scholarly journals Complementary networks of cortical somatostatin interneurons enforce layer specific control

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Alexander Naka ◽  
Julia Veit ◽  
Ben Shababo ◽  
Rebecca K Chance ◽  
Davide Risso ◽  
...  
Keyword(s):  
Barrel Cortex ◽  
Transcriptional Profiling ◽  
Pyramidal Cells ◽  
Top Down ◽  
Bottom Up ◽  
Sensory Inputs ◽  
Layer 4 ◽  
Apical Dendrites ◽  
Specific Control

The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.

scholarly journals Complementary networks of cortical somatostatin interneurons enforce layer specific control

2018 ◽  
Author(s):  
Alexander Naka ◽  
Julia Veit ◽  
Ben Shababo ◽  
Rebecca K. Chance ◽  
Davide Risso ◽  
...  
Keyword(s):  
Transcriptional Profiling ◽  
Neuronal Cell ◽  
Gain Control ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Top Down ◽  
Bottom Up ◽  
Sensory Data ◽  
Layer 4

AbstractThe neocortex is organized into discrete layers of excitatory neurons: layer 4 receives the densest ‘bottom up’ projection carrying external sensory data, while layers 2/3 and 5 receive ‘top down’ inputs from higher cortical areas that may convey sensory expectations and behavioral goals. A subset of cortical somatostatin (SST) neurons gate top down input and control sensory computation by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5. However, it is unknown whether an analogous inhibitory mechanism separately and specifically controls activity in layer 4. We hypothesized that distinct SST circuits might exist to inhibit specific cortical layers. By enforcing layer-specific inhibition, distinct SST subnetworks could mediate pathway-specific gain control, such as regulating the balance between bottom up and top down input. Employing a combination of high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal distinct and complementary SST circuits that specifically and reciprocally interconnect with excitatory cells in either layer 4 or layers 2/3 and 5. Our data further define a transcriptionally distinct SST neuronal sub-class that powerfully gates bottom up sensory activity during active sensation by regulating layer 4 activity. This integrated paradigm further represents a potentially generalizable approach to identify and characterize neuronal cell types and reveal their in vivo function.

Sensory regulation of quadrupedal locomotion: a top-down or bottom-up control system?

2012 ◽  
Vol 108 (3) ◽  
pp. 709-711 ◽  
Author(s):  
Yann Thibaudier ◽  
Marie-France Hurteau
Keyword(s):  
Control System ◽  
Central Pattern Generators ◽  
Top Down ◽  
Bottom Up ◽  
Sensory Inputs ◽  
Sensory Regulation ◽  
Before And After ◽  
Pattern Generators

Propriospinal pathways are thought to be critical for quadrupedal coordination by coupling cervical and lumbar central pattern generators (CPGs). However, the mechanisms involved in relaying information between girdles remain largely unexplored. Using an in vitro spinal cord preparation in neonatal rats, Juvin and colleagues ( Juvin et al. 2012 ) have recently shown sensory inputs from the hindlimbs have greater influence on forelimb CPGs than forelimb sensory inputs on hindlimb CPGs, in other words, a bottom-up control system. However, results from decerebrate cats suggest a top-down control system. It may be that both bottom-up and top-down control systems exist and that the dominance of one over the other is task or context dependent. As such, the role of sensory inputs in controlling quadrupedal coordination before and after injury requires further investigation.

(Invited) In Vitro and In Vivo Near-Infrared Imaging with Biocompatible Bottom-up and Top-Down-Synthesized Graphene Quantum Dots

2020 ◽  
Vol MA2020-01 (6) ◽  
pp. 648-648
Author(s):  
Anton V Naumov ◽  
Md Tanvir Hasan ◽  
Elizabeth Campbell ◽  
Ching-Wei Lin ◽  
Angela M. Belcher
Keyword(s):  
Quantum Dots ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Graphene Quantum Dots ◽  
Top Down ◽  
Near Infrared Imaging ◽  
Bottom Up

