scholarly journals Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

2021 ◽  
Author(s):  
Barbara Feulner ◽  
Matthew G. Perich ◽  
Raeed H. Chowdhury ◽  
Lee E. Miller ◽  
Juan Álvaro Gallego ◽  
...  
Keyword(s):  
Neural Activity ◽  
Recurrent Network ◽  
Neuron Activity ◽  
Local Connectivity ◽  
Neural Recordings ◽  
Synaptic Noise ◽  
Synaptic Changes ◽  
Single Neuron Activity ◽  
Good Agreement

Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

In vivo visualization of single-unit recording sites using MRI-detectable elgiloy deposit marking

2011 ◽  
Vol 105 (3) ◽  
pp. 1380-1392 ◽  
Author(s):  
Kenji W. Koyano ◽  
Akinori Machino ◽  
Masaki Takeda ◽  
Teppei Matsui ◽  
Ryoko Fujimichi ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Single Neuron ◽  
Cognitive Task ◽  
Neuron Activity ◽  
Electrolytic Lesion ◽  
Mr Images ◽  
Gold Standard Method ◽  
Spin Echo Sequence ◽  
Single Neuron Activity

Precise localization of single-neuron activity has elucidated functional architectures of the primate cerebral cortex, related to vertically stacked layers and horizontally aligned columns. The traditional “gold standard” method for localizing recorded neuron is histological examination of electrolytic lesion marks at recording sites. Although this method can localize recorded neurons with fine neuroanatomy, the necessity for postmortem analysis prohibits its use in long-term chronic experiments. To localize recorded single-neuron positions in vivo, we introduced MRI-detectable elgiloy deposit marks, which can be created by electrolysis of an elgiloy microelectrode tip and visualized on highly contrasted magnetic resonance (MR) images. Histological analysis validated that the deposit mark centers could be localized relative to neuroanatomy in vivo with single-voxel accuracy, at an in-plane resolution of 200 μm. To demonstrate practical applications of the technique, we recorded single-neuron activity from a monkey performing a cognitive task and localized it in vivo using deposit marks (deposition: 2 μA for 3 min; scanning: fast-spin-echo sequence with 0.15 × 0.15 × 0.8 mm3 resolution, 120/4,500 ms of echo-time/repetition-time and 8 echo-train-length), as is usually performed with conventional postmortem methods using electrolytic lesion marks. Two localization procedures were demonstrated: 1) deposit marks within a microelectrode track were used to reconstruct a dozen recorded neuron positions along the track directly on MR images; 2) combination with X-ray imaging allowed estimation of hundreds of neuron positions on MR images. This new in vivo method is feasible for chronic experiments with nonhuman primates, enabling analysis of the functional architecture of the cerebral cortex underlying cognitive processes.

scholarly journals Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

2019 ◽  
Author(s):  
M. E. Rule ◽  
D. Schnoerr ◽  
M. H. Hennig ◽  
G. Sanguinetti
Keyword(s):  
State Space ◽  
Point Process ◽  
Neural Activity ◽  
Large Scale ◽  
Single Neuron ◽  
Neural Field ◽  
State Space Models ◽  
Neuron Activity ◽  
Single Neuron Activity ◽  
Neural Field Models

AbstractLarge-scale neural recordings are becoming increasingly better at providing a window into functional neural networks in the living organism. Interpreting such rich data sets, however, poses fundamental statistical challenges. The neural field models of Wilson, Cowan and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. We developed a method based on moment closure to interpret neural field models as latent state-space point-process models, making mean field models amenable to statistical inference. We demonstrate that this approach can infer latent neural states, such as active and refractory neurons, in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatio-temporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.SignificanceDeveloping statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent statespace equations and demonstrate inference for a neural field model of excitatory spatiotemporal waves that emerge in the developing retina.

scholarly journals The rat medial prefrontal cortex exhibits flexible neural activity states during the performance of an odor span task

2018 ◽  
Author(s):  
E De Falco ◽  
L An ◽  
N Sun ◽  
AJ Roebuck ◽  
Q Greba ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Neural Activity ◽  
Activity Patterns ◽  
Span Task ◽  
Neuron Activity ◽  
Neural Recordings ◽  
Odor Span ◽  
Abrupt Shifts

AbstractMedial prefrontal cortex (mPFC) activity is fundamental for working memory (WM), attention, and behavioral inhibition; however, a comprehensive understanding of the neural computations underlying these processes is still forthcoming. Towards this goal, neural recordings were obtained from the mPFC of awake, behaving rats performing an odor span task of WM capacity. Neural populations were observed to encode distinct task epochs and the transitions between epochs were accompanied by abrupt shifts in neural activity patterns. Putative pyramidal neuron activity increased significantly earlier in the delay for sessions where rats achieved higher spans. Furthermore, increased putative interneuron activity was only observed at the termination of the delay thus indicating that local processing in inhibitory networks was a unique feature to initiate foraging. During foraging, changes in neural activity patterns associated with the approach to a novel odor, but not familiar odors, were robust. Collectively, these data suggest that distinct mPFC activity states underlie the delay, foraging, and reward epochs of the odor span task. Transitions between these states enable successful performance in dynamic environments placing strong demands on the substrates of working memory.

scholarly journals How do spike collisions affect spike sorting performance?

