State-aware detection of sensory stimuli in the cortex of the awake mouse
The large majority of what we know about sensory cortex has been learned by averaging the response of individual neurons or groups of neurons across repeated presentations of sensory stimuli ., However , multiple studies in the last three decades have clearly demonstrated that sensory-evoked activity in primary cortical areas varies across repeated presentations of a stimulus , particularly when the sensory stimulus is weak or near the threshold for sensory perception 1–3 , and have suggested that this is an equally important aspect of sensory coding as the average response 4–6 ., Variability is thought to arise from a complex network-level interaction between sensory-driven synaptic inputs and ongoing cortical activity , and single-trial response variability is partially predictable from the ongoing activity at the time of stimulation ., A large body of work has focused on characterizing this relationship between notions of cortical “state” and sensory-evoked responses 7–13 , establishing some simple models of local cortical dynamics 14 ., Less is known about the impact of this relationship for downstream circuits ( though see 15 , 16 ) ., As an example , consider the detection of a sensory stimulus , which has been foundational in the human 17–22 and non-human primate psychophysical literature 23 , 24 and serves as one of the most widely utilized behavioral paradigms in rodent literature 25–27 ., In an attempt to link the underlying neural variability to behavior , the principal framework for describing sensory perception of stimuli near the physical limits of detectability is signal detection theory 28 ., A key prediction of signal detection theory is that , on single trials , detection of the stimulus is determined by whether the neural response to the stimulus crosses a threshold ., Particularly large responses would be detected but smaller responses would not , so variability in neural responses would lead to , and perhaps predict , variability in the behavioral response ., From the perspective of an ideal observer , if variability in the sensory-evoked response can be forecasted using knowledge of cortical state , the observer could potentially make better inferences , but in traditional ( state-blind ) observer analysis , the readout of the ideal observer is not tied to the ongoing cortical state ., In this work , using network activity recordings from the whisker sensitive region of the primary somatosensory cortex in the awake mouse , we develop a data-driven framework that predicts the trial-by-trial variability in sensory-evoked responses in cortex by classifying ongoing activity into discrete states that are associated with particular patterns of response ., The classifier takes as inputs features of network activity that are known to be predictive of single-trial response from previous studies 9 , 14 , as well as more complex spatial combinations of such features across cortical layers , to generate ongoing discrete classifications of cortical state ., We optimize the performance of this state classifier by systematically varying the selection of predictors ., Finally , embedding this classification of state in a state-aware ideal observer analysis of the detectability of the sensory-evoked responses , we analyze a downstream readout that changes its detection criterion as a function of the current state ., We find that state-aware observers outperform state-blind observers and , further , that they equalize the detection accuracy across states ., Downstream networks in the brain could use such an adaptive strategy to support robust sensory detection despite ongoing fluctuations in sensory responsiveness during changes in brain state ., The foundation upon which the state-aware observer is constructed is a prediction of the sensory-evoked cortical response ., This prediction is based on classifying elements of the ongoing , pre-stimulus activity into discrete “states , ” and the goal is to find the features of ongoing activity and the classification rules that generate the best prediction of sensory-evoked responses ., Treating this as a discrete problem was a methodological choice motivated by the rationale that such an approach could find rules that are not linear in the features of ongoing activity and could lend more flexibility in the rules relating features of ongoing activity to variability in the response ., The features of ongoing activity include the power spectrum of pre-stimulus LFP and the instantaneous “LFP activation” ( Fig 2A ) ., To describe sensory-evoked responses , we define a parameterization of the LFP response using principal components analysis ( Fig 2B ) ., The state classifier is a function that takes as inputs features of pre-stimulus LFP and produces an estimate of the principal component ( PC ) weights and thus of the single-trial evoked response ( Fig 2C ) ., In the following sections , we describe this process in detail ., Next , within the general class of pre-stimulus features considered–power ratio and LFP activation–we optimized several choices: the range of frequencies used to compute the power ratio; the cortical depth from which the ongoing LFP signal is taken; and possible combinations of LFP signals across the cortical depth ., Changes in pre-stimulus features resulted in changes in the boundaries between states , and ultimately in changes in prediction performance ., First , we varied the bounds of the low-frequency range ( “L range” , Fig 3A ) ., The increase in fVE was on average 0 . 