Neural response are strongly sensitive to behavioural state ., The onset of movement such as running and whisking is coincident with prominent modulations in neural activity in sensory areas 1–3 ., The rodent whisker system has become a key model system within which to investigate these changes 4–6 ., The onset of active whisking in a previously quiet but attentive rodent is correlated with a marked reduction in endogenous synchronous neural activity of neurons in sensory areas; quantified as a reduction in low frequency fluctuations and a decrease in correlations between the membrane potentials of neurons in the barrel cortex 4 ., Furthermore , membrane potential responses to experimentally induced perturbations of the whisker are also reduced by the presence of whisking 6 ., These changes suggest that movement reduces neural gain 7 , 8 in the barrel cortex suppressing neural fluctuations and sensory response ., Several internal pathways have been implicated in this gain regulation including various neuromodulatory pathways 9 , 10 , intracortical feedback modulation by motor areas 11 or they could be directly triggered by changes in sensory input 12 , 13 via thalamo-cortical projections 14 ., Despite this gain reduction , robust responses to sensory input occur during active contact events when the whisker collides with an object placed in the whisk field 5 , 6 ., Thus , a whisking-induced gain reduction cannot by itself account for the difference in sensory responses to whisker perturbations and active contact events without appeal to additional mechanisms 15 ., The reafference principle ( RP ) 16 also does not straightforwardly explain these differences ., The RP explains the amplitude of sensory response by a mismatch between the actual sensory input and its prediction , where the prediction is based on an efference copy ( an internal copy of motor command ) ., But the RP does not explain why sensory responses to whisker perturbations , which are always unpredicted , are suppressed during movement ., Active behaviours are defined by closed-loop feedback interactions between brain/body/environment which are central to motor control and , it has been argued , pivotal to account of perceptual processes 17–19 ., During active whisking reafferent sensory input ( sensory input resulting from ones own actions ) conveys information about proprioceptive sensory feedback of whisking and which informs the subsequent motor control of the vibrissae 20 , 21 ., Repeated cycles of reafferent sensory input followed by motor output constitute a closed-loop feedback interaction between cells in the barrel cortex and the vibrissae 22 ., In this work , we show that in this system closed-loop feedback mediated by whisking vibrissae can:, 1 . Suppress synchronous endogenous neural fluctuations and passive sensory responses ,, 2 . Account for large response to active touch events because of a transient interruption of this feedback ., The results provide a nuanced view of predictive coding where neurons represent predictions errors about consequences of motor actions rather than the difference between the predicted and actual sensory input ., More generally these results strongly support the centrality of closed-loop interaction in perceptual apparatus 17 by suggesting a specific role they play in event detection ., To support a key prediction of this theory we examine how closed-loop interactions in a motor control behaviour impact on neuronal fluctuations ., Specifically , we re-analysed data from a second system , a larval zebrafish behaving in a virtual reality where fictive water flow is simulated by a grating ( striped image ) drifting across the fish retina 23 ., In this set up zebrafish larvae are immobilised with a neuromuscular blocker ., The fishs attempted movements relative to the grating are monitored through motor neuron activity and translated into appropriate modulation of the velocity of the grating 23 ., With data from this setup we show that the presence of closed-loop interactions between neurons and fictive swim speed causes the suppression of synchronous neural fluctuations across the fish brain in a manner analogous with the rodent whisker system ., Further we show that the amount of this suppression for each neuron is correlated with the strength of its involvement in the optomotor signaling ., Together , these results suggest that understanding changes in neural activity across the brain caused by the onset of movement requires the study of closed-loop brain/body/environment interactions beyond open-loop sensory paradigms ., Thus we strongly support the argument that a full understanding of phenomenology of neural circuits during active behaviors requires moving away from the idealisation of the brain as an input/output information processor toward its role as a dynamic control system regulating behaviour 19 ., In moving animals , the brain