Fiberoptic System for Recording Dendritic Calcium Signals in Layer 5 Neocortical Pyramidal Cells in Freely Moving Rats

2007 ◽  
Vol 98 (3) ◽  
pp. 1791-1805 ◽  
Author(s):  
Masanori Murayama ◽  
Enrique Pérez-Garci ◽  
Hans-Rudolf Lüscher ◽  
Matthew E. Larkum
Keyword(s):  
Pyramidal Neurons ◽  
Calcium Influx ◽  
Signal To Noise Ratio ◽  
Pyramidal Cells ◽  
Cellular Mechanisms ◽  
Novel Approach ◽  
Specific Loading ◽  
Apical Dendrites

Calcium influx into the dendritic tufts of layer 5 neocortical pyramidal neurons modifies a number of important cellular mechanisms. It can trigger local synaptic plasticity and switch the firing properties from regular to burst firing. Due to methodological limitations, our knowledge about Ca2+ spikes in the dendritic tuft stems mostly from in vitro experiments. However, it has been speculated that regenerative Ca2+ events in the distal dendrites correlate with distinct behavioral states. Therefore it would be most desirable to be able to record these Ca2+ events in vivo, preferably in the behaving animal. Here, we present a novel approach for recording Ca2+ signals in the dendrites of populations of layer 5 pyramidal neurons in vivo, which ensures that all recorded fluorescence changes are due to intracellular Ca2+ signals in the apical dendrites. The method has two main features: 1) bolus loading of layer 5 with a membrane-permeant Ca2+ dye resulting in specific loading of pyramidal cell dendrites in the upper layers and 2) a fiberoptic cable attached to a gradient index lens and a prism reflecting light horizontally at 90° to the angle of the apical dendrites. We demonstrate that the in vivo signal-to-noise ratio recorded with this relatively inexpensive and easy-to-implement fiberoptic-based device is comparable to conventional camera-based imaging systems used in vitro. In addition, the device is flexible and lightweight and can be used for recording Ca2+ signals in the distal dendritic tuft of freely behaving animals.

scholarly journals Prior expectations induce pre-stimulus sensory templates

2017 ◽  
Author(s):  
Peter Kok ◽  
Pim Mostert ◽  
Floris P. de Lange
Keyword(s):  
Sensory Neurons ◽  
Temporal Resolution ◽  
Representational Content ◽  
Sensory Cortex ◽  
Top Down ◽  
Neural Signals ◽  
Neural Signal ◽  
Bottom Up ◽  
Time Resolved ◽  
Sensory Inputs

AbstractPerception can be described as a process of inference, integrating bottom-up sensory inputs and top-down expectations. However, it is unclear how this process is neurally implemented. It has been proposed that expectations lead to pre-stimulus baseline increases in sensory neurons tuned to the expected stimulus, which in turn affects the processing of subsequent stimuli. Recent fMRI studies have revealed stimulus-specific patterns of activation in sensory cortex as a result of expectation, but this method lacks the temporal resolution necessary to distinguish pre- from post-stimulus processes. Here, we combined human MEG with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner. We observed a representation of expected stimuli in the neural signal well before they were presented, demonstrating that expectations indeed induce a pre-activation of stimulus templates. These results suggest a mechanism for how predictive perception can be neurally implemented.

scholarly journals Comprehensive mapping of whisker-evoked responses reveals broad, sharply tuned thalamocortical input to layer 4 of barrel cortex

2011 ◽  
Vol 105 (5) ◽  
pp. 2421-2437 ◽  
Author(s):  
Noah C. Roy ◽  
Thomas Bessaih ◽  
Diego Contreras
Keyword(s):  
Cortical Neurons ◽  
Barrel Cortex ◽  
Current Source ◽  
Field Potential ◽  
Fundamental Information ◽  
Layer 4 ◽  
Density Analysis ◽  
Evoked Activity ◽  
Rats And Mice