09 ± 0 . 05 ( N = 11 recordings ) ( Fig 3B; classifier boundaries shown in S2 Fig ) , with a significant increase in 10 of 11 recordings ( Fig 3C , asterisks ) ., We found that the optimal L range could extend to frequencies up to 40 Hz ( Fig 3C ) , with the median bounds of the optimal L being from 1 to 27 Hz ., Using for each recording the power ratio based on the optimized range of low-frequency power ( Fig 3 ) , we next determined where along the cortical depth the most predictive activity was and whether taking spatial combinations of LFP activity could improve the prediction ., Note that in this analysis , the channel for the stimulus-evoked response was held fixed ( L4 ) and thus the parameterization of the evoked response using principal components did not change , but the pre-stimulus channel was varied ., For each recording , we thus built a series of classifiers , using single- and multi-channel LFP activity from across the array ( Fig 4A , S3 Fig ) , which again were optimized for prediction of the single-trial L4 sensory-evoked response ., Classifiers built from a single channel of LFP performed best when the channel was near L4 ( Fig 4B , single example; Fig 4C , average profile ) ., Because the LFP represents a volume-conducted signal , we also examined the current source density ( CSD ) 34–36 , estimated on single trials using the kernel method 37 ., There was no improvement in fVE using CSD to build classifiers ( fVE difference , CSD minus LFP: -0 . 07; range: ( -0 . 12 , -0 . 01 ) ) ., For each recording , we defined an optimal classifier channel based on the spatial profile of fVE for single-channel predictors ( Fig 4B; S3 Fig ) ., In the “pair” combination , we paired the optimal classifier channel with each of the other possible 31 channels ( Fig 4B; green dashed line ) ., We optimized the classifier in the 3-dimensional space defined by power ratio ( on the optimal channel only ) and LFP activation from each of the two channels and compared the fVE to that obtained using the optimal classifier channel only ( Fig 4D ) ., We found no improvement in the prediction using the pair combination compared to using the optimal channel alone ( Fig 4D , mean fVE difference: 0 . 00 ± 0 . 01; 0/11 recordings with significant change , pair vs . single ) or using more complex combinations of channels ( S3 Fig ) ., To summarize , we optimized classifiers based on pre-stimulus features to predict single-trial sensory-evoked LFP responses in S1 cortex of awake mice ., We found that the classifier performance was improved by changing the definition of the power ratio ( L/W ) such that the low-frequency range ( L ) extended from 1 Hz to 27 Hz , depending on the recording , which differed from the range typically used from anesthetized recordings in S1 ( 1–5 Hz ) 8 , 9 ., We also found that the most predictive pre-stimulus LFP activation was near layer 4 ., After establishing a clear enhanced prediction of the single-trial stimulus-evoked response within the LFP by considering the pre-stimulus activity , we investigated the impact of this relationship on the detection of sensory stimuli from cortical LFP activity using a state-aware ideal observer analysis ., We first considered a simple matched-filter detection scheme 38 in which the ideal observer operated by comparing single-trial evoked responses to the typical shape of the sensory evoked response ( Methods , Detection ) ., The matched filter was defined by the trial-average evoked LFP response , and this filtered the raw LFP ( Fig 5A ) to generate the LFP score ( Fig 5B ) ., For the state-blind observer , a detected event was defined as a peak in the LFP score that exceeded a fixed threshold ( Fig 5B , stars ) ., The LFP score distributions from time periods occurring during known stimulus-evoked responses and from the full spontaneous trace were clearly distinct but overlapping ( Fig 5C ) , and detected events ( Fig 5B , stars ) included both “hits” ( detection of a true sensory input ) and “false alarms” ( detection of a spontaneous fluctuation as a sensory input ) ., Next , using the state classifier constructed in the first half of the paper , we analyzed the performance of a state-aware observer on a reserved set of trials , separate from those used for fitting and optimizing the state classifiers ( Methods ) ., Specifically , using the optimized