarly, one can produce mathematical functions of other meabraved fundamental (reliant) variables, potentials, transmembrane currents, broadcastter liberates, and plasticity variables using time as the exclusive autonomous variable. From a scientific point of see, the ask is whether time is the only autonomous variable for operations in neurons and for CNS processes.

2.6.1. Experimental results incompatible with time as autonomous variable in brain activities

Spike trains have traditionpartner been scrutinized with time as the autonomous variable. This could be a catalog of the times spikes are rerentted from neurons according to an outer (computer) clock or changeing the spike train to a continuous rate function of time. However, claiming that all activities in brains all persist according to outer clock time only (i.e., with time as the autonomous variable) is a sturdy hypothesis that can be shown wrong. Regarding spike trains as temporal codes carrying inestablishation to be decoded by the brain is assuming that this type of brain activity depends on time as the autonomous variable (Figure 2). Decades have been spent to discover temporal patterns carrying the code (Barlow, 1961; Bialeck et al., 1997; Rao and Ballard, 1999; Dayan and Abbott, 2001; Bassett et al., 2020). Also simultaneously write downed neurons have been scrutinized for synchrony (Gray and Singer, 1989; Abeles, 1991; Singer et al., 2019).

Working in the premotor and motor cortex of the monkey, Sonja Grün and associates, using rigorous statistics, watchd that the same set of neurons in every one trial fired in the same spatial order while the monkey accomplished out and understanded an object (Grün, 2021; Grün et al., 2022). Subsets of 2–6 neurons elicited from 2 to 6 spikes always in the same spatial order (Figure 8A). These spatial sequences were particular to the components of the accomplishing task, i.e., joind to the cue, procrastinate, preparation, accomplishing, and understanding (Grün, 2021; Grün et al., 2022). These results show spatial vibrants at the microscopic and one neuron scale. These results cannot be expounded as a brain activity using clock time as the autonomous variable. In contrast, they show that the timing and order of the spikes depend on the spatial positions of the collaborating one neurons.

Figure 8.

Spatial vibrants of spiking. (A) Small groups of individual neurons spike in the same spatial order in one trials from the macaque pre-motor and motor cortex (in contrast to synchrony and temporal patterns, Grün et al., 2022). (B) Excitatory sweeps elicited by spiking exciting the dendrites post-synapticpartner in a spatial order. Left: Excitatory sweep, 122 ms after the materializeance of an object moving in the field of see, in areas 19/21 and feedback to areas 17/18. Right: Significant spiking in areas 17/18, mostly in layers 3 and 5, shown by the white spots and excitatory sweep here at 148 ms, ahead of the retinotopical mapping of the moving object (arrow to luminous red). The spiking estimating where the object will be mapped in the future (right arrow) and hence where its position in the field of see will be. See the brimming spatial vibrants in Supplementary Video 2 (isoflurane anesthetized ferret, Harvey et al., 2009).

Another example violating time as the autonomous variable in brain processing is when the retinotopic mapping of a moving object co-exists with the mapping of the foreseeion of its future outer position in the visual areas (Figure 8B and Supplementary Video 2).

If an outer object relocates in the field of see, it is mapped, with separateent procrastinates, in each retinotopicpartner systematic visual area (Supplementary Video 3). So, initipartner, multiple versions, splitd in space and time in the brain, exists of one and the same object. However, higher visual areas convey excitatory feed-back sweeps to lessen visual areas aligning the excitation phase between the areas. This aborts their initial separation in brain time and produce unified motion of the object in retinotopical visual areas. This is anticipateed to give the experience to see only one object moving in the field of see (Figure 9).

Figure 9.

Moving visual object and phase alignment. Object moving downwards from time 0 ms. Phase plot of depolarisation in areas 17, 18, 19, and 21 from six ferrets aligned by their cytoarchitectural borders. Note the directing depolarization in areas 19 and 21 at 119 ms (left). Feedback 137 ms and phase alignment aborting the procrastinates between areas 160.8 ms (right) (Harvey et al., 2009).

Brains do not always process stationary objects that are split in time and space as stationary in time and space (Figure 10A). When first a stationary object materializes at one position in the field of see, this is mapped in its retinotopical position in visual areas as expounded above. If the first object then fades and a second stationary object is flashed at another position in the field of see, the second object is mapped (rightly) in its separateent retinotopical position in the first visual area (Figure 10B). However, the mapping of the second object in higher visual areas elicits spatial-temporal excitatory vibrants evening the mapping of the previous object with the current object in brain space (Figure 10B). After this fusion to one object, its vibrants in space and time in the brain is identical to that of a moving object. This elicits the illusion that the first object relocated to the novel position (Figure 10). Thus, outer objects stationary and split in space and time by brain processing become combined to one moving object (apparent motion).

Figure 10.

