An Emergent Neural Coactivity Code For Dynamic Memory

Contact: joanna.jia@wecistanche.com / WhatsApp: 008618081934791

Abstract

Neural correlates of external variables provide potential internal codes that guide an animal’s behavior. Notably, first-order features of neural activity, such as single-neuron firing rates, have been implicated in encoding information. However, the extent to which higher-order features, such as multi-neuron coactivity, play primary roles in encoding information or secondary roles in supporting single-neuron codes remains unclear. Here we show that millisecond-timescale coactivity amongst hippocampal CA1 neurons discriminates distinct millisecond-lived behavioral contingencies. This contingency discrimination was unrelated to the tuning of individual neurons but instead an emergent property of their coactivity. Contingency discriminating patterns were reactivated offline after learning and their reinstatement predicted trial-by-trial memory performance. Moreover, optogenetic suppression of inputs from the upstream CA3 region selectively during learning impaired coactivity-based contingency information in CA1 and subsequent dynamic memory retrieval. These findings identify coactivity as a primary feature of neural firing that discriminates distinct behaviourally-relevant variables and supports memory retrieval.

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What features of neural activity does the brain use to encode information about the external world? Ample evidence suggests that the firing rates1,2 and temporal tuning properties3,4 of individual neurons show robust correlations with external variables. These first-order features of neural activity could serve as neural codes that are read by downstream structures to subsequently guide behaviour5. Moreover, advances in vivo multi-unit recordings have allowed a further appreciation for the role of neuronal population dynamics in supporting internal representations-, The timescale at which population activity is organized may be critical. In particular, coincidental spiking at the timescale of a neuron's membrane time constant (~10-30 ms for cortical neurons') effectively drives downstream receiver neurons5,10, can be parsed within network oscillations that pace firing of neuronal populations and can be rapidly stabilized through spike-timing-dependent plasticity (STDP)l1,2. Indeed, millisecond-timescale coactivity is a hallmark of some neural codes5-15. Such short-timescale coactivity organizes the firing of neurons with related tuning to external variables, giving rise to robust, population-based representations that are congruent with those of their participating neuronsl4,16,17. Moreover, millisecond timescale coactivity could also play a primary role in encoding information. That is, groups of neurons may encode a variable as a function of their joint activity regardless of whether individually each neuron is tuned to this variable. While this type of emergent, coactivity-based coding has been described for physically well-defined variables such as specific sensory inputs and actions-20, its possible cognitive function has not been explored.

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Given the potential for rapid stabilization and retrieval of neural codes based on millisecond-timescale coactivity, such codes may support behavioral performance when animals must rapidly learn and flexibly retrieve salient information-a process we refer to here as "dynamic memory". Converging evidence suggests a prominent role of the hippocampus for such rapid and flexible learning21-23, supporting models that frame the hippocampus as a fast learning system24. Moreover, neural activity in the hippocampus is organized into temporally precise coactivity pattrns15,225. We, therefore, hypothesized that millisecond-timescale coactivity patterns in the hippocampus serve a primary role in encoding

behaviourally-relevant information supporting dynamic memory, To test this hypothesis. we developed a one-day, two-contingency discrimination task that we combined with multi-unit recording of hippocampal CAl neurons and causal optogenetic manipulation of intra-hippocampal synapses. Our findings demonstrate a role for emergent coactivity-based representations in encoding contingency information and supporting dynamic memory retrieval.

1Medical Research Council Brain Network Dynamics Unit, Nuffield Department of ClinicalNeurosciences, University of Oxford, Oxford, OX1 3TH, United Kingdom

2Physiological Laboratory, Department of Physiology Development and Neuroscience, University of Cambridge, Cambridge, CB2 3EG, United Kingdom

3Department of Pharmacology, University of Oxford, Oxford, OX1 3TH, United Kingdom