receives sensory input that originates in the external environment , or exafferent sensory input ( Fig 1A , blue arc ) ., In addition , efferent motor commands ( Fig 1A , green arc ) drive the body and environment and induce reafferent ( self-generated ) sensory input ( Fig 1A , red arc ) 16 , 24 ., To develop an intuition of how closed-loop feedback , mediated by reafferent input , could impact on neural activity we introduce two model conditions ., First , we assume that when an animal is not moving the brain receives only exafferent input , we describe this as an open-loop condition ( Fig 1B , top ) ., Second , when the animal begins to move the brain interacts with the environment coupling motor action and reafferent sensory input , we refer to this as a closed-loop condition ( Fig 1C top ) ., Note: it is likely that some reafferent input is always present but our focus here is on the effect that the onset of a previously absent reafferent sensory pathway could have on neural activity ., We examine these two conditions in a simple idealized model , see 17 for a similar idealisation , where brain variable B ( which describes collective neural activity , e . g . , membrane potential activity ) receive input from , or interacts with , the body and environment ., In the open-loop condition the collective neural activity , Bo ( t ) , is assumed to be described in term of a first-order linear differential equation ,, dBo ( t ) dt=−Bo ( t ) τ+I ( t ) +ξo ( t ) ,, ( 1 ), where ξo is white noise of instantaneous variance σ2 generated inside the brain , t is time , τ is the time constant of the system and I ( t ) is exafferent input ., Essentially , in the absence of input , we represent collective neural activity as a simple leaky integrator system with leak timescale τ driven by endogenous noise ( see Fig 1B , bottom , for traces ) ., Of interest here is the magnitude of fluctuations which can be calculated as the autocorrelation peak ( instantaneous variance ) of variable Bo which is Peako = σ2 τ/2 , and the gain of the response to sensory input ( calculated as the ratio between a static input and an equilibrium response ) , which is simply Gaino = τ ., Thus in this simple system both the gain and the fluctuations are determined by the timescale of the endogenous dynamics ., However , during the closed-loop condition we write the dynamics of the brain variable ,, dBc ( t ) dt=−Bc ( t ) τ+wBc ( t ) +I ( t ) +ξc ( t ) ,, ( 2 ), where we have idealised reafferent input as a simple self-feedback signal with strength w , i . e . , we have assumed this feedback is linear and instantaneous ( we will relax this assumption later ) ., In this condition , the continuous cycles of reafferent input constitute a closed-loop feedback signal to the brain ., The presence of this feedback changes the effective time constant to τeff = τ/ ( 1−wτ ) ., The magnitude of the fluctuations is now characterized by autocorrelation peak Peakc = Peako/ ( 1−wτ ) and the effective gain of the system is Gainc = Gaino/ ( 1−wτ ) ., In particular , if this feedback is negative ( w < 0 ) , it will suppress both fluctuations and the gain of sensory responses , see Fig 1B and 1C ( bottom panels ) ., This very simple model suggests that , in principle , closed-loop feedback mediated by the body/environment could have a direct impact on neural activity ., One way to accentuate sensory responses is described in Fig 1D ., Here the brain initially has low closed-loop gain ( Gainc = τ/ ( 1−wτ ) ) and thus exhibits suppressed fluctuations ., However , if during a sensory event ( Fig 1D , grey bar ) closed-loop feedback is interrupted , e . g . , if whisking is interrupted by contact with an object ( see below ) , then brain will have temporarily high open-loop gain ( Gaino = τ ) ., Thus the combination of a large sensory response and suppressed background fluctuations prior to sensory event can accentuate signal-to-noise ratios ., In the following , we explain how these three conditions can be realized in the rodent whisker system ., In this study we proposed the idea that negative closed-loop sensory feedback during active behavior reduces network gain , which in turn , suppresses synchronous neural fluctuations and modulates sensory responses ., We supported this with modelling and data analysis in the whisker system and in a behaving zebrafish , see summary Fig 7 ., The formal component of our theory , i . e . , that closed-loop sensory feedback can modulate a systems gain , is well documented in dynamical systems theory and control theory 32 , 33 ., This gain control occurs even though the pathways mediating feedback are purely additive ( c . f . Eqs 1 and 2; i . e . , effectively repeated cycles of feedback accumulate over time and produce a multiplicative effect ) ., Thus , a constitutively active closed-loop feedback that mediates action-perception cycles is essential for the form of gain