Cortical operations at the mesoscopic scale incompatible with time as an autonomous variable (apparent motion). Spatial vibrants underlying apparent motion illusion. (A) At time 0 ms, the lessen object materializes. Spiking (not shown) and (B) excitation incrmitigates map the lessen object retinotopicpartner at area 17/18 border at 32 ms. At 83 ms, the upper object materializes, and the lessen object fades. The upper object gets mapped at 115 ms retinotopicpartner at a separateent spatial location alengthy the 17/18 border. At 117 ms, the spiking transport abouts a straightforwarded excitation alengthy the 19/21 border (appreciate that for moving objects in Figure 8B) and a feedback excitation to the 17/18 border in between the mapping of the now-gone lessen object and the novel upper object. (C) At 118 ms, this elicits a straightforwardional excitation d[ΔV(t)]/dt and spiking r(t) at the 17/18 border persisting 120 ms to 160 ms in between the establisher object mapping site and the novel (top right). (B) The feedbacks then quench the procrastinates between areas, and the cortical excitation persists in phase from 146 ms over the 4 areas. The processing in the cortex finecessitate space and time and changeed two outer spatial and temporal separateent objects to one moving object (A) (top; modified from Ahmed et al., 2008, licensed under CC BY-NC 2.0).

In vision, there is a procrastinate between the materializeance of an object until the spiking incrmitigates in the first visual area: the retino-cortical procrastinate (Supplementary Video 1). Figure 11 shows how excitatory, suppressory, and spiking mechanisms in space and time in the brain can quench the perceptual procrastinate by maximizing spiking in the cortex when two oppositely moving objects occlude one-another in the field of see. In the examples shown in Figures 8–11, the ferrets were anesthetized (isoflurane) shothriveg that these brain vibrants were automatic.

Figure 11.

Cortical spiking, excitation, and suppression at the mesoscopic scale incompatible with time as an autonomous variable. Excitation, suppression, and spiking in ferrets exposed to two bars moving to occlude one another in the cgo in of the field of see at 412 ms. Dots show meaningful spiking and white dots maximal spiking rates, otherdirectd conventions as in Figure 10. Notice the foreseeive excitations of the future retinotopic mappings of the objects in areas 17/18 and 19/21 at 82 ms, the maximal spiking at the cortex recurrenting the cgo in of the field of see at 413 ms in an suppressory regimen of cortical layers 1–3 (data from Harvey and Roland, 2013).

These examples show that all brain activities cannot be expounded as evolving with clock time as the autonomous variable. The examples also show that spiking at the microscopic scale and postsynaptic depolarizations, excitations and suppressions at the mesoscopic scale persist with time and space as mutupartner reliant. The idea of time as an autonomous variable for brain processes has been condemnd from separateent theoretical points of sees (Buzsáki and Tingley, 2018; Gao, 2020; Le Bihan, 2020). For example, expounding both the uncomardenting of brain responses as meabraved aachievest the clock in the computer and the uncomardenting of the clock units-might be a fundamental conset up in current experimental approach (Buzsáki and Tingley, 2018). Unvital assumptions conceptupartner remerciless neuroscience from grothriveg further.

2.6.2. Stationarity

It is normally supposed, or claimed, that brain variables end up in some establish of stationarity. If this happens, the variable has the same probability distribution over time, i.e., uncomardent, variance, and autocorrelation are invariant over time. If time is not an autonomous variable for brain processes, the stationarity concept omits its presentance in neuroscience. Although stationarities are accessible and streamline mathematics and statistics, are they vital for empathetic brain activities? One may ask then, if the concept of stationarity as depictd is invalid for brains, how do brains resolve whether outer objects are stationary? For vision, Supplementary Video 4 might give a clue. Some 90 ms after the materializeance of a stationary object, the spiking, despite continuously changing rates, is restrictd to the retinotopic map of the object in the primary and secondary visual areas (see also Lamme, 1995). This cannot be expounded by statistical and vibrantal systems definitions (e.g., mended point) of stationarity. This is another charitable of stationarity, an example of a brain spatial stationarity.

2.6.3. Dynamical systems theory expounding brain activities

A vibrantal system is writed of a state space and rules describing the evolution of the system over time in this state space. Treating central anxious systems as complicated vibrantal systems as complicated vibrantal systems has had some success expounding accumulateive operations of neurons. In vivo studies of separateent spiking nettoils in the cerebral cortex but also spinal, hypothalamic, and thalamo-cortical nettoils show the accumulateive spiking vibrants of the nettoil neurons persist as trajectories alengthy low-unwiseensional, firm manifelderlys in state space (Churchland et al., 2010; Gallego et al., 2017; Lindén et al., 2022). On the post-synaptic side, field potential, MEG, and EEG studies show state space vibrants appreciate that of strange (turbulent) higher unwiseensional enticeors (Babloyantz and Destexhe, 1986; Stam, 1996; Baria et al., 2017; Willumsen et al., 2022). This vibrant may be identical for all local nettoils in the human cerebral cortex. However, since the trajectories enhuge and lessen, the vibrant is incompatible with the mathematical definition of enticeors (Strogatz, 2018; Willumsen et al., 2022).