Cortical neurons are organized in columns, distinguishable by their physiological properties and input-output organization. Columns are thought to be the fundamental information-processing modules of the cortex. The barrel cortex of rats and mice is an attractive model system for the study of cortical columns, because each column is defined by a layer 4 (L4) structure called a barrel, which can be clearly visualized. A great deal of information has been collected regarding the connectivity of neurons in barrel cortex, but the nature of the input to a given L4 barrel remains unclear. We measured this input by making comprehensive maps of whisker-evoked activity in L4 of rat barrel cortex using recordings of multiunit activity and current source density analysis of local field potential recordings of animals under light isoflurane anesthesia. We found that a large number of whiskers evoked a detectable response in each barrel (mean of 13 suprathreshold, 18 subthreshold) even after cortical activity was abolished by application of muscimol, a GABAA agonist. We confirmed these findings with intracellular recordings and single-unit extracellular recordings in vivo. This constitutes the first direct confirmation of the hypothesis that subcortical mechanisms mediate a substantial multiwhisker input to a given cortical barrel.

Electrophysiological Classes of Cat Primary Visual Cortical Neurons In Vivo as Revealed by Quantitative Analyses

2003 ◽  
Vol 89 (3) ◽  
pp. 1541-1566 ◽  
Author(s):  
Lionel G. Nowak ◽  
Rony Azouz ◽  
Maria V. Sanchez-Vives ◽  
Charles M. Gray ◽  
David A. McCormick
Keyword(s):  
Short Duration ◽  
Cortical Neurons ◽  
Action Potentials ◽  
Low Frequency ◽  
Pyramidal Cells ◽  
Spike Frequency ◽  
Spike Frequency Adaptation ◽  
Frequency Adaptation ◽  
Layer 4

To facilitate the characterization of cortical neuronal function, the responses of cells in cat area 17 to intracellular injection of current pulses were quantitatively analyzed. A variety of response variables were used to separate the cells into subtypes using cluster analysis. Four main classes of neurons could be clearly distinguished: regular spiking (RS), fast spiking (FS), intrinsic bursting (IB), and chattering (CH). Each of these contained significant subclasses. RS neurons were characterized by trains of action potentials that exhibited spike frequency adaptation. Morphologically, these cells were spiny stellate cells in layer 4 and pyramidal cells in layers 2, 3, 5, and 6. FS neurons had short-duration action potentials (<0.5 ms at half height), little or no spike frequency adaptation, and a steep relationship between injected current intensity and spike discharge frequency. Morphologically, these cells were sparsely spiny or aspiny nonpyramidal cells. IB neurons typically generated a low frequency (<425 Hz) burst of spikes at the beginning of a depolarizing current pulse followed by a tonic train of action potentials for the remainder of the pulse. These cells were observed in all cortical layers, but were most abundant in layer 5. Finally, CH neurons generated repetitive, high-frequency (350–700 Hz) bursts of short-duration (<0.55 ms) action potentials. Morphologically, these cells were layer 2–4 (mainly layer 3) pyramidal or spiny stellate neurons. These results indicate that firing properties do not form a continuum and that cortical neurons are members of distinct electrophysiological classes and subclasses.

scholarly journals Learning from unexpected events in the neocortical microcircuit

2021 ◽  
Author(s):  
Colleen J. Gillon ◽  
Jason E. Pina ◽  
Jérôme A. Lecoq ◽  
Ruweida Ahmed ◽  
Yazan Billeh ◽  
...  
Keyword(s):  
Pyramidal Neurons ◽  
Top Down ◽  
Unexpected Events ◽  
Unexpected Event ◽  
Bottom Up ◽  
Two Photon ◽  
Cortical Responses ◽  
To Receive ◽  
Hierarchical Computation ◽  
Apical Dendrites

AbstractScientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. To do so, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of data. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. Such differences in cortical responses have been observed; however, it remains unknown whether these unexpected event signals govern subsequent changes in the brain’s stimulus representations, and, thus, govern learning. Here, we show that unexpected event signals predict subsequent changes in responses to expected and unexpected stimuli in individual neurons and distal apical dendrites that are tracked over a period of days. These findings were obtained by observing layer 2/3 and layer 5 pyramidal neurons in primary visual cortex of awake, behaving mice using two-photon calcium imaging. We found that many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. These unexpected event signals also determined how the responses evolved over subsequent days, in a manner that was different between the somata and distal apical dendrites. This difference between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively. Together, our results provide novel evidence that the neocortex indeed instantiates a predictive hierarchical model in which unexpected events drive learning.