Results

Mice learn and dynamically retrieve two new behavioral contingencies every day

We first established a one-day behavioral paradigm that recruits dynamic memory(Fig.1). Mice were initially pre-trained to collect a transiently available(5-second) drop of sucrose from a liquid dispenser after the presentation of an auditory cue (pre-training phase l; Extended Data Fig. 1). Subsequently, animals experienced a novel learning enclosure every day, which was defined by a new spatial topology, two new sets of wall-mounted LED displays, and two newly positioned dispensers (pre-training phase 2; Extended Data Fig.1). In this learning enclosure, animals encountered the following rule: immediately after tone presentation, one dispenser delivers a drop of sucrose solution whereas the other simultaneously delivers a bitter(quinine)solution; both drops are transiently available. Importantly, the dispenser-solution pairing was contingent on which of the two sets of LED cues is illuminated concurrently with the tone(Fig. la,b). When animals reached an average of 80% performance in this pre-training phase, we then started the training phase, which included three stages every day (Fig. Ic).In the first stage, animals explored the new learning enclosure in two sessions, each with one of the two LED sets continuously illuminated but without tone presentation or drop delivery, as well as another exploration session in a control(task-unrelated) enclosure ("Exploration" stage; Fig, lc and Extended Data Fig.2a). In the second stage, animals learned to associate each LED set with the tone-triggered delivery of a selective drop outcome at each dispenser over four sessions alternating between active LEDs ("Learming'stage; Fig. lc).

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We refer to these associations as LED-defined behavioral "contingencies"(Xand Y, Fig, la), with animals learning two new contingencies every day (extended Data Fig. 2b). During learning, mice rapidly developed a successful approach response to the correct (sucrose) dispenser over the incorrect(quinine)dispenser in each contingency(Fig. ld,e and Extended Data Fig. 2b).In the final stage conducted at the end of each day (one hour after the end of learning), memory for the newly-learned contingencies was tested in a probe session where the tone was presented without drop delivery while pseudo-randomly switching between the two LED sets("Probe" stage; Fig.1c).In these probe trials, mice continued to identify the correct dispenser (Fig, 1f, and Extended Data Fig.2c). Memory performance on a given day was unrelated to that on the previous day(Extended Data Fig.3a) and held when averaging across all probe performances for each individual mouse(Extended Data Fig.3b).

Furthermore, while animals made more mistakes on the first probe trial following a switch in LEDs compared to the other trials(Extended Data Fig. 3c), there was no deterioration of performance as the probe session progressed (Extended Data Fig 3d). Thus, mice successfully learned to discriminate two new behavioral contingencies each day and flexibly retrieved a memory of this discrimination, providing a paradigm to study the neural substrates of dynamic memory.

Emergent millisecond-timescale coactivity discrimination of behavioral contingencies

To investigate whether an emergent coactivity code develops in our task, we monitored hippocampal CAl neuronal ensembles during training days. We first trained a Bayesian classifier to decode the prevailing contingency on a trial-by-trial basis from both average firing rates of principal neurons and short-timescale (25-ms)pairwise temporal correlations between neuronal spike trains. Shuffling temporal correlations across trials, while preserving trial-by-trial average firing rates, significantly impaired decoding of ongoing contingency (Fig.2a; Extended data Fig.4a). Moreover, contingency information in temporal correlations alone was drastically impaired when shifting spikes to destroy short-timescale coactivity while maintaining correlations due to slow fluctuations of population firing rate in each trial(Fig. 2b). Short-timescale correlations also had significantly explained variance for task contingencies(Fig. 2c). These results indicated the presence of contingency-related information in short-timescale coactivity beyond the information in single-neuron firing rates.

To investigate the task relevance of contingency-related coactivity, we isolated coactivity patterns nested within 25-ms time-windows26separately in each contingency within the learning enclosure. We represented each pattern by a weight vector containing the contribution of each neuron to the coactivity underpinning that pattern(Fig.2d). These coactivity patterns differed from those extracted in the control enclosure(Fig.2e,f), showing their spatial context-selective expression. In addition, some learning enclosure patterns discriminated the two contingencies, being selective to either Xor Y(Fig.2e,f; Extended data Fig. 4b; orange).To investigate the functional significance of such patterns, we compared them to a matched group of learning enclosure patterns with high between-contingency similarity(Fig. 2e,f Extended Data Fig.4b; blue). We refer to these as contingency-discriminating and contingency-invariant coactivity patterns, respectively. Neurons that contributed the most to a given pattern are henceforth referred to as"members" of that pattern (see Methods).