state classifier ( Figs 3 and 4 ) , we continuously classified “state” at each time point in the recording ( Fig 5D ) ., The state-aware observer detects events exceeding a threshold , which changed as a function of the current state ( Fig 5E ) ., Instead of a single LFP score distribution , we now have one for each predicted state ( Fig 5F ) , leading to many possible strategies for setting the thresholds for detecting events across states ., In general , the overall hit rate and false alarm rate will depend on hits and false alarms in each individual state ( Fig 6A and 6B for single example; S4 and S5 Figs show all recordings ) , as well as the overall fraction of time spent in each state ( Fig 6A , inset ) ., We walk through the analysis for a single example , selected as one of the clearest examples of how state-aware detection worked ., While this example recording shows a relatively large improvement , it is not the recording with the largest improvement , and , moreover , the corresponding plots for all recordings are shown in S4 and S5 Figs ., To compare between traditional ( state-blind ) and state-aware observers , we compared hit rates at a single false alarm rate , determined for each recording as the false alarm rate at which 80%-90% detection was achieved by a state-blind ideal observer ., To select thresholds for the state-aware observer , we systematically varied the thresholds in state 1 and state 3 , while adjusting the state-2 threshold such that average false alarm rate was held constant ., For each combination of thresholds , we computed the overall hit rate ( Fig 6C ) ., For the example recording highlighted in Fig 6 , the state-aware observer ( hit rate: 96% ) outperformed the traditional one ( hit rate: 90% ) ., This worked because the threshold in state 3 could be increased with very little decrease in the hit rate ( Fig 6B ) , and this substantially decreased the false alarm rate in state 3 ( Fig 6A ) ., Because the overall false alarm rate is fixed , this meant more false alarms could be tolerated in states 1 and, 2 . Consequently , thresholds in states 1 and 2 could be decreased , which increased their hit rates ., Across recordings , we found that the state-aware observer outperformed the state-blind observer in 9 of 11 recordings ( Fig 6D; S4 and S5 Figs ) ., Hit rates slightly but significantly increased from a baseline of 81% for the state-blind observer to 84% for state-aware detection , or an average change of +3 percentage points ( SE: 3%; signed-rank test , p < 0 . 01 , N = 11 ) ., The overall change in hit rate reflects both the fraction of time spent in each state ( some fixed feature of an individual mouse ) and the changes in state-dependent hit rates ., To separate these factors , we analyzed the hit rate of the state-blind and state-aware observers by computing , for each observer , the hit rate conditioned on each pre-stimulus state ( Fig 6E ) ., For this recording , the state-blind observer had very low hit rate in state 1 and high hit rates in states 2 and, 3 . In comparison , hit rates were similar across the three state for the state-aware observer ( Fig 6D ) ., Thus , in state 1 ( smallest responses , blue ) , we observed a large increase in the hit rate depending on whether the observer used state-blind or state-aware thresholds ., Averaged across all recordings , the state-1 hit rates increased from 60% to 76% , which is a relative increase of 26% ( SE 11% ) ., Because this is weighted by the fraction of time spent in state 1 , the overall impact on the hit rate is smaller ., Hit rates increased slightly on average in state 2 ( + 2% , SE 4% ) and decreased slightly in state 3 ( -7% , SE 9% ) ., The net impact of this is that across the majority of recordings , the cross-state range of hit rates for the state-blind ideal observer was much larger than that for the state-aware ideal observer ( Fig 6D and 6F; 19% , average state-blind minus state-aware hit rate range in percentage points ( SE: 5% ) ; p < 0 . 01 , signed-rank test , N = 11 ) ., Thus , while the overall differences between state-aware and state-blind hit rates are modest , the state-aware observer has more consistent performance across all pre-stimulus states than a state-blind observer ., Due to the rapid development of tools that enable increasingly precise electrophysiology in the awake animal , there is a growing appreciation that the “awake brain state” encompasses a wide range of different states of ongoing cortical activity , and that this has a large potential impact on sensory representations during behavior 39–44 ., Here , we constructed a framework for the prediction of highly variable , single-trial sensory-evoked responses in the awake mouse based on a data-driven classification of state from ongoing cortical activity ., In related work , past studies have used some combination of LFP/MUA features to predict future evoked MUA response 9 , 14 ., We used a similar approach for state classification