Neocortical Layer 1: An Elegant Solution to Top-Down and Bottom-Up Integration

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Benjamin Schuman ◽  
Shlomo Dellal ◽  
Alvar Prönneke ◽  
Robert Machold ◽  
Bernardo Rudy
Keyword(s):  
Pyramidal Cells ◽  
Apical Dendrite ◽  
Annual Review ◽  
Publication Date ◽  
Top Down ◽  
Bottom Up ◽  
Violin Concerto ◽  
States Of Consciousness ◽  
The Brain ◽  
Tennis Match

Many of our daily activities, such as riding a bike to work or reading a book in a noisy cafe, and highly skilled activities, such as a professional playing a tennis match or a violin concerto, depend upon the ability of the brain to quickly make moment-to-moment adjustments to our behavior in response to the results of our actions. Particularly, they depend upon the ability of the neocortex to integrate the information provided by the sensory organs (bottom-up information) with internally generated signals such as expectations or attentional signals (top-down information). This integration occurs in pyramidal cells (PCs) and their long apical dendrite, which branches extensively into a dendritic tuft in layer 1 (L1). The outermost layer of the neocortex, L1 is highly conserved across cortical areas and species. Importantly, L1 is the predominant input layer for top-down information, relayed by a rich, dense mesh of long-range projections that provide signals to the tuft branches of the PCs. Here, we discuss recent progress in our understanding of the composition of L1 and review evidence that L1 processing contributes to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A Paradigm Shift in Tissue Engineering: From a Top–Down to a Bottom–Up Strategy

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 935
Author(s):  
Theresa Schmidt ◽  
Yu Xiang ◽  
Xujin Bao ◽  
Tao Sun
Keyword(s):  
Tissue Engineering ◽  
Cell Types ◽  
Clinical Applications ◽  
Organ Shortage ◽  
Top Down ◽  
Bottom Up ◽  
Multiple Cell ◽  
Size And Morphology ◽  
Modular Scaffolds

Tissue engineering (TE) was initially designed to tackle clinical organ shortage problems. Although some engineered tissues have been successfully used for non-clinical applications, very few (e.g., reconstructed human skin) have been used for clinical purposes. As the current TE approach has not achieved much success regarding more broad and general clinical applications, organ shortage still remains a challenging issue. This very limited clinical application of TE can be attributed to the constraints in manufacturing fully functional tissues via the traditional top–down approach, where very limited cell types are seeded and cultured in scaffolds with equivalent sizes and morphologies as the target tissues. The newly proposed developmental engineering (DE) strategy towards the manufacture of fully functional tissues utilises a bottom–up approach to mimic developmental biology processes by implementing gradual tissue assembly alongside the growth of multiple cell types in modular scaffolds. This approach may overcome the constraints of the traditional top–down strategy as it can imitate in vivo-like tissue development processes. However, several essential issues must be considered, and more mechanistic insights of the fundamental, underpinning biological processes, such as cell–cell and cell–material interactions, are necessary. The aim of this review is to firstly introduce and compare the number of cell types, the size and morphology of the scaffolds, and the generic tissue reconstruction procedures utilised in the top–down and the bottom–up strategies; then, it will analyse their advantages, disadvantages, and challenges; and finally, it will briefly discuss the possible technologies that may overcome some of the inherent limitations of the bottom–up strategy.

Sign in / Sign up

Export Citation Format

Share Document