We confirmed that for members of the same contingency discriminating, but not invariant, the pattern was more correlated in one contingency than the other(Fig. 3a). Importantly, however, members of contingency-discriminating patterns were not individually contingency selective (Fig. 3b; Extended Data Fig. 4c), regardless of the membership threshold used (Extended Data Fig.4d,e); and are hence separable from previously reported contextually modulated neurons2-29. Moreover, such coactivity-based contingency discrimination was not explained by differences in temporal firing properties of individual member neurons between contingencies(Fig. 3c-e). Furthermore,contingency-discriminating pattern members were not tuned to the goal (Fig.3fg)and hence did not report trajectories to goal 30. We also noted no differences in the participation of neurons along the transverse axis of the CAl to contingency-discriminating and invariant patterns(52.1% and 48.2%of pattern member neurons found in proximal and distal CAl, respectively; Fisher's exact test: odds ratio=1.17; P=0.55), no segregation by hemisphere (Extended Data Fig.4f) and no differences in the participation of neurons from the deep or superficial CAl pyramidal sublayer to contingency-discriminating compared to contingency-invariant patterns(39.9%and 28.4% of pattern member neurons, respectively; Fisher's exact test: odds ratio=1.44; P-0.20). However, we observed a trend towards contingency-discriminating coactivity pattern members firing at earlier theta phases compared to members of contingency invariant patterns(Extended data Fig.4g). Overall, these findings identify an emergent,short-timescale neural coactivity-based discrimination of behavioral contingencies in the hippocampal CA1.

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We next asked whether contingency-discriminating coactivity patterns relate to contingency learning, When we tracked the strength of each pattern (Fig.2d)over time, we found that contingency-invariant patterns began increasing in strength during the initial exploration of the new leaming enclosure on each day, before animals experienced task contingencies, their strength further increased and subsequently plateaued during learning(Fig. 4a; Extended data Fig.5a,b). Conversely, contingency-discriminating coactivity was more stable during exploration but markedly increased during contingency learning(Fig. 4a; Extended data Fig. 5a, b). Pattern strengthening during learning reflected increased temporal correlations between members' activity rather than changes in their average firing rates(Extended data Fig, 5c,d). Furthermore, the cofiring of contingency-invariant pattern members increased during sharp-wave/ripples post-compared to pre-exploration sleep, and increased again in the sleep session after learning, while contingency-discriminating pattern members only increased their sharp-wave/ripple cofiring after contingency learning (Fig.4b,c). Thus, the distinction between contingency-invariant and contingency-discriminating pattern members was not equivalent to the difference between the previously described"rigid" and"plastic" cells31. Moreover, our findings did not simply reflect representations of rewarded/aversive locations2/since pattern strength was calculated outside dispenser locations, nor were they a simple reflection of the animal's differential behavior across the two contingencies(ie. heading towards a given dispenser, Extended Data Fig 5e). Importantly, the reinstatement of contingency-discriminating patterns during memory retrieval predicted trial-by-trial performance; these patterns were stronger before correct, compared to incorrect, behavioral responses to tone presentation(Fig. 4d; Extended Data Fig. 5f). This contingency-selective and performance-related reinstatement of CAl coactivity was not associated with a firing rate bias of member neurons nor animal running speed (Extended Data Fig. 5gh) and was notably absent when animals performed at the chance(i.e., when correct and incorrect trials were equivalent; Extended Data Fig.5i).In contrast, the strength of contingency-invariant patterns was not related to trial-by-trial memory performance (Fig. 4d). Moreover, while there was contingency-related information in longer(1-second) timescale coactivity (Extended Data Fig.5j), the reinstatement of second-timescale contingency-discriminating coactivity during the probe session did not predict memory performance(Extended Data Fig, 5k). These findings show that CAl neuronal spiking is gradually organized during learning to form millisecond-timescale coactivity patterns representing newly-learned contingencies, which are subsequently reinstated on a trial-by-trial basis during dynamic memory retrieval.

