Computational analysis of network activity and spatial reach of sharp wave-ripples

Canakci, Sadullah; Toy, Muhammed Faruk; Inci, Ahmet; Liu, Xin; Kuzum, Duygu

doi:10.1371/journal.pone.0184542

Cited by 5 publications

(10 citation statements)

References 55 publications

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“…The signal amplitude can be significantly reduced due to spatial-averaging effects across the recording area of the electrodes. This spatial averaging has been studied and reported in several publications (Canakci et al, 2017; Hill et al, 2018; Viswam et al, 2018b). The work of Viswam et al (2018b) shows that the signal from a very close source can have up to 25 % attenuation when measured with an 86-μm 2 electrode as compared to an 11-μm 2 electrode.…”

Section: Resultsmentioning

confidence: 98%

High-Density Electrical Recording and Impedance Imaging With a Multi-Modal CMOS Multi-Electrode Array Chip

et al. 2019

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Multi-electrode arrays, both active or passive, emerged as ideal technologies to unveil intricated electrophysiological dynamics of cells and tissues. Active MEAs, designed using complementary metal oxide semiconductor technology (CMOS), stand over passive devices thanks to the possibility of achieving single-cell resolution, the reduced electrode size, the reduced crosstalk and the higher functionality and portability. Nevertheless, most of the reported CMOS MEA systems mainly rely on a single operational modality, which strongly hampers the applicability range of a single device. This can be a limiting factor considering that most biological and electrophysiological dynamics are often based on the synergy of multiple and complex mechanisms acting from different angles on the same phenomena. Here, we designed a CMOS MEA chip with 16,384 titanium nitride electrodes, 6 independent operational modalities and 1,024 parallel recording channels for neuro-electrophysiological studies. Sixteen independent active areas are patterned on the chip surface forming a 4 × 4 matrix, each one including 1,024 electrodes. Electrodes of four different sizes are present on the chip surface, ranging from 2.5 × 3.5 μm 2 up to 11 × 11.0 μm 2 , with 15 μm pitch. In this paper, we exploited the impedance monitoring and voltage recording modalities not only to monitor the growth and development of primary rat hippocampal neurons, but also to assess their electrophysiological activity over time showing a mean spike amplitude of 144.8 ± 84.6 μV. Fixed frequency (1 kHz) and high sampling rate (30 kHz) impedance measurements were used to evaluate the cellular adhesion and growth on the chip surface. Thanks to the high-density configuration of the electrodes, as well as their dimension and pitch, the chip can appreciate the evolutions of the cell culture morphology starting from the moment of the seeding up to mature culture conditions. The measurements were confirmed by fluorescent staining. The effect of the different electrode sizes on the spike amplitudes and noise were also discussed. The multi-modality of the presented CMOS MEA allows for the simultaneous assessment of different physiological properties of the cultured neurons. Therefore, it can pave the way both to answer complex fundamental neuroscience questions as well as to aid the current drug-development paradigm.

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Section: Resultsmentioning

confidence: 98%

High-Density Electrical Recording and Impedance Imaging With a Multi-Modal CMOS Multi-Electrode Array Chip

et al. 2019

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“…The hippocampus CA1 model is based on the previous work, which has demonstrated the generation of sharp-wave-ripples under noisy inputs [14-16]. Our adapted model consists of 400 pyramidal cells (PY cells) and 100 basket cells (BS cells), which are chosen to comply with the CA1 neuroanatomy [31].…”

Section: Methodsmentioning

confidence: 99%

“…In our model, the background noise of each neuron is introduced through the noisy AMPA synapses of Poissonian characteristics. In order to quantify the intensity of the background noise, we compute the second moment of the noise current, which is the same as previous studies [14, 16].…”

Section: Methodsmentioning

confidence: 99%

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A biophysical computational model for memory trace transfer from hippocampus to neocortex

Liu¹,

Kuzum²

2018

Preprint

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The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex exhibit coordinated reactivations during awake ripple events. Also, the reactivation seems stronger during initial awake learning. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist the memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear. In this work, we build a biophysical model that includes both CA1 and layer V networks of the prefrontal cortex to investigate the possible mechanisms, by which the memory traces in the hippocampus can be transferred to prefrontal cortex. We first show that the local field potentials generated in the hippocampus and prefrontal cortex exhibit ripple range activities that are consistent with the recent experimental studies. Then, we find that the sequence information stored in the hippocampus can be successfully transferred to the prefrontal cortex recurrent networks through spike-timing dependent plasticity (STDP) and sequence replays. Further, we investigate the mechanisms of memory retrieval in the PFC network. Our findings suggest that the stored memory traces in the prefrontal cortex network can be retrieved through two different mechanisms, namely the cell-specific input and non-specific spontaneous background noise. Finally, we show that more SWRs and an optimal background noise level will both contribute to better sequence reactivations in the PFC network during memory retrieval. Our study presents a possible explanation for the memory trace transfer from the hippocampus to the neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.

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“…Toward this goal, we build a unified biophysical computational model that couples a hippocampal CA1 network (Canakci et al, 2017) with a PFC network to study the memory transfer from the HPC to the PFC and memory trace reactivations. Under the sequential inputs from a virtual CA3 network, the CA1 network generates ripple range oscillations in the local field potentials along with simultaneous sequential pyramidal cell replays.…”

Section: Introductionmentioning

confidence: 99%

Hippocampal-Cortical Memory Trace Transfer and Reactivation Through Cell-Specific Stimulus and Spontaneous Background Noise

Liu

Kuzum

2019

Front. Comput. Neurosci.

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The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex (PFC) exhibit coordinated reactivations during awake ripple events. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear due to the lack of unified models that include both the hippocampal and cortical networks. In this work, using a coupled biophysical model of both CA1 and PFC, we investigate possible mechanisms of hippocampal-cortical memory trace transfer and the conditions that assist reactivation of the transferred memory traces in the PFC. To validate our model, we first show that the local field potentials generated in the hippocampus and PFC exhibit ripple range activities that are consistent with the recent experimental studies. Then we demonstrate that during ripples, sequence replays can successfully transfer the information stored in the hippocampus to the PFC recurrent networks. We investigate possible mechanisms of memory retrieval in PFC networks. Our results suggest that the stored memory traces in the PFC network can be retrieved through two different mechanisms, namely the cell-specific input representing external stimuli and non-specific spontaneous background noise representing spontaneous memory recall events. Importantly, in both cases, the memory reactivation quality is robust to network connection loss. Finally, we find out that the quality of sequence reactivations is enhanced by both increased number of SWRs and an optimal background noise intensity, which tunes the excitability of neurons to a proper level. Our study presents a mechanistic explanation for the memory trace transfer from the hippocampus to neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.

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Computational analysis of network activity and spatial reach of sharp wave-ripples

Cited by 5 publications

References 55 publications

High-Density Electrical Recording and Impedance Imaging With a Multi-Modal CMOS Multi-Electrode Array Chip

High-Density Electrical Recording and Impedance Imaging With a Multi-Modal CMOS Multi-Electrode Array Chip

A biophysical computational model for memory trace transfer from hippocampus to neocortex

Hippocampal-Cortical Memory Trace Transfer and Reactivation Through Cell-Specific Stimulus and Spontaneous Background Noise

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