Importantly, to be a truly higher unwiseensional (turbulent) vibrantal complicated system, the CNS must show sensitivity to initial conditions (Strogatz, 2018). This uncomardents that one must resolve the initial conditions for a CNS. This needs that for “one point in time,” say wislender a fraction of a ms, we must understand how many variables there are at each point of each neuron (say a point is a membrane surface of 0.1 μm²) and which order they have (e.g., higher derivatives of the variables as a result of spatial includeion; Figure 6). We must understand exactly where and in which axon or axonal branches action potentials are and understand the carry oution velocities of each branch (Figure 4). Moreover, as we cannot be brave whether a neuron only has unintentional ongoing unsystematic activity or joins in intrinsic or task-joind systematic activity, we must understand the cherishs of all these variables for all neurons of the CNS wislender this ms. To depict an initial condition in a CNS having ever-ongoing changes of its variables at all spatial scales seems impossible.

Dynamical systems analysis gives the temporal evolution of the accumulateed neurons or local nettoil and disthink abouts spatial includeions. However, one can uphold the locations of the neurons in the data and instead watch the spatial evolution as trajectories in state space (disthink abouting the temporal evolution) (Roland et al., 2017). Both these approaches thus have restrictations. As shown here, vibrantal systems theory might not always fit brain activities. The examples in section 2.6.1 show that one can straightforwardly watch and meabrave spatial temporal includeions in the cerebral cortex, instead of analysing temporal and spatial trajectories in abstract state space.

2.7. Spatial vibrants, a ambiguous hypothesis

The fundamental mechanism of includeion in CNS of most species is spatiotemporal: each neuron sends action potentials thcdisesteemful all axon branches to its two–three orders of magnitude more countless aim neurons (Figure 4). This fundamental mechanism produces spatial vibrants in the nettoil of neurons. Postsynapticpartner, the spatial persist of currents in the dendrites resolve plasticity and spike production (Figure 6, section 4.1). Spatial vibrants is a ambiguous hypothesis that can be tested experimenloftyy. The hypothesis states that changes in activity variables (section 3) propagate thcdisesteemful the nettoil of neurons that produces up a central anxious system. These propagations uncover spatial and temporal includeions underlying CNS activities at separateent scales (Roland, 2017; Grün et al., 2022). The forces driving the spatio-temporal includeions thus are transmembrane currents, receptor driven, and biochemical. The word vibrants refer to these biophysical and biochemical forces driving the includeions. Thus, spatial vibrants is not joind to vibrantal systems theories and do not carry any further assumptions about brain activity variables and their includeions.

2.7.1. Spatial vibrants at separateent scales of observation

Spatial vibrants is not a novel idea. Tasaki et al. (1968) included a voltage comardent dye to chase the course of an action potential. Spatial vibrants has been cataloglessly persisting since then but increaseed by recent techniques assistting simultaneous meabravements of CNS activity variables in huge parts or a whole CNS (see technical obstacles). Figures 8–11 and Supplementary Videos 1–4 are concrete examples of spatial vibrants of spiking and postsynaptic changes in excitation and suppression directing to visual object perception and the apparent motion illusion. Spatial vibrants of spiking and postsynaptic activities run in one neurons (Figures 6, 8A) minuscule groups of neurons (Figures 8B, 11), and huger populations of neurons (Figures 8B–12). Spatial derivatives are necessitateed to discern separateent establishs of postsynaptic processing at the nettoil scale (Supplementary Videos 1, 5). Spatial vibrants of the activity variables persist though the low-unwiseensional geometry of a CNS and are therefore wellsuited to uncover mechanisms of neuron includeions at the population (mesoscopic) scale. Its dispute is to discover principles to establish theories of includeions between multiple neurons.

Figure 12.

Trained mice suppress and excite relevant cortical areas prior to stimulation and motor response. (A) At the time showd by the vertical green line, a feeble whisker stimulus is given. Intrackllular Ca²⁺ and spiking rate, r(t), decrmitigated in pyramidal neurons in motor and visual areas, but incrmitigated in anterior cingutardy and pre-motor cortex. However, the moinclude must painclude 1,000 ms until a beep inestablishs that it can achieve its reward by licking (redrawn from Esmaeili et al., 2021, licensed under CC-BY 4.0). (B) Mice continuously watch a moving grating for a upholded change in speed and reply by licking their reward. At periods when such a change was doubtful, this elicited temperate intrackllular Ca²⁺ incrmitigates in premotor and motor areas in contrast to when the change was foreseeed. Note the intrackllular Ca²⁺ decrmitigate in pyramidal neurons’ primary visual cortex and incrmitigate in visual association areas in persist of the stimulus change (redrawn from Orsolic et al., 2021, licensed under CC-BY 4.0).