control we propose ., This means that discrete and intermittent involvement of reafferent input does not imply gain modulation ., For example , the classical reafference principle explains neural responses by a one-time detection of the mismatch between an efference copy ( predicted ) and reafferent ( actual ) input 16 ., However , this situation is likely an inaccurate idealization to describe the closed-loop systems studied here ., For example , in the zebrafish system , swim bouts typically occur every 700 ms and this interval closely overlapped with the peak of the estimated sensory feedback interaction ( Fig 6B ) ., Hence , the neural responses in the fish experiment suggest a more dynamic system , where neural activity evoked by many cycles of action and sensation are continuously and mutually interacting ., The idea that closed-loop feedback is central to cognition is not new and has early precedents in behavioral psychology 19 , resonate with a movement in embodied cognitive science 18 , 34 , 35 and has recently been proposed as concrete alternative to input/output conception of perceptual processing 17 , 36 ., Our work shares the view of these proposals and provides a specific example where brain function is contingent on closed-loop interactions between brain/body/environment ., Furthermore , we provided a mathematical model showing why neural dynamics underlying cognitive states cannot be recapitulated even if the sensory input during active behavior is identically repeated , i . e . , a replay condition 37 ., The presence of continuous negative closed-loop sensory feedback during active behavior is fundamental for our theory ., In our rodent study we assumed negative closed-loop sensory feedback was mediated directly by a cortical-whisker circuit ., However , our theory is agnostic to the detail of the neural implementation and several other schemes are possible ( see S2 Appendix ) ., This assumption is consistent with the idea that the barrel cortex comprises a nested set of servo control loops that regulate various aspects of whisker dynamics 22 ., At the level of the whole vibrissa system multiple parallel and nested feedback loops both positive and negative most likely exist 22 ., In zebrafish , the presence of negative feedback during swimming behavior is a priori necessary for optic-flow stabilization behavior because the fish must act in opposition to perceived optic flow in order to minimize horizontal displacement 38 , 39 ., Interestingly , neurons that received strong negative feedback and were substantially stabilized were located in the cerebellum ( Fig 6D ) ., This is consistent with the theoretical viewpoint that the cerebellum is strongly involved in the action-perception cycle 40–42 ., We suggest that closed-loop sensory feedback plays a major role in brain state control ., However , importantly , we do not propose this mechanism is mutually exclusive with other mechanisms , such as thalamo-cortical input 25 or neuromodulation 10 , 43 , 44 because brain state transitions also occur in the absence of sensory feedback e . g . , the onset of running that does not change the visual input 3 , 45 , during sleep 46 , 47 , or by dissection of the sensory nerve 5 , 25 ., Mechanisms underlying brain state transitions are likely to be redundant and occur even in the absence of mechanisms , such as thalamo-cortical input 25 or corollary discharge 26 , albeit involving further delay ( see S1 ) ., Such functional redundancy may help to maintain the stability of brain state 44 , 48 , 49 ., Furthermore , the relative importance of internal and external mechanisms might adaptively change in an experience-dependent manner 50 ., In the whisking model , we proposed that the regulation of cortical gain by closed-loop sensory feedback could explain enhanced active touch ., Specifically , negative sensory feedback during whisking reproduces suppressed fluctuations and reduces responses to passive whisker stimulation ( see Figs 2 and 3 ) ., Moreover , robust neural response to active touch events could be explained by the interruption of this feedback when the whisker is driven into an external object ., These interruptions transiently release the cortex from a low gain state and enhancing sensory responses to salient sensory stimuli ., This mechanism for active touch contrasts with the account of sensory processing suggested by the reafference principle 16 , which postulates that motor efference is discounted from sensory input , allowing animals to sense exafferent signals ( externally caused sensory input ) without being confounded by the consequences of their own motor actions ., In contrast , our theory suggests that the sensory system is insensitive to pure exafference during active sensing 4; see Fig 3 , but sensitive to the interruption of reafference which may allow animals to focus attention on the consequences of their own motor