Distinct spatial tuning of contingency-discriminating and invariant coactivity patterns

During the exploration of a novel environment, CAl neurons with overlapping place fields can form spatially tuned coactivity patterns,17,32. To investigate the spatial tuning of coactivity patterns during contingency learning, we computed for each detected pattern the spatial map corresponding to the time-course of its activation strength, as well as the individual firing rate maps of each of its member neurons. Contingency-discriminating coactivity was markedly less spatially coherent than contingency-invariant coactivity (Fig.5a-d and Extended Data Fig.6). This was concomitant with the less spatially coherent firing of contingency-discriminating pattern members relative to their contingency-invariant counterparts (Extended Data Fig. Ta,b), with contingency-discriminating members also exhibiting a trend towards more place fields within a given session compared to contingency-invariant members(Extended Data Fig, 7c). Moreover, while members of a given contingency-invariant pattern had overlapping firing fields, members of a given contingency-discriminating pattern were markedly less spatially correlated (Fig. 5a,b,e; Extended Data Fig. 6). This weaker spatial overlap was observed regardless of the membership threshold used(Extended Data Fig. 7d,e) and was robust to differences in temporal correlation amongst members spike trains (Extended Data Fig. 7f. In addition, while contingency-invariant coactivity was spatially biased towards the place fields of their member neurons, this bias was significantly weaker for contingency-discriminating patterns (Extended Data Fig. 7g,h), This finding was corroborated by a separate analysis showing lower place field similarity of neuron pairs with high explained variance for contingency compared to neuron pairs with low explained variance (Extended Data Fig. 7i). Finally, we found no evidence that contingency discrimination by a given coactivity pattern reflects contingency-gated spatial remapping of its member neurons. In fact, the spatial map of an individual member of either pattern type was on average more similar across sessions of different contingencies than sessions of the same contingency (Fig. 5f,g; Extended Data Fig. 7j), even when matching the spatial coherence of contingency-discriminating pattern members to that of contingency-invariant counterparts(Extended Data Fig. 7k). Moreover, members of the same contingency-discriminating pattern were spatially correlated with each other across sessions of their preferred contingency as they were across sessions of opposite contingency(Extended Data Fig.7). Overall, these findings show that contingency-invariant coactivity provides robust place representations by binding spatially congruent neurons. In contrast,contingency-discriminating patterns stitch together neurons irrespective of their spatially correlated activity, giving rise to spatially discontiguous coactivity consistent with a specialization in representing ongoing behavioral contingency.

CA3-→CA1 inputs are necessary for contingency discriminating coactivity and dynamic memory retrieval

Finally, to address the functional role of contingency-discriminating coactivity, we sought to identify and manipulate a neural pathway necessary for their formation. CAl coactivity could rely on synaptic inputs from the recurrently-connected upstream hippocampal CA3 area33,34, and recent work suggests a critical mnemonic role of left CA3(CA3L) inputs to CA135,36. Accordingly, we transduced CA3-pyramidal neurons of Grik4-Cre mice with the yellow light-driven proton pump Archaerhodopsin-3.0(Fig.6a,b); bilateral implantation of tetrodes and optic fibers further allowed simultaneous monitoring of, and light delivery to, CAl ensembles. Light delivery targeting CA3-axons in CAl during learning markedly reduced the power of theta-nested slow-gamma, but not mid-gamma, oscillations in CAl (Fig.6c; Extended Data Fig. 8a-c), consistent with the suggestion that CAl slow-gamma oscillations report incoming CA3 inputs37,38. While suppressing CA3L→CAl inputs preserved both the organization of CAl neurons into coactivity patterns during learning and the reinstatement strength of such patterns during memory retrieval (Extended Data Fig. 8d,e), this intervention altered the information content of CAl coactivity. Firstly, the distribution of between-contingency pattern similarity and patten strength ratio was shifted towards contingency-invariance(Fig. 6d,e; Extended Data Fig 8f, Extended Data Fig.9). Secondly, this manipulation reduced the explained variance for contingencies in short-timescale pairwise correlations(Extended Data Fig. 8g). Thirdly, Bayesian decoding of contingency using such short-timescale coactivity was markedly impaired with CA3L→CAl input suppression(Extended Data Fig. 8h). At the behavioral level, suppressing CA3-→CAl inputs selectively during learning had no effect on ongoing performance (Extended Data Fig. 8i)but reduced memory performance to chance levels in the subsequent probe test one hour after, during which there was no input suppression (Fig. 6f). This latent memory impairment was seen when mice had to flexibly retrieve two contingencies(Fig.6f; Extended Data Fig. 8j-1), but not when retrieving only one contingency(Extended Data Fig. 8m). Moreover, flexible memory retrieval of the two contingencies was preserved after suppressing right CA3 inputs to CAl(Extended Data Fig 8n-u). Together, these findings show that short-timescale CAl coactivity-based contingency-information necessitates CA3L inputs and is required for dynamic retrieval of two-contingency memory.