and response prediction in cortical recordings in the awake animal , extending this to allow complex combinations of ongoing activity in space and different features of the pre-stimulus power spectrum as predictors ., We found that simple features of pre-stimulus activity sufficed to enable state classification that yielded single-trial prediction of sensory evoked responses ., These predictive features were analogous to the synchronization and phase variables found in previous studies 8 , 9 , 14 , though we found a revised definition of synchronization was more predictive ., In particular , we found that the very low-frequency band of the LFP power spectrum ( 1–5 Hz ) was less predictive of single-trial evoked responses in our recordings than a wider band ( e . g . 1 to 27 Hz ) ., This is consistent with findings from a recent study 40 that surveyed the power spectrum of LFP across different behavioral states in the awake animal and demonstrated differences in the power spectrum between quiet and active wakefulness up to 20 Hz ., While we have focused on the problem of state classification and prediction from the perspective of an internal observer utilizing neural activity alone , future work could investigate whether the state classifier is also tracking external markers of changes in state , such as those indicated by changes in pupil diameter 42 , 45 , whisking 40 , or other behavioral markers in the awake animal ., We fit classifiers for each individual recording rather than pooling responses across animals and recording sessions ., The structure of the classification rules was similar across recordings , showing that the relationship between pre-stimulus features and evoked responses is robust ., This suggests that a single classifier could be fit , once inputs and outputs are normalized to standard values ., This normalization could be accomplished by determining the typical magnitude of LFP sensory responses and rescaling accordingly ., Moreover , the ordered structure of the classification rules suggests that a continuous model of state , rather than a discrete model , would have worked as well ., To implement as a continuous model , one would fit a regression of the evoked response coefficients using as independent variables LFP activation and power ratio ., Judging by the classification boundaries shown in S1 and S2 Figs , keeping only linear terms in activation and power ratio would give a good prediction ., In its current formulation , this framework utilizes only the features of ongoing cortical activity that are reflected in the LFP in order to classify state and predict the evoked LFP response ., Both as features underlying the state classifier and as the sensory-evoked response being predicted , LFP must be interpreted carefully , as the details of how underlying sinks and sources combine depend on the local anatomy and population spiking responses 46 ., In barrel cortex , the early whisker-evoked LFP response ( 0 to 25 ms ) is characterized by a current sink in L4 initially driven by thalamic inputs to cortex , but also reflecting cortical sources of activity: the evoked LFP is highly correlated with the layer-4 multi-unit activity response 47 , 48 ., We restricted our predictive framework to the high degree of variability in this initial response ., It remains to determine how LFP response variability is reflected in the sensory-evoked single-unit cortical spiking activity patterns ., Further , regarding LFP as a predictor used by the state classifier , LFP is a mesoscopic marker of cortical state that neglects finer details of cortical state organization ., In addition to establishing whether better predictions are made from more detailed representations of cortical state , it is an interesting question how microcircuit spiking dynamics are related to the mesoscopic markers of cortical state , or how much can be inferred about population spiking dynamics from the LFP ., Finally , thalamic and cortical activity are tightly linked , and the results presented here may also reflect variations in ongoing thalamic activity ., Disentangling thalamic and cortical sources of variability in the evoked response will require paired recordings and perturbative experimental approaches designed to address issues of causality ., In the second part of the paper , we used ideal observer analysis to show that state-aware observers , with oracle knowledge of the spontaneous , ongoing state fluctuations informative of the single-trial sensory-evoked response , can out-perform a state-blind ideal observer ., Our analysis relied on classification of the markers of ongoing state ., This is not to suggest that this specific estimation takes place in the brain , but instead could potentially be achieved dynamically by a downstream network through the biophysical properties of the circuitry ., Theoretically , the gain and threshold of such a readout neuron or network could be dynamically modified on the basis of