actions ., This idea is supportive of other work that has cast doubt on the role of efference copy during active sensing 51 ., This mechanism is also distinct from the most common form of predictive coding 52 , where neural activity represents the error between the actual and the brain’s prediction of sensory input ., Instead our suggestion could be viewed as a more specific form of predictive coding where neurons represent predictions errors about consequences of motor actions , in this sense it is closer to the idea of active inference 53 , 54 ., While it is straightforward to generalize this sensory mechanism to other tactile systems , its implication for other modalities is less clear ., However , in theory , closed-loop sensory feedback could be interrupted anywhere along the action-perception cycle , thus dynamically regulating neural gain ., The timely interruption of this feedback , possibly related to transient freezing of behavior , could serve as a general mechanism for temporarily accentuating neural responses against a background of reduced noise ., For example , closed-loop sensory feedback could be gated by the frequency of miniature eye movements 55 a hypothesis that complements a previous proposal suggesting such movements are under active closed-loop control 56 ., Furthermore , cerebellum neurons , which are strongly involved in the sensory-motor cycle , could be suppressed in anticipation of salient sensory events by a relevant brain area , such as the reticular formation 31 , 57 ., The importance of using naturalistic sensory stimuli to study and manipulate brain state dynamics is widely demonstrated 58 ., However , an important prediction of our theory ( Fig 4 ) , supported by our experimental findings is that brain dynamics during active sensing cannot be fully recapitulated or re-encoded , even if the same sensory input is precisely recorded and replayed back into a passive brain ., These results provide evidence that brain state during active behaviors can only be accurately understood by a quantitative account of ongoing brain-environment interactions 18 ., To investigate the ‘in principle’ feedback between barrel cortex and whiskers we model a simple cortical circuit that interacts with a single whisker , see Fig 2A ., Our cortical circuit comprises of N excitatory and N inhibitory neurons ( i = 1…N are excitatory and i = N+1 , … , 2N are inhibitory , N = 100 ) modeled as a linear dynamical system by ,, x . i=−xi+∑j=12Nwijxj−ai−wxθθp+ξi+I ,, which is numerically simulated by a Euler forward integration method with time-bin dt = 0 . 5 ms . Hereafter , all time derivatives are taken to represent single-step differences divided by dt ( e . g . x ., ( t ) =x ( t+dt ) −x ( t ) /dt ) , but we omit the ms time unit ., wij is the synaptic strength from neuron j to i , ai is an adaptation current described below , θp is the whisker protraction angle interacting with neurons with weight wxθ = 0 . 002 , I is exafferent input that takes I = 0 . 035 upon whisker stimulation and otherwise zero , and ξi is independent white noise of unit variance added to each neuron ., We interpret xi as both the firing rate and membrane potential , assuming a roughly linear relationship between the two ., Entries in the connectivity matrix are assigned as wij = bijJ + b′ijg for excitatory synapses ( j = 1… , N ) and wij = −b″ijg for inhibitory synapses ( j = N + 1 , … , 2N ) , where bij , b′ij , b″ij are all random binary values that take b0 > 0 with probability p = 0 . 1 and 0 with probability 1 − p , respectively ., The weights are scaled by J=1pN and g=g0√2Np ( 1−p ) , so that dynamics are insensitive to the parameter values of p and N . Note that the eigenvalue spectrum of the connectivity matrix wij is centered around b0 and spread with the radius b0g0 in the limit of large N . Hence , the network is excitation dominated ., The variability of weight values across neurons is controlled by the magnitude b0g0 of the excitatory-inhibitory-balanced component and this variability is controlled by the parameter g0 = 0 . 05 , which reproduces highly synchronized up/down-like fluctuations during the quiet state ., To promote significant network fluctuations observed in the barrel cortex we scale of the connectivity matrix b0 such that the lead eigenvalue of this matrix is close to unity ( ≈ 0 . 975 and the dynamics are close to instability ., We include an adaptation current that gives these fluctuations a low frequency ( ca . 1 Hz ) component modelling up/down-like oscillations 59–61 in the absence of neuron/whisker interactions ., The adaptation current is integrated as, ai˙=−0 . 07ai+0 . 