ABSTRACT
The ability of a neuronal population to effectuate response decorrelation has been identified as an essential prelude to efficient neural encoding. To what extent are diverse forms of local and afferent heterogeneities essential in accomplishing such response decorrelation in the dentate gyrus (DG)? Here, we incrementally incorporated four distinct forms of biological heterogeneities into conductance-based network models of the DG and systematically delineate their relative contributions to response decorrelation. We incorporated intrinsic heterogeneities by stochastically generating several electrophysiologically-validated basket and granule cell models that exhibited significant parametric variability, and introduced synaptic heterogeneities through randomized local synaptic strengths. In including adult neurogenesis, we subjected the valid model populations to randomized structural plasticity and matched neuronal excitability to electrophysiological data. We assessed networks comprising different combinations of these three local heterogeneities with identical or heterogeneous afferent inputs from the entorhinal cortex. We found that the three forms of local heterogeneities were independently and synergistically capable of mediating significant response decorrelation when the network was driven by identical afferent inputs. Strikingly, however, when we incorporated afferent heterogeneities into the network to account for the unique divergence in DG afferent connectivity, the impact of all three forms of local heterogeneities were significantly suppressed by the dominant role of afferent heterogeneities in mediating response decorrelation. Our results unveil a unique convergence of cellular- and network-scale degeneracy in the emergence of response decorrelation in the DG, and constitute a significant departure from the literature that assigns a critical role for local network heterogeneities in input discriminability.
SIGNIFICANCE STATEMENT The olfactory bulb and the dentate gyrus (DG) networks assimilate new neurons in adult rodents, with adult neurogenesis postulated to subserve efficacious information transfer by reducing correlations in neuronal responses to afferent inputs. Heterogeneities emerging from the lateral dendro-dendritic synapses, mediated by locally-projecting neurogenic inhibitory granule cells, are known to play critical roles in channel decorrelation in the olfactory bulb. However, the contributions of different heterogeneities in mediating response decorrelation in DG, comprising neurogenic excitatory granule cells projecting beyond DG and endowed with uniquely divergent afferent inputs, have not been delineated. Here, we quantitatively demonstrate the dominance of afferent heterogeneities, over multiple local heterogeneities, in the emergence of response decorrelation in DG, together unveiling cross-region degeneracy in accomplishing response decorrelation.
The ability of a neuronal population to effectuate response decorrelation has been identified as an essential prelude to efficient neural encoding, as the decorrelation process ensures that information conveyed by different neuronal channels is complementary (1-5). The critical importance of local circuit heterogeneities, — including those in intrinsic properties, in synaptic strengths and in neuronal structure, observed either under baseline conditions or achieved specifically through adult neurogenesis, — in achieving such response decorrelation has been recognized across different brain regions (1-18). Studies in the olfactory bulb (OB), one of the two prominent brain regions that express adult neurogenesis, have assessed the impact of these local heterogeneities on response decorrelation (1, 2, 6, 10), emphasizing the critical importance of intrinsic heterogeneities and lateral inhibition in the emergence of response decorrelation. However, despite the dentate gyrus (DG) being the other prominent brain region expressing adult neurogenesis and despite the widespread literature on the role of DG in pattern separation (7-9, 19-22), it is surprising that the impact of distinct forms of local and afferent heterogeneities on channel decorrelation has not been assessed in the DG.
This lacuna in the literature is especially striking because of the stark contrasts in terms of the unique afferent connectivity to the dentate and in the specific roles of adult neurogenesis in the DG vs. the OB (1-3, 7-9, 18-26), although both circuits have been implicated in response decorrelation and express adult neurogenesis. First, there is evidence for adult neurogenesis resulting in both excitatory granule cells and inhibitory basket cells in the dentate, whereas adult neurogenesis results in inhibitory granule cells in the OB. Second, OB granule cells lack axons and make local lateral inhibitory dendrodendritic synapses with other local circuit cells, and do not project outside the OB. In striking contrast, DG cells extend unmyelinated axons connecting both within and beyond (principally to CA3) the DG. It was this feature of the DG granule cells as the principal projection cell to the CA3 that was important in its theoretically postulated role in response decorrelation before inputs are fed to the pattern completing recurrent CA3 network (27, 28). Third, neurogenesis results in the replacement of the majority of granule neurons in the olfactory bulb, whereas it leads to a substantial addition of granule neurons to the hippocampal dentate gyrus, suggesting that adult neurogenesis could play distinct roles in the two brain regions (24). Finally, and most importantly, the principal inputs to the olfactory granule cells are the mitral cells through local dendrodendritic synapses, whereas the principal inputs to the ~1.2 billion dentate granule cells are the ~30,000 LII entorhinal cortical neurons. This significant divergence and sparsity of connections in the afferent projections from the excitatory cells in the EC to the excitatory DG granule cells is therefore unique to the DG, and is critical to the analysis in terms of the specific roles of local vs. afferent heterogeneities.