2.7.2. Cortical spatial vibrants

Spatial vibrants in the cerebral cortex retardy straightforwardly to uncoverion, foreseeion, perception, illusions, retrieval, and validateation of memories in rodents, carnivores, and primates (Grün et al., 2022) (Figures 8–12). Here it is not the purpose to scrutinize spatial vibrants, only to give some concrete examples.

Postsynaptic excitations propagating over dendritic fields may have many shapes and speeds (Supplementary Videos 1–5) (Xu et al., 2007; Mohajerani et al., 2010; Denker et al., 2018; Dickey et al., 2021). Broad postsynaptic net-excitations chaseed by local net-suppressions give the astonishion of a wave propagation though the cortical nettoil. The separateent establishs of (mesoscopic) postsynaptic changes have separateent roles in brain activities. For example, frequency-modutardyd sounds elicit a depolarization sweep over the relevant tonalities in the first and secondary auditory areas (Horikawa et al., 1998; Farley and Norena, 2013; Horikawa and Ojima, 2017). Retinal excitatory sweeps transport aboutd by a saccade elicit a cortical sweep in V1 suiting the straightforwardion of motion over the retinal pboilingoreceptors (Slovin et al., 2002).

Waves in separateent straightforwardions materialize in mesoscopic write downings of current changes in upper layers of cortex with quick voltage indicators (Prechtl et al., 1997; Senseman, 1999; Roland et al., 2006, 2017; Xu et al., 2007; Mohajerani et al., 2010; Denker et al., 2018; Davis et al., 2020) (Figures 9–11), or in geneticpartner labeled pyramidal excitatory neurons, or as changes in glutamate liberate (Berger et al., 2007; Song et al., 2018; Ahorribleshi et al., 2020; Zhu et al., 2021). The examples in Figures 8B–11 and Supplementary Videos 1–5 were write downings from isoflurane anesthetized ferrets receiving a visual stimulus. Although the visual stimulus initipartner drives the cortical neurons after some 28 ms, the cortex does not produce a spatial pattern of the stimulus in each visual area. Rather autonomous cortical spiking and postsynaptic spatial vibrants apshow over producing tardyral spreading excitation, feedback waves and local suppressions. This vibrants after some 90–120 ms encounter to a spatio-temporal “expoundation of the visual surround” in the visual areas. Similarly, the moving visual stimulus initipartner anticipateed drives the retinotopical depolarization, but autonomus spatial vibrants apshow over and produce foreseeive depolarizations and spiking and further spatial vibrants (Supplementary Videos 2, 3).

2.7.3. Lachieveing reliant spatial vibrants in awake animals

In animals trained to carry out a task, intrackllular Ca²⁺ can stay incrmitigated for lengthyer periods, while in other areas intrackllular Ca²⁺ stays decrmitigated for lengthyer periods. These changes are lachieveing and task reliant (Gilad and Helmchen, 2020; Salkoff et al., 2020; Clancy and Mrsic-Flogel, 2021; Liang et al., 2021) (Figure 12). The chooseical signals inestablishing these changes stem mainly from the upper, supragranular, layers of cortex. However, there are cut offal examples of discrepancies between spiking and mesoscopic post-synaptic activity, even in supragranular layers. This could be spiking under suppressory regimes (Orsolic et al., 2021) (see also Figure 11), or no spiking under excitatory post-synaptic regimes, pre-excitation (Roland, 2010) (Figure 12). These discrepancies are in accordance with the earlier alludeed observations that dendrites may be well desplitd without giving ascend to action potentials or apical dendrites suppressed while neurons are spiking (section 3.1).

Generpartner, spatial vibrants are causal. In naïve animals feeble or temperate stimuli may not give ascend to a local excitation and spiking in primary sensory areas. If it does, the excitation and spiking do not persist to other areas and structures. This contrasts with well-trained animals. In trained animals, fall shorture of a trial particular spatial vibrants to persist from the primary sensory area to other areas and subcortical structures directs to fall shorture to reply (Gilad and Helmchen, 2020; Salkoff et al., 2020; Esmaeili et al., 2021; Orsolic et al., 2021). Thus, spatial vibrants is anticipateed to propagate such that changes in the activity variables propagate from microscopic scales to comprise huger parts of a CNS. However, this does not omit more remercilessed local establishs of spatial vibrants. Details of how spatial includeions persist in and between subcortical structures are not understandn (Figure 5).

3. Technical obstacles

The deficiency of techniques to chase the course of action potentials thcdisesteemful a CNS is normally claimed the reason for the deficiency of persist in systems neuroscience (Bargmann et al., 2014). Given the premise that many parts of a CNS, the brain stem, thalamus, basal ganglia, cedefylum, and the brain itself do seem to join even in basicr tasks, global access to a CNS seems a must. The axonal diameters of primate cortico-cortical axons range from 0.2 to 4 μm (Liewald et al., 2014). This gives carry oution velocities up to 35 mm ms⁻¹ (Waxman and Bennett, 1972). In compriseition, the relevant sampling space in humans range from synapses 0.5 μm³ to a human brain hemisphere 700 cm³, i.e., 14 orders of magnitude. In comparison, Zebrafish larvae with their translucent CNS and 100,000 neurons with cataloglesser axonal carry oution of action potentials seem an chooseimal species for studying spatial CNS vibrants.