the ongoing activity as a biophysical manifestation of the adaptive state-aware ideal observer , though the identification of specific mechanisms was beyond the scope of the current study ., We found that the state-aware observer had higher accuracy than the traditional , state-blind observer , but the absolute gain in hit rate ( at fixed false alarm rate ) averaged across all states was modest ., When pre-stimulus states were analyzed separately , however , we found that accuracy in the low-response state was substantially higher for the state-aware observer , where there was a relative increase of 25% in the hit rate for this state ., Because small sensory responses are predictable from the ongoing activity , transiently lowering the threshold for detection resulted in more “hits” in the low-response state , while false alarms in high-response states could be avoided by raising the threshold when the state changed ., However , the cortical activity was classified to be in this particular state approximately 20% of the time , and thus had a relatively modest impact on the overall performance , averaged across all states ., What is not currently known is the overall statistics associated with the state transitions ( i . e . distribution of time spent in each state , rate of transitions , etc . ) during engagement within perceptual tasks , but in any case , what we observe here is a normalization of detectability across brain states ., For near-threshold sensory perception , the signal detection theory framework asserts that single-trial responses are predictive of perceptual report 28 ., While there are many previous studies that seem to support this 49–52 , several animal studies have called this into question , showing that primary sensory neural activity does not necessarily co-vary with perceptual report on simple detection tasks 23 , 25 , 27 ., It is possible that the conflicting findings in the literature are due to behavioral state effects , and that more consistent reports would emerge if the analysis of the neural activity incorporated elements of the state-classification approach developed here ., Our results show how single-trial response size can be decoupled from perception , if a downstream network predicts and then accounts for the variability in sensory responses ., Moreover , our analysis showed that some states of pre-stimulus activity should be associated with higher or lower performance on a near-threshold detection task , which has been observed in near-threshold detection studies in the rodent 26 and monkey 24 ., It should be noted that there is controversy regarding the relevance of primary sensory cortex in simple behavioral tasks 53 , 54 , but this is likely related to the task difficulty 55 , where a large body of literature has resolutely shown that processing in primary cortical areas is critical for difficult tasks that increase cognitive load , and we suspect that near threshold stimuli such as those shown here fall in that category ., Many studies have demonstrated a link between pre-stimulus cortical activity and perceptual report on near-threshold detection tasks in humans 17 , 18 , 56–59 ., Currently , it is not entirely clear how far the parallel in cortical dynamics between the mouse and human can be taken ., One challenge is that connecting invasive recordings in the mouse to non-invasive recordings in human studies is non-trivial ., Here , at the level of LFP , we observed similarities between species in the interaction between ongoing and evoked activity: the largest evoked responses tended to be preceded by positive deflection in the LFP , and the smallest evoked responses were preceded by negative deflection in the LFP ., This relationship , the negative interaction phenomenon , points to a non-additive interaction between ongoing and evoked activity and is also observed in both invasive and non-invasive recordings in humans 33 , 56 , 60 , 61 ., Establishing parallels between cortical dynamics on a well-defined task , such as sensory detection , between humans and animal models is an important direction for future studies ., In summary , we have developed a framework for the prediction of variable single-trial sensory-evoked responses and shown that this prediction , based on cortical state classification , can be used to enhance the readout of sensory inputs ., Utilizing state-dependent decoders for brain-machine interfaces has been shown to greatly improve the readout of motor commands from cortical activity 62 , 63 , at the very end-stage of cortical processing ., Others have raised the possibility of using state knowledge to ‘cancel out’ variability in sensory brain-machine interfaces , with the idea that this could generate a more reliable and well-controlled cortical response 64 , 65 , which would in theory transmit information more reliably ., This is intriguing , though our analysis suggests a slightly different interpretation: if downstream circuits also have