In the DG network, there are at least four distinct forms of heterogeneities that could mediate response decorrelation (the first three are local to the DG network whereas the fourth is afferent onto the network): (i) heterogeneity in intrinsic ion channel and excitability properties of the neurons; (ii) non-uniformities in the local synaptic connectivity; (iii) structural heterogeneities in neurons introduced by adult neurogenesis; and (iv) input-driven heterogeneity that is reflective of the distinct sets of afferent inputs that impinge on different neurons (as a consequence of the unique divergence in DG connectivity). Which of these distinct forms of heterogeneities play a critical role in mediating response decorrelation in the DG? Does a highly divergent, sparsely active network need an additional layer of neurogenesis-induced heterogeneity for effectuating response decorrelation? What is the impact of cell-to-cell variability in ion channel properties and excitability on response decorrelation in the DG network receiving different patterns of inputs?
In this study, we systematically and incrementally incorporate the four different forms of heterogeneities into conductance-based network models of the DG and delineate the impact of each form of heterogeneity on response decorrelation. Specifically, we employed a stochastic search algorithm spanning an exhaustive parametric space (involving experimentally-determined ion channel as well as neurophysiological properties) to reveal cellular-scale degeneracy in the DG, whereby disparate combinations of passive and active properties yielded analogous cellular physiology of excitatory granule (GC) and inhibitory basket cell (BC) populations. Next, we further expanded the parametric search space to encompass biologically observed heterogeneities in local/afferent network connectivity and in neurogenesis-induced alteration to neuronal structure and excitability. We systematically assessed response decorrelation in different DG networks, each built with incremental addition of the four distinct forms of heterogeneities. We found that in the absence of afferent heterogeneities, that is when the DG network was driven by identical afferent inputs, the three forms of local heterogeneities were independently and synergistically capable of mediating significant response decorrelation. Strikingly, however, when we incorporated afferent heterogeneities into the network to account for the unique divergence in DG afferent connectivity, we found that the impact of all three forms of local heterogeneities were suppressed by the dominant role played by afferent heterogeneities in mediating the emergence of response decorrelation. These conclusions on the dominance of afferent heterogeneities constitute a significant departure from the literature that assigns a critical role for local network heterogeneities (including those induced by adult neurogenesis) in input discriminability, and unveils crucial distinctions in the emergence of response decorrelation in the DG vs. the OB network.
RESULTS
In systematically delineating the impact of distinct forms of heterogeneities on response decorrelation, we constructed networks of 500 GCs and 75 BCs from respective conductance-based model populations (Fig. 1A–B). The heterogeneous conductance-based model populations of GC and BC neurons were derived from independent stochastic search procedures that replicated 9 different electrophysiological measurements (Fig. 1C–G) for each cell type (Tables S1–S4). These 575 cells were distributed in a cylindrical neuropil of 156-μm diameter and 40-μm depth (Fig. 1A), with cell density and local connection probability between GCs and BCs (Fig. 1B) matched with experimental equivalents. Each cell in the network received local circuit inputs from other BCs or GCs (Fig. 1B) and external inputs (Fig. 1H) from several cells in the medial (MEC) and lateral entorhinal cortices (LEC), which allowed it to fire (Fig. 1I) at specific locations (Fig. 1J) within the arena that the virtual animal traversed in randomized order (over the entire simulation period of 1000 s).