The physioreasonablely relevant techniques are electro- physioreasonable, magnetic, and chooseical. Applications of these techniques in multiple write downings simultaneously from CNS are well portrayd in recent scrutinizes (Engel and Steinmetz, 2019; Cardin et al., 2020; Moreaux et al., 2020; Machado et al., 2022; Urai et al., 2022). So here the cgo in is on restrictations that cannot be solved by combinations of electrophysioreasonable and chooseical techniques.

Modern multi-electrodes can in principle access all parts of the CNS, produceing spiking from 20,000 to 100,000 neurons simultaneously in animals, and humans with sampling frequencies >20 kHz (Jun et al., 2017; Steinmetz et al., 2019; Paulk et al., 2021). Spike write downings do not uncover the type of neurons comprised (excitatory glutamatergic, suppressory GABAergic, and glycine-ergic sub-types). Moreover, extrackllular spike write downings are blind to the dendritic contributions.

Optical write downings can seize dendritic contributions in relevant space-time scales, with voltage-comardent dyes or geneticpartner encoded voltage sensors (GEVI) with sampling rates op to 2 kHz (Roland et al., 2017; Song et al., 2018; Villette et al., 2019; Moreaux et al., 2020). Intrackllular Ca²⁺ changes in one dendrites and one synapses can be uncovered with recent GCaMP inestablishers, which are able to seize changes currently at 20 ms scale (50 Hz). This seizes catalogless spatial vibrants, but not the quick (Ferezou et al., 2007; Muller et al., 2016; Grün et al., 2022) (Figures 8–12, Supplementary Videos 1–5). The local interdigitation of dendrites from thousands of neurons (Figure 3) implies that post-synaptic changeation by individual neurons cannot be resolved with one-pboilingon, two-pboilingon, or three-pboilingon chooseical write downings, becainclude it is difficult to suit the dynamic dendritic branches with the right neuron. Labeling all dendritic and axonal terminal branches with voltage sensors gives an overcrowded picture in which this problem apshows immense unwiseensions. In compriseition, it is a dispute to track action potentials in slender axonal branches and their origin from neurons in other areas (Figures 3, 4).

Geneticpartner encoded voltage sensors particularpartner conveyed in only one-subclass of neurons produce this problem easier to tackle (Abdeadorerweighttah et al., 2019; Piatkevich et al., 2019; Villette et al., 2019). In these neurons, one can chase the depolarizations, hyperpolarizations, and persist of action potentials in one trials in vivo with 1 kHz sampling rates. It is possible to imstructuret fiber chooseics and even chooseical probes providing excitation weightless and uncoverion of fluorescence alengthy multiple sites on the same probe. However, write downings of dendritic excitation and suppression vibrants are remercilessed to the slender space alengthy the imstructureted chooseic probe (Moreaux et al., 2020).

At high resolution, it is possible to pickively spendigate subclasses of excitatory and suppressory neurons. However currently, no coherent write downings of a whole insect or mammalian CNS is possible at any spatial scale (Piatkevich et al., 2019; Villette et al., 2019; Cardin et al., 2020; Moreaux et al., 2020; Machado et al., 2022; Urai et al., 2022). Moreover, the genetic incorporation of inestablishers of membrane current changes, and contributions from neuron subclasses is restrictcessitate to a scant species.

It is difficult to envisage a noninvasive technique for primates with physioreasonablely relevant sampling frequency. Perhaps, novel MEG-techniques with quantum field sensors and betterd depth resolution may grow into tomodetailed MEG for primate brains (Bezsudnova et al., 2022).

4. Experimental obstacles

Ordinarily, experiments are carry outed on a CNS to test a hypothesis. The hypothesis is the foreseeion of the outcome of the experiment. Sometimes, the hypothesis can be quite ambiguous. In most experiments, the experimgo in resolves and maniputardys the autonomous variables. For example, deal withling the surround to reduce conset uping factors and accomprehendledgeing the behavioral conditions (see conceptual frontier 5; Figure 13).

Figure 13.

Experimental train in neuroscience. Dependent variables, for example Membrane potential, Vm(t), trans-membrane currents dVm(t)/dt, spike rate r(t), or action potentials, AP, and their spatial vibrants. The experimental trial can commence with a cue or a stimulus. During the trial, the experimgo in meabraves reliant variables, for example spike trains and membrane currents or membrane potential changes. The write downed reliant variables are then contrastd to write downings of the same reliant variables during a baseline or deal with condition.