Discussion

In this study, we report a coactivity-based hippocampal code for dynamic memory retrieval of short-lived behavioral contingencies. Encoding information is an emergent property of coactivity amongst multiple neurons(Extended Data Figure 10a) allows effective discrimination of newly encountered contingencies every day, without committing individual neurons to represent such short-lived cognitive variables. The emergent nature of this code points to short-timescale coactivity as a primary feature of neural activity that is used to encode information and guide cognition, rather than only playing secondary roles, such as organizing αr stabilizing single neuron rate-based codes. In particular, our findings show that millisecond-timescale coactivity is highly suited for mnemonic processing of short-lived information: it is rapidly formed and readily reinstated to support flexible memory retrieval. Millisecond-timescale neural coactivity may preferentially exhibit spike-timing-dependent plasticity (STDP)11,12,39 to rapidly stabilize the code in memory. In contrast, while second-timescale coactivity contained contingency information in our task, its reinstatement during dynamic memory retrieval was not predictive of trial-by-trial performance, Second-timescale coactivity may exhibit slower plasticity, and hence be more suited for the stable representation of long-lived contingencies.

Our findings also provide new insights into the role of correlated neural activity in guiding contextual behavior. Spatial remapping, where patterns of spatial correlations between hippocampal principal cells disambiguate distinct spatial contexts, has been proposed as a neural basis for contextual learning23.In this study, we observe that contingency discriminating coactivity is not a reflection of spatial remapping. Instead, our findings are consistent with the view that spatial remapping may be a specific instance of a more general phenomenon of "temporal remapping", in which the short-timescale temporal correlation structure of neurons differs across distinct contexts23. Indeed, in tasks where animals must disambiguate different spatial reference frames, millisecond timescale coactivity is a robust correlate of moment-by-moment behavioral discrimination of different contexts, both in networks that show spatial remapping2and those that do no25. This is also consistent with a reader-centric view of neural codes² since downstream reader/actuator neurons can detect temporal, but not spatial, correlations amongst their input neurons. Notably, one prediction from this coding scheme is that downstream receiver neurons "read" the incoming information, represented as an emergent property of the collective activity of multiple neurons, by disambiguating the relevant patterns of millisecond input coincidence from the myriad of other inputs they receive5. Such decoding may be implemented by a "reader"network40 or even a single "reader" neuron41(see also Extended Data Fig, 10b).

Our findings further establish that to "write" a millisecond coactivity code for learned contingencies in memory, CA3-→Cal inputs are necessary. Whether this is related to lateralization in information content, processing, and/or plasticity35,4 of CA3→CAl inputs remains to be investigated. Nevertheless, we show that distinct types of coactivity patterns show qualitatively distinct functional plasticity. While contingency-invariant patterns develop during exploration and are reactivated in sharp-wave/ripples during offline(sleep rest) periods following spatial exploration,contingency-discriminating patterns show robust increases in strength during learning and are reactivated offline in sharp-wave/ripples after contingency learning. Thus, both invariant and discriminating patterns show a signature of previously described"plastic" cells, albeit in different behavioral states. This is consistent with a division of labor amongst hippocampal coactivity patterns, with contingency-invariant patterns reflecting unsupervised learning about the spatial structure of the environment, and contingency-discriminating patterns supporting flexible memory-guided behavior. Altogether, our findings open new perspectives for future empirical and modeling studies to elucidate mechanisms for writing and reading coactivity-based information and to relate coding schemes across multiple timescales of population activity.

How can the code be written and read?

The hippocampus is embedded in a wider network of cortical and subcortical structures that may mediate or modulate the formation of the emergent coactivity code we describe here (writing)and its subsequent use by downstream neurons (reading)to select contingency-specific behavior. Below we outline hypotheses about possible mechanisms for both Writing and reading processes.