Degeneracy in single neuron physiology of granule and basket cell model populations
We employed a well-established stochastic search strategy (29-32) to arrive at populations of conductance-based models for GCs and BCs. This exhaustive parametric search procedure was performed on 40 parameters for GCs (Table S1), and 18 parameters for BCs (Table S3), involving ion channel properties derived from respective neuronal subtypes. Nine different measurements, defining excitability and action potential firing patterns (Fig. 1; Table S2), were obtained from each of the 20,000 stochastically generated unique GC models, and were matched with corresponding electrophysiological GC measurements. We found 126 of the 20,000 models (~0.63%) where all nine measurements were within these electrophysiological bounds (Table S2), and thus were declared as valid GC models. A similar procedure was employed for BC cells, where 9 different measurements from 8,000 unique models were compared with corresponding electrophysiological BC measurements. Here, we found 54 of the 8,000 models (~0.675%) where all nine measurements were within electrophysiological bounds (Table S4), and declared them as valid BC models. The experimental bounds on physiological measurements for granule (Table S2) and basket (Table S4) cells were obtained from references (33-37).
Did the validation process place tight restrictions on model parameters that resulted in the collapse of all valid models to be near-homogeneous equivalents with very little changes in their parametric values? To address this, we plotted model parameters of 6 GCs (Fig. 2) and 6 BCs (Fig. S1), which had near-identical measurements values, and found the parametric values to spread through a wide span of the range employed in the respective stochastic searches. To further validate this, we plotted histograms of each of the 40 GC model parameters and the 18 BC model parameters, and found them to spread through the entire span of their respective ranges (Fig. 3A). These results demonstrated that the valid models were not near-homogeneous parametric equivalents, but form heterogeneous populations of GCs and BCs that functionally matched their respective electrophysiological measurements, thereby unveiling cellular-scale degeneracy in GC and BC neurons.
How did these neuronal populations achieve degeneracy? Did they achieve this by pair-wise compensation across parameters, or was change in one parameter compensated by changes in several other parameters to achieve robust physiological equivalence? In answering this, we plotted pair-wise scatter plots, independently on valid model parameters of the GC and BC populations (Fig. 3A), and computed pair-wise Pearson’s correlation coefficients for each scatter plot (Fig. 3B–C). We found that a vast majority of these pairs displayed very weak pair-wise correlations (R2 < 0.25; Fig. 3B–C), suggesting that degeneracy in both populations was achieved through collective changes spanning several parameters.
Heterogeneities in neuronal intrinsic properties mediated decorrelation of neuronal responses to identical external inputs
Cellular-scale degeneracy in these valid model populations provided an ideal manifestation of physiologically constrained intrinsic heterogeneities in the GC and BC model populations. Consequently, in defining the first layer of heterogeneity, we constructed a network of these heterogeneous populations with identical external inputs from the MEC and LEC and homogenous local synaptic connectivity.
We allowed the virtual animal to traverse the arena, recorded the voltage traces of all the GCs and BCs in this network, computed their firing rates and overlaid neuronal firing structure on the arena to observe the emergence of place fields (Fig. 4A). To quantify the extent of decorrelation achieved through the introduction of intrinsic heterogeneities, we computed instantaneous firing rates of all neurons in the network across the entire traversal period (Fig. 4A; Fig. S2) and calculated pair-wise Pearson’s correlation coefficients across these firing rate arrays for all neurons (Fig. 4B; Fig. S2B). If the network were composed of a homogeneous population of GCs and BCs receiving identical inputs, then the responses of all GCs would be identical to each other, with all pair-wise correlation coefficients set at unity. However, owing to heterogeneous intrinsic excitability of individual neurons, their responses exhibited significant differences, especially in terms of overall firing rate at individual place fields (Fig. 4A; Fig. S2), even with identical external inputs and homogeneous local synaptic weights. Such dissimilarity in neuronal firing rate response emerges from two distinct manifestations of intrinsic heterogeneity. First, certain periods of identical synaptic inputs would be subthreshold for neurons with lower excitability (e.g., Cell #2 in Fig. 4A), but would be suprathreshold for neurons with relatively higher excitability (e.g., Cell #5 in Fig. 4A), thereby manifesting as changes in firing rate or in the emergence of place fields at specific locations (38). Additionally, these observations also suggest that DG neurons could undergo rate remapping (22, 26) merely as a consequence of plasticity in intrinsic excitability. Second, although the numbers and synaptic weights of excitatory or inhibitory synapses received by neurons were identical, the patterns of activation of these synapses would be different across neurons as a consequence of significant variability in their respective presynaptic neuronal firing (Fig. 4A, Fig. S2).