4.1. Baseline and deal with conditions

Animals must be trained to carry out tasks. In the example in Figure 12A, turn asideion of the whisker at an timely stage of training will give no change in intrackllular Ca²⁺ in the cortex. After many training trials, intrackllular Ca²⁺ and spiking will incrmitigate in the primary sensory (barrel) cortex and spread to the secondary sensory cortex and from there to the premotor and motor cortex (Esmaeili et al., 2021; Gallero-Salas et al., 2021). Thus, the prerequisite for the task-transport aboutd spatial vibrants is accomplished lachieveing.

When mice have lachieveed a task, spiking incrmitigates prior to the experimental trial in CA3 of the hippocampus, dentate gyrus, basal ganglia, zona incerta, substantia nigra, midbrain reticular establishation and anticipatory Ca²⁺ incrmitigates may materialize in particular cortical areas (Steinmetz et al., 2019; Salkoff et al., 2020; Orsolic et al., 2021) (Figure 12). Humans are usupartner verbpartner directed to carry out experimental tasks. If they understand the direction, the regional cerebral blood flow incrmitigates in cortical areas comprised in the processing of the sensory stimuli, prior to the experimental trial (Figure 14).

Figure 14.

Pre-trial CNS activity. (A) Regional cerebral blood flow incrmitigates in percent in prefrontal, primary, and parietal somatosensory areas prior to a one trial in which the subject foresees a threshelderly stimulus on the tip of the right index finger contrastd to physioreasonable depictd rest condition (see text) (Roland, 1981). (B) Changes in spiking rates prior to experimental trials. Spiking prior to trials (indicating task comprisement) of neurons in visual, somatosensory, primary motor, retrosplenial, ACA cortex, and posterior thalamus (LP, PU) corretardys pessimisticly with the comprisement, but the spiking in nucleus accumbens, globus pallidus ext., CA3 of the hippocampus, dentate gyrus, parafasicular nucleus of thalamus, midbrain reticular establishation, and substantia nigra corretardys chooseimisticly with task comprisement, if “compliant” visual stimulation is apshown as baseline condition (from Steinmetz et al., 2019).

Awake-trained animals and humans are not naïve. In contrast, they are particularpartner comprised in carry outing the task prior to the experimental trial. Prior to the experimental trial, spatial vibrants persists in the brain stem, hippocampus, basal ganglia, and cortex. This experimental-joind preparatory spatial vibrant probably fine tune the excitability in structures and cortical areas relevant for executing the task (Roland, 1981; Steinmetz et al., 2019; Gilad and Helmchen, 2020; Salkoff et al., 2020; Esmaeili et al., 2021; Orsolic et al., 2021) (Figures 12, 14). These preparatory spatial vibrants may expound how micro-stimulation of singe neurons can deal with the choice of an animal (Romo et al., 1998; Houweling and Brecht, 2008). Changes in brain variables in most cases are meabraved relative to a unfair pre-stimulus or pre-trial meabravements in which the CNS structures to spendigate are already dynamic or particularpartner suppressed.

Historicpartner, the field of human brain imaging tried to set up a normally consentd reference, a depictd rest condition. This is a behavioral reference, during which there are no changes in sensory input and no voluntary motor activity, and with physioreasonablely depictd reference cherishs of blood presbrave, heart rate, galvanic skin response, and EEG pattern (Roland and Larsen, 1976). But the rest condition is also a consequence of an direction. The assumption that this “rest state” is stationary and priceless as a reference for trials done promptly before or after the rest meabravement is most anticipateed counterfeit. So, if there are no outer or inner stationary references, how should we meabrave changes in spiking and currents and magnetic signals from brains? Also, how should we expound the meabraved changes?

A pragmatic solution is that one could meabrave where and when changes in membrane currents, magnetic fields, and spiking occur without any inner or prior brain reference. This could also be done during the training of the animals and while humans get the task directions.

Theoreticpartner, at least, one could get a cdisesteemful classification of brain activities to commence with. Secondly one could retardy these data to other changes in brain variables in space and time.

4.2. Experimental structure, one trials

Single-trial structure and analysis is compulsory becainclude brains structure behavior with separateences in one trials. The spiking vibrants mirrors a one-trial variability (Riehle et al., 2018; Steinmetz et al., 2019; Cowley et al., 2020; Salkoff et al., 2020; Williams and Linderman, 2021). Spatial spiking vibrants is a one-trial vibrants (Grün et al., 2022).

Averaging atraverse neurons, one trials, one areas, or other CNS structures hides the underlying spatial vibrants (Riehle et al., 2018; Davis et al., 2020; Grün et al., 2022). The concepts behind this praxis, behind the experimental structure, and behind the expoundation of results are affectd by the separation of time and space. For example, this helderlys for concepts such as recurrentation, spike pattern, temporal codes, maps, place cell, and synchrony.

The assumptions underlying temporal and spatial averaging, multi-trial statistics, and statistical independence of trials are most anticipateed wrong. So, neuroscientists are forced to structure one-trial experiments and scrutinize one trials statisticpartner (Lee et al., 2010; Rey et al., 2015; Williams and Linderman, 2021).