some knowledge of state , canceling out encoding variability may not be the appropriate goal ., Instead , the challenge is to target the response regime for each state ., This could be particularly relevant if structures controlling state , including thalamus 66 , are upstream of the cortical area in which sensory BMI stimulation occurs ., The simple extension of signal detection theory we explored suggests a solution to the problem that the brain faces at each stage of processing: how to adaptively read out a signal from a dynamical system constantly generating its own internal activity ., All procedures were approved by the Institutional Animal Care and Use Committee at the Georgia Institute of Technology ( Protocol Number A16104 ) and were in agreement with guidelines established by the National Institutes of Health ., Six nine to twenty-six week old male C57BL/6J mice were used in this study ., Mice were maintained under 1–2% isoflurane anesthesia while being implanted with a custom-made head-holder and a recording chamber ., The location of the barrel column targeted for recording was functionally identified through intrinsic signal optical imaging ( ISOI ) under 0 . 5–1% isoflurane anesthesia ., Recordings were targeted to B1 , B2 , C1 , C2 , and D2 barrel columns ., Mice were habituated to head fixation , paw restraint and whisker stimulation for 3–7 days before proceeding to electrophysiological recordings ., Following termination of the recordings , animals were anesthetized ( isoflurane , 4–5% , for induction , followed by a euthanasia cocktail injection ) and perfused ., Local field potential was recorded using silicon probes ( A1x32-5mm-25-177 , NeuroNexus , USA ) with 32 recording sites along a single shank covering 775 μm in depth ., The probe was coated with DiI ( 1 , 1’-dioctadecyl-3 , 3 , 3′3’-tetramethylindocarbocyanine perchlorate , Invitrogen , USA ) for post hoc identification of the recording site ., The probe contacts were coated with a PEDOT polymer 67 to increase signal-to-noise ratio ., Contact impedance measured between 0 . 3 MOhm and 0 . 7 MOhm ., The probe was inserted with a 35° angle relative to the vertical , until a depth of about 1000 μm ., Continuous signals were acquired using a Cerebus acquisition system ( Blackrock Microsystems , USA ) ., Signals were amplified , filtered between 0 . 3 Hz and 7 . 5 kHz and digitized at 30 kHz ., Mechanical stimulation was delivered to a single contralateral whisker corresponding to the barrel column identified through ISOI using a galvo motor ( Cambridge Technologies , USA ) ., The galvo motor was controlled with millisecond precision using a custom software written in Matlab ( Mathworks , USA ) ., The whisker stimulus followed a sawtooth waveform ( 16 ms duration ) of various velocities ( 1000 deg/s , 500 deg/s , 250 deg/s , 100 deg/s ) delivered in the caudo-rostral direction ., To generate stimuli of different velocity , the amplitude of the stimulus was changed while its duration remained fixed ., Whisker stimuli of different velocities were randomly presented in blocks of 21 stimuli , with a pseudo-random inter-stimulus interval of 2 to 3 seconds and an inter-block interval of a minimum of 20 seconds ., The total number of whisker stimuli across all velocities presented during a recording session ranged from 196 to 616 stimuli ., For analysis , the LFP was down-sampled to 2 kHz ., The LFP signal entering the processing pipeline is raw , with no filtering beyond the anti-aliasing filters used at acquisition , enabling future use of these methods for real-time control ., Prior to the analysis , signal quality on each channel was verified ., We analyzed the power spectrum of LFP recorded on each channel for line noise at 60 Hz ., In some cases , line noise could be mitigated by fitting the phase and amplitude of a 60-Hz sinusoid , as well as harmonics up to 300 Hz , over a 500-ms period in the pre-stimulus epoch , then extrapolating the sinusoid over the stimulus window and subtracting ., A small number of channels displayed slow , irregular drift ( 2 or 3 of 32 channels ) and these were discarded ., All other channels were used ., Current source density ( CSD ) analysis was used for two different purposes: first , to functionally determine layers based on the average stimulus-evoked response , and second , to analyze the pre-stimulus activity ( in single trials ) to localize sinks and sources generating the predictive signal ., We describe the general method used here ., Prior to computing the current source density ( CSD ) , each channel was scaled by its standard deviation to normalize impedance variation between electrodes ., We then implemented the kernel CSD method 37 to compute CSD on single trials ., This method was chosen because it accommodates irregular spacings between electrodes , which occurs when recordings on a particular contact do not meet quality standards outlined above ., To determine the best values for the kernel method parameters ( regularization parameter , λ; source extent in x-y plane , r; and source extent in z-plane , R ) we followed the suggestion of Potworowski ( 2012 ) and selected the parameter choices that minimize error in the reconstruction of LFP from the CSD ., These parameters were similar across recordings , so for all recordings we used: λ = 0 . 