008xi, Over time , the adaptation variable slowly builds upon neural activity and suppresses neurons , resulting in the ca ., 1-Hz oscillation ., Consequently , in the absence of interactions with the whisker , implemented by setting wxθ = 0 , this simple network reproduces the power spectrum and cross-correlogram of neurons in the barrel cortex 5 , 6 , see Fig 2B and 2C ., We model a simple flexible vibrissa as two hinged sections ( with bending angle θh ) connected at the base ( with protraction angle θp to the body ) of unit length which are constrained by simple torsion springs with spring constant k1 and k2 respectively , see Fig 2A ., We assume the whisker is light and frictionless and simulated it by numerically minimising the energy of the system ,, E=k1 ( θp−θeq ) 2+k22θh2 ,, where θeq equilibrium value of the base spring ., Here , only the ratio k1/k2 is important for the results and , without losing generality , we set k1 = 1 ., The central hinge spring has an equilibrium value of zero angular displacement and thus tends to align both sections ., Whisking is driven both by the cortex and a central a pattern generator ( CPG ) 62 ., Specifically , the equilibrium value of the base spring , θeq is set as ,, θeq˙=−0 . 93θeq+wθxN∑i=1Nxi+u ,, where the second term on the right-hand side is the sum of activity in the cortical excitatory population and the third term is the external CPG activity ., Here u is modeled as simple stochastic oscillator , given by, u˙=− . 98u+2πFwhiskv+ξu, v˙=− . 98v−2πFwhisku+ξv ,, where Fwhisk = 10Hz is the frequency of the oscillator and ξu , ξv are independent Gaussian white noise ., wθx = 0 . 085 describes the relative strength of the cortex versus the CPG in driving the whisker variable ., With this parameter , the whisker is mainly driven by the CPG and is only modulated by cortical activity ., In this model , most excitatory neurons respond to whisker retraction and drive whisker protraction ., Adding a separate counterpart population that responds to whisker protraction and drives whisker retraction in a similar manner does not change the model’s behavior ., We simulate a passive deflection of the whisker by a brief injection of input of I = 0 . 035 to the cortical neurons for c . a 25 ms . The magnitude of this input approximately matches the evoked change over the standard deviation of the membrane potential ( ΔVm/σVm ) in response to magnetic whisker deflection during the whisking condition 5 ., Contact events are simulated by simulating a horizontal solid wall is placed above the whisker ( 1 unit length away ) ., To simulate contact with the wall we solve the energy equation subject to the length constraint in the vertical direction ,, sin ( θp ) +sin ( θp−θh ) <1 ., Thus , as the whisker collides with the wall it deforms accordingly , see Fig 2A ., By adjusting the relative stiffness of each torsion spring ( i . e . k2/k1 ) , we can control the degree to which the protraction angle is affected by contact events , e . g . , if the whisker is very flexible , the protraction angle will change continuously , despite contact of the tip ., During contact we also inject an input ( I = 0 . 035 ) to the cortical neurons for the duration of the contact event , but for no longer than 25 ms to simulate contact-detection signal that results from the stereotypical response of pressure sensitive cells in the trigeminal ganglion 27 ., The model was run for 200 s in the closed loop , open-loop , and sustained period of active touch to calculate all quantitative measures ., To quantify the discriminability of whisker contact events we calculated an information theoretic measure of generalized signal-to-noise-ratio ., Specifically , we calculated the Chernoff distance 63–65 between probability distributions , p1 ( x ) and p0 ( x ) , in the presence or absence of a sensory event , respectively ., Specifically , this measure, Ψ ( p1∥p0 ) ≡−min0<λ<1log∫p1λ ( x ) p01−λ ( x ) dx, summarises the detectability of whisker stimulation based on population responses and , unlike a naive calculation of signal-to-noise ratio , is applicable even when p1 ( x ) and p0 ( x ) are very different distributions ., For our model , the probability distribution for each condition is well described by a Gaussian distribution ,, p0/1 ( x ) =|2πC0/1|−1/2exp ( −12 ( x−μ0/1 ) C0/1−1 ( x−μ0/1 ) ) ,, where C0/1 and μ0/1 are covariance matrix and vector of means , respectively , in the presence ( with subscript 1 ) or absence ( with subscript 0 ) of a sensory event ., By substituting this into the expression for Chernoff distance and employing the Gaussian integral identity and expressing the Chernoff distance in terms of C0 , C1 , and μ0 , μ1 , we calculate the covariance and mean between a small number of neurons ( here three ) , randomly selected from the cortical network described above ., We calculate covariance’s across ensembles of 500 networks every 10 ms for a period of 1 s , starting at the onset of the sensory event ., Minimization with respect to λ is computed numerically ., In a transgenic fish expressing the calcium indicator GCaMP2 brain-wide calcium activity was monitored using a two-photon microscope to scan single planes in the brain ., We analyzed the calcium signal ( ΔF/F ) at various sample frequencies ( ca . 