Current one-trial statistics produce include of a vibrantal systems approach. The key to watch separateences between one trials is to write down simultaneously from many positions/neurons. Often, spike data, membrane, and field potentials are of lessen unwiseensionalities than the number of neurons/positions write downed. So, first one necessitates to approximate the real unwiseensionality of the data at hand.

Dimensionality is the number of unwiseensions one necessitates to get an exhaustive description of the vibrants of variables in state space. There are cut offal methods by which one can discover the unwiseensionality of time series data. The best method is the Grassberger and Procaccia (1983) method (Camastra, 2003). The end-product is a trajectory of the one-trial behavior in a multiunwiseensional state space of the set up real unwiseensions. Trials with separateent vibrants persist in partly separateent parts of this multiunwiseensional state space (Churchland et al., 2010).

The drawback of this method is that the unwiseensionality of the state space must be constant for all one trials (Spaak et al., 2017; Willumsen et al., 2022).

5. Obstacles in expoundation and expounding CNS operations

In experimental neuroscience, scientists usupartner meabrave changes in some reliant brain variables transport aboutd by experimental manipulations of autonomous variables (Figure 13). The meabraved changes in the watchd reliant variables, spiking, membrane potentials, field potentials, magnetic and electrical fields, blood flow, and BOLD signals are expounded joind to outer, chooseogenetic, or straightforward brain stimulation, particular behaviors, rewards, memory retention, clear behavior, and changes in carry outance. Careful analyses of the meabravements normally show that only inmeaningful proportions of the variance or inestablishation in the data can be expounded as joind to stimuli, motor behavior, reward behavior, and carry outance (Urai et al., 2022). This uncovers cut offal fundamental asks for the expoundation of CNS meabravements.

Summarizing the conclusions from the analysis of the barriers hampering persist, the premises for the expoundation of experimental results in systems neuroscience are:

Lack of reference or baseline conditions.
The continuously changing spiking and changing transmembrane currents everywhere in a CNS implies that one cannot utilize a classical cainclude-effect analysis: if A, t₁ then B, t₂.
Central anxious systems, in contrast to complicated vibrantal systems, have no evident initial state definition, neither locpartner nor globpartner. This implies that we cannot expound the future states of the system from local or global initial states.
Neither can we suppose any pre-existing unwiseensional state space, becainclude unwiseensionalities change concurrently in many locations in a CNS. This implies that vibrantal systems theory may be of restrictcessitate cherish.
Time is probably not an autonomous variable for CNS operations. In a CNS, vibrants are space and time reliant, i.e., spatial vibrants. This implies that pooling data from separateent neurons or locations and temporal and spatial averaging demolish the spatial vibrants. Repeated observations show that spatial vibrants can vary from trial to trial. This in turn implies that conclusions must be drawn from the outcomes of one trials. Moreover, since outer clock time does not distinctly retardy to the activities of neurons, other types of causality, e.g., Granger causality, are of no help. Assumptions of statistical stationarities of spiking or transmembrane currents are most anticipateed invalid.
Referring to outer input or motor, behavioral, output has restrictcessitate exstructureatory power, becainclude many CNS processes are intrinsic and relatively autonomous.
Separating task joind activities from unintentional and intrinsic cognitive activities in a CNS is still difficult.
For experiments in humans, introspection is invalid to expound CNS activities, becainclude brains produce experience and motor activity as results of processes lasting from some 120 ms to more than 1,000 ms (Fried, 2022). These spatial vibrants processes, which initipartner are reasonablely in-accessible, must reach to some stage of organization before the human subject can inestablish.
Current neuroscience is restrictcessitate to watch spatial vibrants in discrete parts of CNS only.

Theoretical frontier 1: How can we reliably expound our results?
Theoretical frontier 2: How can we reliably expound our results?
Theoretical frontier 3: How can we commence to produce theories of brains?

A scientific brain theory would be an experimenloftyy based ambiguous exstructureation on how the elements in brains include at all scales of observation under all conditions. A theory must serve as a conceptual structure in which gaps of understandledge and inconsistencies can be isotardyd. It must recommend rules and coherent exstructureations, to some extent encompassing separateent scales of observation. With the recent technical persists, neuroscience now is free to scrutinize complicated brain tasks and conditions in many species. Hopebrimmingy, scientists could include their experimental results to discover principles which could be part of a brain or CNS theory.

Author’s notice

Despite a century of anatomical, physioreasonable, and molecular bioreasonable efforts scientists do not understand how neurons by their accumulateive includeions produce percepts, thoughts, memories, and behavior. Scientists do not understand and have no theories expounding how brains and central anxious systems toil. The normal exstructureations are that scientists deficiency methods, techniques, and fruitful data analysis to achieve this goal. These are no lengthyer the main reasons. The main obstacles for systems neuroscience seem to be conceptual. That is deficiency of concepts rooted in firm experimental results, unvital assumptions, and cgo in on analogies from other disciplines (inestablishation theory, computer science, physics, and psychology). Brains cannot be understood treating time and space as autonomous variables. Methods are now useable for measuring spatial vibrants at microscopic to mesoscopic scales, also in one trials. This paper condenses the conceptual, theoretical, statistical, and experimental train obstacles which necessitate to be take awayd to fruitfully include and expound results with these novel methods.