Consequent to such variability in firing responses of this intrinsically heterogeneous population of neurons, we found the distribution of correlation coefficients of instantaneous firing rates to be significantly (Kolmogorov Smirnov, KS test; p<0.001) different from an all-unity distribution representative of identical responses achieved in the absence of intrinsic variability (Fig. 4B–C; Fig. S2C). Next, we repeated these simulations with different combinations of excitatory and inhibitory synaptic weights, setting all local synapses to the same value, and computed cumulative histograms of firing rate correlation coefficients (Fig. 4D–E; Fig. S2D–E). We found a significant shift (Fig. 4C vs. Fig. 4E; KS test; p<0.001) in the level of decorrelation with different combinations of synaptic weights.
Synaptic heterogeneity modulates decorrelation of neuronal responses to identical external inputs
Motivated by observations on the role of the local synaptic weights in modulating response decorrelation, we systematically assessed the impact of altering the excitatory and inhibitory synaptic weights on the correlation histograms. As a first step, the network was endowed with intrinsic heterogeneities and all local synaptic weights were identical but were assigned different values across different simulations (Fig. 5A–B; Fig. S3A–B). Although increases in either excitatory or inhibitory weights significantly enhanced the level of response decorrelation, the impact of increasing inhibitory weights had a dominant impact on decorrelating network responses (Fig. 5A–B; Fig. S3A–B) emphasizing the critical role of local inhibitory neurons in defining response decorrelation in excitatory neurons (9, 12, 13).
Would introduction of synaptic heterogeneities, where different synapses in the local network assume distinct values, further enhance neuronal response decorrelation? To test this, we assigned weights of excitatory and inhibitory synapses in the local network to randomized values picked from respective uniform distributions (Fig. 5C–E; Fig. S3C). Surprisingly, we found that introduction of synaptic heterogeneity did not enhance the level of response decorrelation, but allowed response decorrelation to express at a level that was within the bounds set by extreme values of identical synaptic weights (Fig. 5E; Fig. S3C). Importantly, the level of decorrelation achieved by the introduction of local synaptic heterogeneity into a homogeneous population (no intrinsic heterogeneity) of GCs and BCs was significantly minimal compared to that achieved by the mere presence of intrinsic heterogeneity (Fig. 5E; Fig. S3C; cf. Fig. 4, Fig. S2). Together, although the introduction of synaptic heterogeneity critically modulated the level of response decorrelation, these results suggest intrinsic heterogeneity as the dominant form among intrinsic and synaptic forms of heterogeneities in mediating response decorrelation.
Adult neurogenesis-induced structural heterogeneity in neuronal age enhances decorrelation of neuronal responses to identical external inputs
A prominent hypothesis on the specific functions of adult neurogenesis in DG neurons is on their role in response decorrelation. One part of the rationale behind this hypothesis is the distinct excitability properties of new neurons that provide an additional layer of heterogeneity (7-9, 15, 19-21, 39). Although there are lines of evidence linking adult neurogenesis to response decorrelation, the specific role of new neurons and the additional layer of heterogeneity introduced by them in regulating input discriminability has not been systematically assessed.
To introduce neurogenesis-induced heterogeneity into our network, we noted that the excitability of new born neurons in the DG, which could mature to either GCs or BCs, is higher as a consequence of lower surface area reflective of the diminished arborization of immature neurons (9, 15, 39, 40). To quantitatively match the excitability properties of these neurons, we introduced structural plasticity by reducing the surface area of the valid GC and BC models (Fig. 3) through reduction of their diameter. This reduction in surface area expresses as an increase in input resistance (39, 41, 42) in each of these neurons (Fig. 6A), which in turn translates to increase in firing rate (Fig. 6B).