2021 ◽  
Author(s):  
Samuel Garcia ◽  
Alessio Buccino ◽  
Pierre Yger
Keyword(s):  
Template Matching ◽  
Ground Truth ◽  
Spatial Density ◽  
Neuron Activity ◽  
Spike Sorting ◽  
Extracellular Signals ◽  
New Generation ◽  
Single Neuron Activity

Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also dramatically increase the amount overlapping "synchronous" spikes (colliding in space and/or in time), challenging the already complicated process of spike sorting (i.e. extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels.

Compartmental modeling of the hepatic transport of taurocholate in the rat in vivo

1989 ◽  
Vol 257 (2) ◽  
pp. G210-G220 ◽  
Author(s):  
X. Deroubaix ◽  
T. Coche ◽  
E. Depiereux ◽  
E. Feytmans
Keyword(s):  
Excretion Rate ◽  
Compartmental Analysis ◽  
Hepatic Transport ◽  
Lumped Model ◽  
Perfusion Model ◽  
Direct Measurements ◽  
Distribution In Blood ◽  
Variability Model ◽  
Good Agreement

Compartmental analysis was used to study the hepatobiliary transport of taurocholate (TC) in the rat in vivo. The available data are the following: [14C]TC kinetics in blood and bile, weighting factors associated with these data and computed from a theoretical variability model, and TC excretion rate in bile. The lumped model that best fits the data contains five compartments: three compartments for TC distribution in blood and two compartments for the liver. It includes a compartmental representation of the laminar flow of bile in the collecting catheter. This model overestimates TC concentration in blood. A perfusion model that includes a compartment representing explicitly the sinusoidal TC concentration gradient was developed. TC concentration in blood estimated by this model is in good agreement with direct measurements, showing that the perfused model has a better descriptive capacity than the lumped model. The amounts of TC estimated in the two hepatic compartments are similar to values previously published.

Comparison of Experimental and Numerical Simulations of Flow Within an Arterio-Venous Graft

2000 ◽  
Author(s):  
Paul F. Fischer ◽  
Seung Lee ◽  
Francis Loth ◽  
Hisham S. Bassiouny ◽  
Nurullah Arslan
Keyword(s):  
Flow Field ◽  
Steady Flow ◽  
Laser Doppler Anemometry ◽  
Transition To Turbulence ◽  
Flow Conditions ◽  
Venous Graft ◽  
Venous Anastomosis ◽  
Av Graft ◽  
Good Agreement

Abstract This was a study to compare computational and experimental results of flow field inside the venous anastomosis of an arteriovenous (AV) graft. Laser Doppler anemometry (LDA) measurements were conducted inside an upscaled end-to-side graft model under steady flow conditions at Reynolds number 1820 which is representative of the in vivo flow conditions inside a human AV graft. The distribution of the velocity and turbulence intensity was measured at several locations in the plane of the bifurcation. This flow field was simulated using computation fluid dynamics (CFD) and shown to be in good agreement. Under steady flow conditions, the flow field demonstrated an unsteady character (transition to turbulence).

scholarly journals The intellectual disability gene Kirrel3 regulates target-specific mossy fiber synapse development in the hippocampus

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
E Anne Martin ◽  
Shruti Muralidhar ◽  
Zhirong Wang ◽  
Diégo Cordero Cervantes ◽  
Raunak Basu ◽  
...  
Keyword(s):  
Synapse Formation ◽  
Mossy Fiber ◽  
Neuron Activity ◽  
Target Cells ◽  
Synaptic Changes ◽  
Mossy Fiber Synapse ◽  
Ca3 Neurons ◽  
Circuit Function ◽  
Feed Forward Inhibition

Synaptic target specificity, whereby neurons make distinct types of synapses with different target cells, is critical for brain function, yet the mechanisms driving it are poorly understood. In this study, we demonstrate Kirrel3 regulates target-specific synapse formation at hippocampal mossy fiber (MF) synapses, which connect dentate granule (DG) neurons to both CA3 and GABAergic neurons. Here, we show Kirrel3 is required for formation of MF filopodia; the structures that give rise to DG-GABA synapses and that regulate feed-forward inhibition of CA3 neurons. Consequently, loss of Kirrel3 robustly increases CA3 neuron activity in developing mice. Alterations in the Kirrel3 gene are repeatedly associated with intellectual disabilities, but the role of Kirrel3 at synapses remained largely unknown. Our findings demonstrate that subtle synaptic changes during development impact circuit function and provide the first insight toward understanding the cellular basis of Kirrel3-dependent neurodevelopmental disorders.

Sign in / Sign up

Export Citation Format

Share Document