Data useability statement

The innovative contributions currented in the study are integrated in the article/Supplementary material, further inquiries can be straightforwarded to the correplying author.

Author contributions

The author validates being the sole contributor of this toil and has apvalidated it for uncoveration.

Acunderstandledgments

I am in debt to the participants in the How do brains toil toilshops: Adrienne Fairhall, Alain Destexhe, Alessandro Treves, Alexa Riehle, Bing Brunton, Bruce McNaughton, Carl Petersen, Daniel Durstewitz, David McCormick, Dora Angelaki, Dew Robson, Gaute Einevoll, Gustavo Deco, Gyørgy Buzsáki., Hajime Hirasi, Jennifer Li, Kenneth Harris, Maria Sanchez-Vives, Mark Humphries, Micha Tsodyks, Michael Brecht, Mikael Lundqvist, Nicolas Brunel, Peter Dayan, Ricchallenging Morris, Riitta Hari, Sara Solla, Sonja Grün, Sophie Denève, Tatiana Engel, Tatjana Tchumachenko, Tatyana Sharpee, Terrence Sejnowski, Thomas Mrsic-Flogel, Viktor Jirsa, Wulfram Gerstner, Xaq Pitkow, Yasser Roudi, Yiota Poirazi, and Zhaoping Li, who by their toil, ideas, and converseions uncovered landscapes of neuroscience frontiers establishing the conceptual background for this study. Special thanks to Jens Midtgaard, Madelaine Bonfils, John Hertz, and Gilad Silberberg for critics of earlier versions of the manuscript and elucidateing converseions.

Conflict of interest

The author proclaims that the research was carry outed in the absence of any commercial or financial relationships that could be consreald as a potential struggle of interest.

Publisher’s notice

All claims conveyed in this article are solely those of the authors and do not necessarily recurrent those of their affiliated organizations, or those of the publisher, the editors and the scrutinizeers. Any product that may be appraised in this article, or claim that may be made by its manufacturer, is not promised or apvalidated by the publisher.

Supplementary material

The Supplementary Material for this article can be set up online at: https://www.frontiersin.org/articles/10.3389/fnsys.2023.1147896/brimming#supplementary-material

Supplementary Video 1

Single trial write downing of temporal derivative of the voltage signal (shothriveg excitation and suppression) over visual areas 17, 18, 19, and 21 (see Figures 7, 8). From −180 ms to +20 ms the movie shows unintentional un-systematic spatial fluctuations. From 21 to 200 ms systematic spatial excitation and suppression vibrants in response to a 3° × 3° stationary square at 0 ms, exposed for 133 ms.

Supplementary Video 2

Statisticpartner meaningful (p < 0.01 after Bonferroni rightion) depolarization in visual areas of a ferret in response to a bar moving downwards commenceing in the peripheral field of see. The retina is stationary. Note that the bar then is mapped as moving excitation over the cortex. However, at 104 ms the neurons in area s 19/21 compute an excitation far ahead of the bar mapping. After feedback to areas 17/18 this repeats here. The bdeficiency holes show the electrode penetration sites alengthy the border between areas 17 and 18 correplying to the vertical meridian. When the spiking at any layer of the cortex becomes statisticpartner meaningful (p < 0.01) the hole turns white. Note the mapping of the future bar trajectory when the bar recurrentation on the cortex has accomplished the left white arrow (155 ms). Note also how the object mapping, depictd by the boiling spot in area 17/18 actupartner chases the cortical route foreseeed already at 160 ms. Animal 410 (from Harvey et al., 2009).

Supplementary Video 3

Three-unwiseensional visualization of derivative of the voltage signal shothriveg excitation (orange to red) and suppression (unwise green to blue) in areas 17, 18, 19, 21 of a ferret to an object moving down from time 0 ms in the field of see. For localization of area borders (see Figure 9) (from the top areas 17, 18, 19, and 21). Note the non-licforfeit spatial vibrants, feedback from areas 21 and 19 to 18 and 17 at 115 ms, foreseeive excitation 135-195 ms and suppression chasing the excitations from 500 ms (from Harvey et al., 2009).

Supplementary Video 4

Spiking in layer 4 of areas 17 and 18 of 8 ferrets. Electrode positions are labeled with white circles. Color scale shows the proportion of trials giving ascend to meaningful incrmitigates (contrastd to pre-trial baseline). Note that meaningful spiking gets remercilessed to the retinotopic mapping after 90 ms (time on top) (from Roland et al., 2017).

Supplementary Video 5

Spatial derivatives in areas 17, 18, 19, 21, to a 3° × 3° stationary square at 0 ms, exposed for 250 ms. Compare with Supplementary Video 1.

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