With the ability to introduce intrinsic, synaptic and neurogenesis-induced forms of heterogeneity into our network, we analyzed three distinct networks (fully mature, fully immature and variable age) to specifically understand the role of neurogenesis-induced heterogeneity on response decorrelation to identical inputs. All three networks were endowed with intrinsic as well as synaptic heterogeneities receiving afferent inputs from the same arena (Figs. 4–5), and the distinction between the three cases was only with reference to neuronal age (Fig. 6D). In comparing the firing rates of the GCs for different network configurations, we found that the firing rates of all GCs were comparable for all cases where neurogenesis-induced heterogeneities were absent. However, with the introduction of neurogenesis, especially in the scenario where all cells were immature, the firing rates increased and spanned a larger range. In the more physiologically relevant scenario of heterogeneous cellular age, although the firing rates spanned a larger range, a significant proportion of them were in the low firing regime characteristic of GCs (Fig. 6E).
We found that the level of response decorrelation in the fully immature network was significantly (KS test; p<0.001) higher than that achieved in the fully mature network (Fig. 6F). This is to be expected because the structural heterogeneity (effectuated by changes in diameter) would amplify the inherent intrinsic heterogeneity of neurons in the network, thereby enhancing the beneficiary effects of intrinsic heterogeneity that we had observed earlier (Fig. 4). Importantly, reminiscent of our results with the introduction of synaptic heterogeneity (Fig. 5), in the network that was endowed with variability in neuronal age, the level of decorrelation was intermediate between that obtained with the fully mature and the fully immature networks (Fig. 6F). Together, these results demonstrate that neurogenesis-induced variability in neuronal response properties adds an additional layer of heterogeneity in the DG network, and enhances network decorrelation to identical external inputs.
Input-driven heterogeneity mediated by sparseness of afferent connectivity is a dominant regulator of neuronal response decorrelation
An important defining characteristic of the DG network is the sparseness of the afferent connectivity matrix that is reflective of massive convergence and divergence reflecting the small number of layer II EC cells (~30,000) that project to a large (~1.2 million) number of DG cells, resulting in significant variability in the set of afferent external inputs impinging on each GC (23, 43). Thus far in our analysis, in an effort to delineate the impact of three distinct forms of heterogeneity, we employed an artificial construct where all neurons in the network received identical inputs. To assess the impact of this fourth form of afferent input-driven heterogeneity, we introduced divergence in the set of EC neurons that project onto each GC and BC. This implied that each GC and BC now received distinct sets of EC inputs.
As a consequence of distinct set of inputs impinging on each GC, the firing fields were distinct across different GCs (Fig. 7A) and BCs (Fig. S4), unlike the near-identical firing fields (except for differences in firing frequency or threshold) in the case where neurons received identical inputs (Fig. 4A; Fig. S2). Importantly, when we analyzed pair-wise correlation of firing rates across different neurons, we found that the correlation coefficients were lower irrespective of the presence or absence of different forms of heterogeneity (Fig. 7B). The overall firing rate distributions obtained with either identical (Fig. 6) or distinct (Fig. 7C) afferent inputs were similar, thereby ruling out changes in firing rate as a possible cause for the observed differences in correlation coefficients.
Strikingly, when we plotted the cumulative distributions of correlation coefficients obtained with the introduction of distinct forms of network heterogeneities, we found them to significantly overlap with each other (Fig. 7D). This is in stark contrast to the network receiving identical external inputs (Fig. 5E, Fig. 6F), where introduction of each of intrinsic, synaptic and neurogenesis-induced heterogeneities enhanced or altered the level of response decorrelation. The negligible impact of the intrinsic or synaptic or age heterogeneities on the overall level of response decorrelation achieved in the presence of input-driven heterogeneities, which was higher than that obtained with identical inputs (Fig. 7E), unveiled the dominance of heterogeneities driven by afferent connectivity in determining response decorrelation.
Were our conclusions on the role of different forms of heterogeneities scalable and invariant to network size? To test this, we repeated our analyses in Figs. 4–7 with a smaller network made of 100 GCs and 15 BCs, and found our conclusions to scale across different network sizes (Fig. S5). Together, our results demonstrate that local heterogeneities in intrinsic, synaptic and neuronal structural (driven by adult neurogenesis) properties contributed to significant levels of response decorrelation in the presence of identical afferent synaptic drive. However, when the network received heterogeneous external inputs, the impact of local heterogeneities on response decorrelation was strongly suppressed by the dominant role of afferent heterogeneities in mediating neuronal response decorrelation.