2−3 Hz ) across 1908 cells in 32 fish , see 23 and electrical recordings of swim power ., We analyzed data taken from a 6-min recording of 1−6 prominent calcium sources per fish , putative neurons , across 600 trials ., In the first 3 min , the fish performed the closed-loop optomotor behavior ., For the subsequent 3 min , each fish was presented with the stimulus received in the closed-loop stimulus which is a repeat of what the animal experienced in the previous 3 min , the replay condition ., In the original study , the gain ( i . e . , the multiplicative factor between fictive swim power , and the speed of visual feedback ) was alternated between a high and low gain condition every 30 s ., This gain alternating protocol is not relevant to the current study ., To reduce this variability in data , we subtracted the mean activity level in each gain setting in our analysis ( from both brain and behavior variables ) ., Notably , our main results were qualitatively the same , even without such subtraction of the means ., We distinguish variables in the closed loop condition ( Bc and Ec ) and replay condition ( Br and Er ) , see Fig 5B ., Specifically , we assume that the closed-loop dynamics in the frequency domain are described by the following equations ,, Bc ( ω ) =F ( ω ) Ec ( ω ) +RBc ( ω ), ( 4 ), Ec ( ω ) =G ( ω ) Bc ( ω ), where F ( ω ) is an afferent filter describing the interaction from the environment to the brain ( i . e . , the Ec → Bc filter , see Fig 5B dashed blue arrow ) and G ( ω ) is an efferent filter from the brain to the environment ( i . e . , the Bc → Ec filter , see Fig 5B dashed green arrow ) , respectively , and RBc ( ω ) is the residual inputs not accounted of by the filters ., Note: we have assumed that the noise on the environment is negligible , this is a reasonable assumption given that visual flow is directly modulated by motor nerve activity ., Similarly , we also write the replay dynamics in the frequency domain as ,, Br ( ω ) =F ( ω ) Ec ( ω ) +RBr ( ω ), ( 5 ), Er ( ω ) =G ( ω ) Br ( ω ) ., In the replay condition , neurons are driven by the recorded visual stimulus in the closed-loop condition , which is determined by fish’s motor activity in the closed-loop condition Ec ., Note: we have made the assumption that F ( ω ) and G ( ω ) are the same filter in the both conditions ( i . e . , the interactions with the same color in Fig 6A have the same property ) because the sensory and motor circuits in the brain remain the same between the conditions ., We use Eq 5 in the replay condition to fit the linear filters F ( ω ) and G ( ω ) because the computation would be more involved in the closed-loop condition than the replay condition ., We first calculate linear filter F ( Fig 6A , solid blue arrow ) that minimizes the mean square error between the observed variable Br and the convolution F * Ec over time ., Next , we determine G ( t ) by first calculating the residual variability of neural activity in the replay condition that cannot be accounted for by the closed-loop environment , i . e . , RBr ( ω ) =Br ( ω ) −F ( ω ) Ec ( ω ) and subsequently calculating how RBr drives the environment in the replay condition Er , effectively determining the Br → Er interaction ( Fig 6A , solid green arrow ) ., The filters were constrained as a superposition of Laguere functions ., We use Laguere functions up to the order that best satisfied the Akaike Information Criterion 66 ., Almost all filters had an order that was mid-range between 1 and 15 ., The Ec → Br → Er interaction ( Fig 6A solid orange arrow ) is then straightforwardly computed by the convolution of both filters , H ( ω ) = F ( ω ) G ( ω ) ., Based on the assumption that the filters are the same in the two conditions , we assume that self-feedback in the closed-loop condition ( Fig 6A , dashed orange arrow ) is the same as H ( ω ) ., In our investigation , we calculated the ratio of the low frequency power of neural fluctuations between the closed-loop and replay conditions ., We then compare this empirical ratio with the theoretically expected ratio based on the estimated filters ., To derive this theoretically expected ratio , we write the dynamics of neural activity in the closed- and replay conditions in the frequency domain as ,, Closed‑loop:Bc ( ω ) =H ( ω ) Bc ( ω ) +RBc ( ω ) = ( 1−H ( ω ) ) −1RBc ( ω ) Replay:Br ( ω ) =H ( ω ) Bc ( ω ) +RBr ( ω ) ,, where H ( ω ) = F ( ω ) G ( ω ) is the estimated combined filter in the frequency domain and we assume the noise in the closed- and replay conditions have the same power spectrum , i . e . , RBc ( ω ) 2=RBr ( ω ) 2 ., The ratio of the power between each condition is then ,, Bc ( ω ) 2Br ( ω ) 2=1H ( ω ) 2+1−H ( ω ) 2 ., We also investigated the effect of accumulative cycles of feedback on brain