0316; r = 200μm; R = 37 . 5μm ., The trial-averaged evoked response was computed on each trial by subtracting the pre-stimulus baseline ( average over 200 ms prior to stimulus delivery ) and computing the average across trials ., The CSD of this response profile was computed as described above ., The center of layer 4 was determined by finding the largest peak of the trial-averaged evoked LFP response as well as the location of the first , large sink in the trial-averaged sensory-evoked CSD response ., We assume a width of 205 μm for layer 4 , based on published values for mice 32 ., The matched filter ideal observer analysis 38 is implemented as follows ., The score s, ( t ) is constructed by taking the dot product of the evoked responses yt with a filter matched to the average evoked response:, s, ( t ) =yt∙ξ0, This is equivalent to computing the sum, s, ( t ) =∑t=1Nξ ( x ( t+t ) -x, ( t ) ) ξ ( t ), In the standard encoding model , if η is zero-mean white noise , this gives a signal distribution, P, ( s ) ~N ( ∥ξ0∥ , σ2 ), where σ2=∥ξ0∥2ση2 and a noise distribution with mean 0 ., In practice , we do not parameterize the distribution , because η is not uncorrelated white noise , and work from the score distribution directly ., For the state-aware decoder , we use the prediction α^t , k of evoked responses, yt=ξ0+∑k=1NCα^t , kξk+η, This changes the score to, s, ( t ) =|ξ0|2+∑kα^t , kξk∙ξ0+η∙ξ0, Typically , one of the first two PCs ( ξ1 or ξ2 ) has a very similar shape to ξ0 , while the other one has both positive and negative components ( Fig 2 , S1 and S2 Figs ) ., For the state-aware threshold , we use state predictions for the component that is more similar to ξ0 , as indicated in S1 and S2 Figs ., An event is detected at time t for threshold θ when s, ( t ) > θ is a local maximum that is separated from the nearest peak by at least 15 ms and has a minimum prominence ( i . e . drop in s before encountering another peak that was higher than the original peak ) of |ξ
Cortical responses to sensory inputs vary across repeated presentations of identical stimuli , but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood ., Using multi-channel local field potential ( LFP ) recordings in primary somatosensory cortex ( S1 ) of the awake mouse , we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses , based on features of the spontaneous , ongoing LFP recorded across cortical layers ., Our findings show that , by utilizing an ongoing prediction of the sensory response generated by this state classifier , an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain , which could have implications for variability in the performance of detection tasks across brain states .
Establishing the link between neural activity and behavior is a central goal of neuroscience ., One context in which to examine this link is in a sensory detection task , in which an animal is trained to report the presence of a barely perceptible sensory stimulus ., In such tasks , both sensory responses in the brain and behavioral responses are highly variable ., A simple hypothesis , originating in signal detection theory , is that perceived inputs generate neural activity that cross some threshold for detection ., According to this hypothesis , sensory response variability would predict behavioral variability , but previous studies have not born out this prediction ., Further complicating the picture , sensory response variability is partially dependent on the ongoing state of cortical activity , and we wondered whether this could resolve the mismatch between response variability and behavioral variability ., Here , we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs ., This observer has higher overall accuracy than the standard ideal observer ., Moreover , because of the adaptation , the observer breaks the direct link between neural and behavioral variability , which could resolve discrepancies arising in past studies ., We suggest new experiments to test our theory .
single channel recording, medicine and health sciences, engineering and technology, statistics, somatosensory cortex, signal processing, matched filters, brain, social sciences, neuroscience, signal filtering, multivariate analysis, animal anatomy, mathematics, membrane electrophysiology, bioassays and physiological analysis, zoology, research and analysis methods, mathematical and statistical techniques, principal component analysis, signal detection theory, electrophysiological techniques, animal physiology, psychology, anatomy, vibrissae, biology and life sciences, sensory perception, physical sciences, statistical methods