DISCUSSION
Dominance of input-driven heterogeneity and implications for the physiological roles for adult neurogenesis
Our results quantitatively demonstrate a dominant role for afferent heterogeneities, driven specifically by the unique network structure of the DG involving huge number of GCs innervated by the small number of LII EC neurons, in driving response decorrelation in the DG. Within this framework, this dominant connectivity divergence in feed-forward afferents, synergistically coupled to the hetereogeneous intrinsic properties and the sparse GC activity that is sharpened by the local inhibitory network places the DG network as an ideal decorrelating system. Importantly, our conclusions on the dominance of heterogeneous afferent connectivity, with the local network heterogeneities playing secondary roles, pose specific questions on the role of adult neurogenesis in input discriminability, questioning the rationale behind the need for new neurons in a highly divergent, sparsely active network (9).
If adult neurogenesis-induced heterogeneities in neuronal properties were not the dominant contributor to response decorrelation, what is the precise role of adult neurogenesis in the DG? One possibility within our framework is that adult neurogenesis could be a mechanism for implementing afferent heterogeneities across DG neurons, whereby new neurons establish connections to afferent fibers in an activity-dependent manner (14, 44-46), thereby assigning a specific set of active afferent inputs to new neurons of the same time of birth (9, 25, 47). In such a scenario, the afferent heterogeneities would be driven by active assignment of spatial connectivity from the EC to individual DG neurons, whereby the novel contexts encountered by the animal are encoded by the temporal onset of neurons. Such active assignments could be driven by activity-dependent connectivity aided by the hyper-plastic, hyper-excitable nature of new neurons, and the resultant afferent heterogeneities (different neurons get different EC inputs) then plays specific roles in response decorrelation, in encoding temporal context and in controlling memory resolution (9, 14, 20, 25, 39, 44-47). In addition to this, our results suggest that the variability introduced by new neurons in terms of their intrinsic excitability (Figs. 4, 6) and in terms of altered excitation-inhibition balance (Fig. 5) could also be candidate mechanisms through which adult neurogenesis enhances (beyond what is driven by afferent heterogeneities) the degree of response decorrelation achieved in the DG network (8, 9, 16, 20, 25, 47).
Multiscale degeneracy: Convergence of different scales of degeneracy to achieve single-neuron homeostasis and response decorrelation
A central premise of robustness in biological function is degeneracy, where distinct structural components could combine to elicit analogous function. Given the several possible routes through which similar function can be achieved, it is possible for biological systems to invoke compensatory mechanisms to achieve the same function through very different parametric combinations (29-32, 48). Complementary to this powerful theory, our results show that identical levels of output dissimilarity could be achieved with disparate classes of parametric heterogeneity. Specifically, whereas degeneracy suggests that similar functionalities could be achieved with disparate combinations of constituent parameters, our results argue for a case where similar degrees of response decorrelation could be achieved through disparate classes of heterogeneity. Do our results constitute a departure from the premise of degeneracy in biological systems?
In systems that are responsible for encoding of novel information, robust homeostasis of output constitutes only one side of the overall physiological goals. The other side constitutes encoding of new information, which by definition involves changes to certain output characteristics to reflect this encoding process. With specific reference to the DG, whose prime encoding function has been postulated to be response decorrelation, it is important that the focus is not on mere maintenance of robust outputs. If response decorrelation were the overall function, and different classes of heterogeneity are considered as disparate structural components, our conclusions are consistent with the overall framework of degeneracy where distinct structural components could come together to elicit analogous function. Thus, there are several layers of degeneracy, spanning the molecular, cellular, network and behavioral scales of analyses, embedded in results presented in this study. At the cellular scale, distinct combinations of intrinsic parameters (the molecular components: the ion channels) come together to elicit analogous cellular response properties. At the network scale, distinct combinations of intrinsic and synaptic properties interact to elicit similar levels of response decorrelation. Together, our results unveil a systematic convergence of degeneracy spanning different scales of analysis in the DG network, achieving the twin goals of the DG network (input discriminability and firing rate homeostasis) within the broad framework of degeneracy.
