Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity

Chavlis, Spyridon; Petrantonakis, Panagiotis C.; Poirazi, Panayiota

doi:10.1002/hipo.22675

Cited by 65 publications

(112 citation statements)

References 85 publications

(122 reference statements)

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“…On the other hand, we observed a subtle reduction in recurrent inhibition and a decrease in LTD induction in the CE group that may contribute to the altered nature of spatial representations in CA1. Alternatively, there may have been changes to the dentate gyrus in terms of its pattern separation capabilities (Chavlis, Petrantonakis, & Poirazi, 2017;Morris, Churchwell, Kesner, & Gilbert, 2012;Rolls, 2013;Santoro, 2013;Treves & Rolls, 1994) or to the perforant path input to the CA1 pyramidal cells. The effect of environmental enrichment on the perforant path input to region CA1 have not yet been investigated while no change has been observed in the perforant path inputs to the dentate gyrus (Eckert & Abraham, 2010).…”

Section: Discussionmentioning

confidence: 99%

Exposure to complex environments results in more sparse representations of space in the hippocampus

et al. 2017

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The neural circuitry mediating sensory and motor representations is adaptively tuned by an animal's interaction with its environment. Similarly, higher order representations such as spatial memories can be modified by exposure to a complex environment (CE), but in this case the changes in brain circuitry that mediate the effect are less well understood. Here we show that prolonged CE exposure was associated with increased selectivity of CA1 “place cells” to a particular recording arena compared to a social control (SC) group. Furthermore, fewer CA1 and DG neurons in the CE group expressed high levels of Arc protein, a marker of recent activation, following brief exposure to a completely novel environment. The reduced Arc expression was not attributable to overall changes in cell density or number. These data indicate that one effect of CE exposure is to modify high-level spatial representations in the brain by increasing the sparsity of population coding within networks of neurons. Greater sparsity could result in a more efficient and compact coding system that might alter behavioural performance on spatial tasks. The results from a behavioural experiment were consistent with this hypothesis, as CE-treated animals habituated more rapidly to a novel environment despite showing equivalent initial responding.

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Section: Discussionmentioning

confidence: 99%

Exposure to complex environments results in more sparse representations of space in the hippocampus

et al. 2017

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“…While early models were able to capture several features of pattern separation, they lacked the ability to point the underlying mechanisms. Recent, more detailed neuron models generated a number of mechanistic explanations (Chavlis et al, ; Platschek et al, ; Tejada & Roque, ; Ujfalussy et al, ; Yim et al, ). For example, the dendrites of GCs were very recently assigned a role in their ability to distinguish between similar inputs (Chavlis et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Recent, more detailed neuron models generated a number of mechanistic explanations (Chavlis et al, ; Platschek et al, ; Tejada & Roque, ; Ujfalussy et al, ; Yim et al, ). For example, the dendrites of GCs were very recently assigned a role in their ability to distinguish between similar inputs (Chavlis et al, ). While dendrites have been suggested to serve as powerful computational units of pyramidal neurons across various brain areas (London & Häusser, ; Papoutsi, Kastellakis, Psarrou, Anastasakis, & Poirazi, ; Poirazi, Brannon, & Mel, ; Sidiropoulou, Pissadaki, & Poirazi, ; Silver, ; Spruston, ), their role in pattern separation is just starting to be addressed.…”

Section: Discussionmentioning

confidence: 99%

“…Note that even among computational models, neural pattern separation in measured in slightly different ways. Some assess the difference in the overlap of input and output patterns (Myers & Scharfman, ; Nolan, Wyeth, Milford, & Wiles, ) while not explicitly accounting for sparsity and network size, while others calculate the corresponding distances in the input and output patterns (Chavlis et al, ; Faghihi & Moustafa, ) taking such features into account. In general, the main difference among these approaches lies in the metric used to quantify neural pattern separation.…”

Section: Assessing Pattern Separationmentioning

confidence: 99%

“…Recent modeling efforts have explored the possible contribution of dendrites in the DG function (Platschek, Cuntz, Vuksic, Deller, & Jedlicka, ; Tejada, Arisi, García‐Cairasco, & Roque, ; Tejada, García‐Cairasco, & Roque, ; Ujfalussy, Kiss, & Érdi, ), however they did not study the effect of GC structure on pattern separation. Chavlis et al () recently used a simple neuronal network of the DG consisting of four neuronal types (GCs, MCs, BCs, and HCs) to show that dendrites could contribute to pattern separation by controlling sparsity. Inspired by the DG network model of (Myers & Scharfman, ), the simulated GCs were extended to include experimentally constrained dendritic compartments of various numbers, length and diameter.…”

Section: Detailed Computational Approaches To Pattern Separationmentioning

confidence: 99%

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Pattern separation in the hippocampus through the eyes of computational modeling

Chavlis

Poirazi

2017

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Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub-cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.

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Disparate forms of heterogeneities and interactions among them drive channel decorrelation in the dentate gyrus: Degeneracy and dominance

2018

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The ability of a neuronal population to effectuate channel decorrelation, which is one form of 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 channel 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 channel decorrelation. First, to effectively incorporate intrinsic heterogeneities, we built physiologically validated heterogeneous populations of granule (GC) and basket cells (BC) through independent stochastic search algorithms spanning exhaustive parametric spaces. These stochastic search algorithms, which were independently constrained by experimentally determined ion channels and by neurophysiological signatures, revealed cellular‐scale degeneracy in the DG. Specifically, in GC and BC populations, disparate parametric combinations yielded similar physiological signatures, with underlying parameters exhibiting significant variability and weak pair‐wise correlations. Second, we introduced synaptic heterogeneities through randomization of local synaptic strengths. Third, 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 channel decorrelation when the network was driven by identical afferent inputs. However, when we incorporated afferent heterogeneities into the network to account for the divergence in DG afferent connectivity, the impact of all three forms of local heterogeneities was significantly suppressed by the dominant role of afferent heterogeneities in mediating channel decorrelation. Our results unveil a unique convergence of cellular‐ and network‐scale degeneracy in the emergence of channel decorrelation in the DG, whereby disparate forms of local and afferent heterogeneities could synergistically drive input discriminability.

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Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity

Cited by 65 publications

References 85 publications

Exposure to complex environments results in more sparse representations of space in the hippocampus

Exposure to complex environments results in more sparse representations of space in the hippocampus

Pattern separation in the hippocampus through the eyes of computational modeling

Disparate forms of heterogeneities and interactions among them drive channel decorrelation in the dentate gyrus: Degeneracy and dominance

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