Pattern separation in the hippocampus through the eyes of computational modeling

Chavlis, Spyridon; Poirazi, Panayiota

doi:10.1002/syn.21972

Cited by 21 publications

(18 citation statements)

References 125 publications

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“…Pattern separation is defined as the transformation of a non-simultaneous set of similar input patterns of neuronal activity into less similar output patterns [5], and is theorized to happen in the hippocampus before encoding a memory trace in order to avoid confusion between similar memories [6]. Despite a long history of research on the subject, mostly in silico [7], it is still unclear how the activity of single neurons underlies this computation.…”

Section: Introductionmentioning

confidence: 99%

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

2019

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Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extents and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.

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Section: Introductionmentioning

confidence: 99%

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

2019

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“…Indeed, DG lesions impair mnemonic discrimination both in rodents (Treves et al, 2008; Kesner and Rolls, 2015; Kesner et al, 2016) and humans (Yassa et al, 2011; Baker et al, 2016; Bennett and Stark, 2016; Dillon et al, 2017). Moreover, computational models (Chavlis and Poirazi, 2017) and recent experiments (Berron et al, 2016; Knierim and Neunuebel, 2016; Madar et al, 2019a, 2019b; Woods et al, 2020) suggest that the DG circuitry supports multiple forms of pattern separation (Santoro, 2013). Whether such computations underlie mnemonic discrimination remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

Madar

Pfammatter

Bordenave

et al. 2020

Preprint

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8Health 9 Acknowledgement: We thank Lin Lin who helped start behavioral tests in mice in the Jones lab 10 and Drs. C.E.L Stark and M.A. Yassa for allowing us to use software and materials they 11 developed.12 Abstract: 24 In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the 25 hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help 26 discriminate between similar memories by performing pattern separation, but whether epilepsy 27 leads to a breakdown in this neural computation, and thus to mnemonic discrimination 28 impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by 29 behavioral deficits in mnemonic discrimination tasks, in both humans and mice. Using a recently 30 developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the 31 dentate gyrus to perform certain forms of pattern separation. This is due to a subset of granule 32 cells with abnormal bursting that can develop independently of early EEG abnormalities. 33Overall, our results linking physiology, computation and cognition in the same mice, advance 34 our understanding of episodic memory mechanisms and their dysfunction in epilepsy. 35 36 Keywords: 37 Patch-clamp, low-dose systemic kainate model, kainic acid, interictal spikes, novelty 38 recognition, firing rate, spiking patterns, similarity metrics, input-output, entorhinal cortex, 39 perforant path, EPSC, excitation/inhibition ratio, dentate gate, Hebb-Marr theory 40 41 Introduction: 42 Temporal lobe epilepsy (TLE) represents about 60% of all epilepsy cases, a third of 43 which are refractory to medication (Tellez-Zenteno and Hernandez-Ronquillo, 2012). TLE is 44DG neural pattern separation and 2) that such a failure causes mnemonic discrimination 87 impairments remain experimentally untested. 88Here we tested, in humans and mice, whether TLE is characterized by deficits in 89 mnemonic discrimination and then recorded, in brain slices from the same mice, the spiking 90 patterns of single GCs in response to parametrically varied afferent stimulation in order to gauge 91 neuronal pattern separation. 92 93 Materials and Methods: 94 Human Behavior. A mnemonic similarity task (Stark et al., 2019), also known as a 95 behavioral pattern separation (BPS) task (Stark et al., 2013), was administered to 15 patients in 96 the University of Wisconsin-Madison Epilepsy Monitoring Unit. Under an approved Institutional 97 Review Board protocol and after obtaining informed consent, the task was administered in 98 conjunction with a standard neuropsychiatric evaluation and during electroencephalographic 99 (EEG) recording, both of which are part of standard practice for diagnosing the patients' seizures. 100 Patients were 18-65 years old, male and female. Only patients with a preliminary diagnosis of 101 TLE were included in our analysis. As controls, 20 subjects without epilepsy, recruited to match 102 the patients' age and sex distributions (family members of ...

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“…Many genes affecting synaptic plasticity undergo pronounced circadian oscillations, changing the rules of synaptic plasticity depending on the time of day 6 . Theoretical and empirical work suggests that the dentate gyrus (DG) actively reduces the overlap of activity patterns coming from entorhinal cortex [7][8][9] . In vivo electrophysiology and calcium imaging experiments revealed that the majority of GCs are silent, and of the active cells, just a small fraction show spatial tuning ("place cells") [10][11][12][13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

cFos Ensembles in the Dentate Gyrus Rapidly Segregate Over Time and do not Form a Stable Map of Space

Lamothe-Molina

Franzelin

Auksutat

et al. 2020

Preprint

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Mice require several days of training to master the water maze, a spatial memory task for rodents. The hippocampus plays a key role in the formation of spatial and episodic memories, a process that involves the activation of immediate-early genes such as cFos. We trained cFos-reporter mice in the water maze, expecting that consistent spatial behavior would be reflected by consistent cFos patterns across training episodes. Even after extensive training, however, different sets of dentate gyrus (DG) granule cells were activated every day. Suppressing activity in the original encoding ensemble helped mice to learn a novel platform position (reversal learning). Our results suggest that even in a constant environment, cFos+ ensembles in the dorsal DG segregate as a function of time, but become partially reactivated when animals try to access memories of past events.

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Pattern separation in the hippocampus through the eyes of computational modeling

Cited by 21 publications

References 125 publications

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

cFos Ensembles in the Dentate Gyrus Rapidly Segregate Over Time and do not Form a Stable Map of Space

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