Comparison of mechanisms for decorrelation in the dentate gyrus and in the olfactory bulb
The olfactory bulb (OB) is another brain region that expresses adult neurogenesis and has been postulated to play a critical role in channel decorrelation (referred here as response decorrelation) (1-3, 18). Although there are similarities in our conclusions with those in the olfactory literature in terms of the roles of neuronal nonlinearites, intrinsic heterogeneities and inhibition in effectuating channel decorrelation in the absence of afferent heterogeneities, the significant departure in our conclusions is with reference to the dominant role of afferent heterogeneities. We argue the dominance of afferent heterogeneities is a distinctive feature of the DG circuit, and is reflective of the unique afferent connectivity to the dentate and the several stark contrasts between the roles of adult neurogenesis in the DG vs. the OB circuit (1-3, 7-9, 18-26). Specifically, the significant divergence and sparsity of connections in the afferent projections from the excitatory cells in the EC to the excitatory GCs is unique to the DG, and is critical to the conclusions that we draw here in terms of the dominance of the afferent heterogeneities in mediating response decorrelation.
The well-established distinctions between these two networks, in conjunction with our conclusions on the unique role of afferent heterogeneities in the DG network suggest that the mechanisms behind achieving decorrelation in the OB and the DG networks are very different. Specifically, whereas decorrelation in the OB has been postulated to be aided by new laterally inhibiting neurons forming dendrodendritic synapses across the local circuit (1-3, 18), our conclusions here, derived from the specifics of the DG network, its intrinsic heterogeneities and its afferent connectivity, present a dominant role for the afferent heterogeneities supported by synergistic interactions with local heterogeneities. Together, these disparate structural routes to achieve decorrelation further emphasize our conclusions on degeneracy in encoding mechanisms. In discussing the role of distinct forms of heterogeneities in effectuating decorrelation, we had emphasized in the previous section the possibility of how distinct forms of heterogeneities could be recruited to achieve analogous levels of decorrelation. With these distinctions between the OB and the DG, it is clear that this degree of degeneracy could be much broader where the OB and DG could be using adult neurogenesis in very different ways towards achieving response decorrelation.
METHODS
Detailed experimental procedures are provided in the SI Methods. Briefly, the principal goal of this study was to systematically assess the impact of different forms of heterogeneities on response decorrelation in the DG. In doing this, we took advantage of the versatility of conductance-based neuronal network models, and distinguished between four different types of heterogeneities: (i) intrinsic heterogeneity, where the GC and BC model neurons that were employed to construct the network had widely variable intrinsic parametric combinations yielding physiological measurements that matched their experimental counterparts. These heterogeneous model populations were obtained using independent stochastic search procedures for GCs and BCs; (ii) synaptic heterogeneity, where the synaptic strength of the local GC-BC network was variable with excitatory and inhibitory synaptic permeability values picked from uniform random distributions; (iii) neurogenesis-induced heterogeneity in age of the neuron, where the DG network could be made entirely of mature or immature neurons, or be constructed from neurons that represented different randomized neuronal ages; and (iv) input-driven heterogeneity, where the GC and BC populations received either identical inputs from the EC, or each GC and BC received unique inputs from the EC. Networks endowed with different combinations of heterogeneities were constructed, and received inputs that were tied to the location of a virtual animal traversing in an arena. Spike timings of individual neurons in response to arena traversals were recorded, and pairwise correlations were computed on instantaneous firing rates of GC or BC neuron populations. Analyses of correlation coefficients across distinct networks endowed with disparate forms of heterogeneities were employed to derive insights into the roles of distinct forms of heterogeneities in driving input discriminability in these networks. All simulations were performed using the NEURON simulation environment (49).
Acknowledgments
This work was supported by the Wellcome Trust-DBT India Alliance (Senior fellowship to RN; IA/S/16/2/502727), Human Frontier Science Program (HFSP) Organization (RN), the Department of Biotechnology (RN), the Department of Science and Technology (RN), and the Ministry of Human Resource Development (RN & PM). The authors thank the members of the cellular neurophysiology laboratory for helpful discussions and for comments on a draft of this manuscript.
Footnotes
Competing final interests: The authors declare no conflict of interest.