We show a necessity ofCA3--CAl inputs during learning for the expression of an emergent coactivity code for short-lived behavioral contingencies, which opens a window into the generative mechanisms at play. Left hemisphere originating CA3 inputs in mice exhibit more robust long-term plasticity35,42, including STDp2, and are preferentially involved in long-term memory compared to right CA3 inputs35,6. Such a difference in plasticity may provide part of the mechanism by which contingency-discriminating patterns are strengthened during learning. The dynamic memory task we assess here necessitates the rapid acquisition and stabilization of contingency information(within 30 trials in each contingency across~3 hours) as well as its rapid and flexible retrieval in the memory probe test (I hour after learning, with frequent, pseudorandom switches in contingency). Such rapid mnemonic processing may be preferentially coded by short-timescale coactivity since STDP mechanisms are more likely to rapidly stabilize neuronal co-firing within short(10s of ms)compared to that within longer(ls) windows1,239. Indeed, we show that short (25ms)but not longer(ls)timescale coactivity is reinstated to predict performance (Fig. 4d; Extended data Fig. 5k). However, it is also possible that other plasticity mechanisms are at play (including non-synaptic ones). While STDP might stabilize millisecond timescale coactivity patterns, what processes generate such contingency-discriminating coactivity in the first place? Neurons in the dentate gyrus, two synapses upstream of the CAl, have been implicated in pattern separation processes that may be necessary for contextual behaviour43 and can do so through differences in millisecond-timescale coactivity25. Moreover, there is evidence for a left dominance in the expression of the activity marker cFos in the dentate gyrus during novel object exploration. Importantly, while CA3 neurons in one hemisphere send commissural projections to the contralateral hemisphere33, the two hemispheres seem to retain functional differences in their projections to CA35,2 which we target directly.

Such lateralization could in part result from developmental lateralization of factors involved inactivity and plasticity45, which may be robust to any potential synchronizing effects of commissural projections. It is plausible therefore that a combination of lateralized processing of contextual information, starting as early as the pattern separation circuits of the dentate gyrus, and lateralized plasticity at CA3-CAl synapses contribute to the formation and stabilization of emergent contingency discriminating patterns in the CAl, respectively. Given that contingency discriminating patterns emerge during learning rather than spatial exploration (Fig. 4a), their formation is not simply a reflection of sensory differences between the two LED displays(which are also distinct during exploration sessions X0 and YO) but instead relates to the different reward contingencies the animal must learn to discriminate, Indeed, recent evidence suggests that neural discrimination of distinct spatial contexts in the CAl, but not dentate gyrus, is related to behavioral discrimination of these contexts46, suggesting an additional gating of behaviourally relevant environmental differences between the dentate gyrus and CA1. How such behavioral contingency information is conveyed to the CAl is currently unclear and may involve inputs from the prefrontal cortex*7. The finding described here, that an emergent coactivity code in hippocampal CAl is necessary for dynamic retrieval of contingency-discrimination, will motivate subsequent empirical and modeling studies that elucidate the cross-circuit interactions involved in generating such functional coactivity.

How is the hippocampal coactivity code for contingencies decoded by downstream neurons in executive and motor areas to elicit appropriate behaviors in each contingency? Cortical neurons have membrane time constants in the range of 10-30ms' meaning that convergent input from neurons coactive at the 25-ms timescale we investigate here can exhibit effective temporal summation in the downstream ("reader")neuron's dendrites and contribute to its spiking. Moreover, coincident synaptic activation within this time window is consistent with the initiation of active, voltage-gated conductances in dendrites, which allows their supra-linear summation48. This may also serve as a mechanism for disambiguating different patterns of coactivity by a single reader neuron, where inputs that are preferentially spatially clustered on individual dendrites will be more likely to elicit such non-linearities than more dispersed inputs4, even when the mean synaptic weights of such inputs are indistinguishable(Extended Data Figure 10b). This would allow selective reading of emergent coactivity, as readers would not disambiguate the firing of individual members of contingency discriminating coactivity patterns, only their synchronous activity. Other single-neuron and network-based coactivity reading mechanisms have also been suggested1041. For all of these cases, the fast(10s of ms)nature of this code should allow rapid processing of contingency information supporting rapid behavioral responses in dynamically changing environments. These outlined candidate mechanisms by which emergent coactivity codes could be read by downstream circuits may be tested in future ex viva in Vivo and in silico studies.