dynamics by comparing the full closed-loop effect with a control effect that includes only one-time feedback ., Namely , we can expand the contribution of each cycle in a geometric series as, Bc ( ω ) = ( 1−H ( ω ) ) −1RBc ( ω ) = ( 1+H ( ω ) +H2 ( ω ) +H3 ( ω ) ⋯ ) RBc ( ω ), where the O ( Hn ) term in the above Taylor expansion describes the effect from signal propagation along the feedback loop for n times ., By neglecting the contributions with n>1 , we can write the effect of a single cycle of feedback effect as ,, B1 ( ω ) = ( 1+H ( ω ) ) RBc ( ω ) ., This yields an alternative expression for the ratio of the power between each condition that only includes one-time effect of feedback as ,, B1 ( ω ) 2Br ( ω ) 2=1H ( ω ) 2+1+H ( ω ) −2 ., To further investigate how the effective interaction between the brain and the environment depends on the closed-loop feedback , we compare Ec → Br filter in the replay condition and the Ec → Bc filter in the closed-loop condition naively computed by neglecting closed-loop effects ., Notably , the naïve Ec → Bc filter in the closed-loop condition generally has an acausal component , because the brain Bc and the environment Ec are mutually interacting ( see below ) ., Thus to calculate these filters we use Hermite rather than the Laguere functions to capture the acausal ( t<0 ) side of the filter ., To quantify the difference between these filters , using Eq 4 , we write, Ec ( ω ) = ( 1−H ( ω ) ) −1 ( G ( ω ) RBc ( ω ) ) ,, and thus the naïve Ec → Bc filter in the closed-loop condition is, Bc ( ω ) Ec* ( ω ) Ec ( ω ) Ec* ( ω ) =F ( ω ) + ( Ec ( ω ) RBc* ( ω ) Ec ( ω ) Ec* ( ω ) ) *=F ( ω ) + ( G ( ω ) 1−H ( ω ) ) *|RBc ( ω ) |2|Ec ( ω ) |2, where * describes complex conjugate ., Hence , this filter is different from the corresponding filter F ( ω ) in the replay condition by the second term ., To predict the second term without knowing RBc , we again assume |RBc ( ω ) |2≈|RBr ( ω ) |2 , where the latter spectrum is based on the residual RBr computed in the replay condition .
During active behaviours like running , swimming , whisking or sniffing , motor actions shape sensory input and sensory percepts guide future motor commands ., Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and , it has been argued , for perceptual processes ., This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood ., Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and , consequently , change endogenous neural fluctuations and responses to sensory input ., We support this theory with modeling and data analysis in two vertebrate systems ., First , in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex ., We argue this suppression provides an appealing account of a brain state transition ( a marked change in global brain activity ) coincident with the onset of whisking in rodents ., Moreover , this mechanism suggests a novel signal detection mechanism that selectively accentuates active , rather than passive , whisker touch signals ., This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input ., We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator ., We show , as predicted by this theory , that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour ., More generally we argue that our results demonstrate the dependence of neural fluctuations , across the brain , on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone .
Animals actively exploring or interacting with their surroundings must process a cyclical flow of information from the environment through sensory receptors , the central nervous system , the musculoskeletal system and back to the environment ., This closed-loop sensorimotor system is essential for an animals ability to adapt and survive in complex environments ., Importantly , closed loop feedback signals also regulate brainwide neural circuits for behavior ., Specifically , the activity of coherent populations of neurons inform motor behaviours and in turn are influenced by sensory feedback signals mediated by the environment ., We develop a theory that suggests that this feedback can explain the marked changes in large-scale neural dynamics and sensory processing ( together referred to as brain state ) that coincide with the onset of active behaviours ., This feedback may contribute to flexible context dependent neural computations in brain systems .
control theory, medicine and health sciences, fish, swimming, engineering and technology, membrane potential, vertebrates, electrophysiology, social sciences, neuroscience, animals, biological locomotion, motor neurons, control engineering, animal anatomy, systems science, mathematics, zoology, computer and information sciences, animal cells, touch, animal physiology, cellular neuroscience, psychology, eukaryota, cell biology, vibrissae, physiology, neurons, biology and life sciences, cellular types, physical sciences, sensory perception, organisms