scholarly journals Distributed Dynamical Computation in Neural Circuits with Propagating Coherent Activity Patterns

2009 ◽  
Vol 5 (12) ◽  
pp. e1000611 ◽  
Author(s):  
Pulin Gong ◽  
Cees van Leeuwen
Keyword(s):  
Neural Circuits ◽  
Activity Patterns

scholarly journals Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Theoretical Studies ◽  
Pattern Completion ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Brain States ◽  
Associative Function

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

scholarly journals Neuronal networks in the spotlight

e-Neuroforum ◽  
2013 ◽  
Vol 19 (2) ◽  
Author(s):  
F. Helmchen ◽  
M. Hübener
Keyword(s):  
Neuronal Networks ◽  
Neural Circuits ◽  
Calcium Influx ◽  
Fluorescent Proteins ◽  
Activity Patterns ◽  
Cell Activity ◽  
Movement Control ◽  
Dynamic Activity ◽  
Two Photon Microscopy

AbstractThe brain’s astounding achievements regard­ing movement control and sensory process­ing are based on complex spatiotemporal ac­tivity patterns in the relevant neuronal net­works. Our understanding of neuronal net­work activity is, however, still poor, not least because of the experimental difficulties in di­rectly observing neural circuits at work in the living brain (in vivo). Over the last decade, new opportunities have emerged-especial­ly utilizing two-photon microscopy-to in­vestigate neuronal networks in action. Cen­tral to this progress was the development of fluorescent proteins that change their emis­sion depending on cell activity, enabling the visualization of dynamic activity patterns in local neuronal populations. Currently, genet­ically encoded calcium indicators, proteins that indicate neuronal activity based on ac­tion potential-evoked calcium influx, are be­ing increasingly used. Long-term expression of these indicators allows repeated moni­toring of the same neurons over weeks and months, such that the stability and plastici­ty of their functional properties can be char­acterized. Furthermore, permanent indicator expression facilitates the correlation of cel­lular activity patterns and behavior in awake animals. Using examples from recent studies of information processing in the mouse neo­cortex, we review in this article these fasci­nating new possibilities and discuss the great potential of the fluorescent proteins to eluci­date the mysteries of neural circuits.

Wipe and flexion reflexes of the frog. I. Kinematics and EMG patterns

1993 ◽  
Vol 69 (5) ◽  
pp. 1725-1735 ◽  
Author(s):  
J. L. Schotland ◽  
W. Z. Rymer
Keyword(s):  
Neural Control ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Activity Patterns ◽  
Three Dimensional ◽  
Building Blocks ◽  
Lateral Position ◽  
Movement Trajectories ◽  
Ankle Joints ◽  
Emg Patterns

1. We evaluated the hypothesis that the neural control of complex motor behaviors is simplified by building movement sequences from a series of simple neural "building blocks." In particular, we compared two reflex behaviors of the frog, flexion withdrawal and the hindlimb-hindlimb wipe reflex, to determine whether a single neural circuit that coordinates flexion withdrawal is incorporated as the first element in a sequence of neural circuits comprising the wipe. The neural organization of these two reflexes was compared using a quantitative analysis of movement kinematics and muscle activity patterns [electromyograms (EMGs)]. 2. The three-dimensional coordinates of the position of the foot over time and the angular excursion of hip, knee, and ankle joints were recorded using a WATSMART infrared emitter-detector system. These data were quantified using principal-components analysis to provide a measure of the shape (eigenvalues) and orientation (eigen-vector coefficients) of the movement trajectories. The latencies and magnitudes of EMGs of seven muscles acting at the hip, knee, and ankle were analyzed over the interval from EMG onset to movement onset, and EMG magnitudes during the initial flexion of the limb. These variables were compared during flexion withdrawal and the initial flexion movement of the limb during the hindlimb-hindlimb wipe reflex (before the onset of the frequently rhythmic portion when the stimulus is removed) when the two reflexes were elicited from comparable stimulus locations. 3. In both the flexion reflex and the initial movement segment of the wipe reflex, the foot moves along a relatively straight line. However, the foot is directed to a more rostral and lateral position during flexion than during wipe. All three joints flex during flexion withdrawal, whereas during the wipe, the knee and ankle joints flex but the angular excursion of the hip joint may vary. The different orientations of the movement trajectories are associated with EMG patterns that differ in both timing and magnitude between the two reflexes. 4. The differences in the kinematics and EMG patterns of the two reflexes during unrestrained movements make it unlikely that the neural circuit that coordinates flexion withdrawal is incorporated as the first element in the sequence of neural circuits underlying the wipe reflex. 5. Unlike the wipe reflex, during flexion withdrawal there is no apparent constraint on the accuracy of placement at the end of the movement, yet the animals nevertheless achieved consistent final positions of both the foot and of each joint. The implications of these findings with respect to the controlled variables are discussed.

scholarly journals An analog of psychedelics restores functional neural circuits disrupted by unpredictable stress

2021 ◽  
Author(s):  
Ju Lu ◽  
Michelle Tjia ◽  
Brian Mullen ◽  
Bing Cao ◽  
Kacper Lukasiewicz ◽  
...  
Keyword(s):  
Somatosensory Cortex ◽  
Neuronal Activity ◽  
Sensory Processing ◽  
Cognitive Deficits ◽  
Neural Circuits ◽  
Wide Spectrum ◽  
Activity Patterns ◽  
Mild Stress ◽  
Brain Functions ◽  
Post Stress

AbstractPsychological stress affects a wide spectrum of brain functions and poses risks for many mental disorders. However, effective therapeutics to alleviate or revert its deleterious effects are lacking. A recently synthesized psychedelic analog tabernanthalog (TBG) has demonstrated anti-addictive and antidepressant potential. Whether TBG can rescue stress-induced affective, sensory, and cognitive deficits, and how it may achieve such effects by modulating neural circuits, remain unknown. Here we show that in mice exposed to unpredictable mild stress (UMS), administration of a single dose of TBG decreases their anxiety level and rescues deficits in sensory processing as well as in cognitive flexibility. Post-stress TBG treatment promotes the regrowth of excitatory neuron dendritic spines lost during UMS, decreases the baseline neuronal activity, and enhances whisking-modulation of neuronal activity in the somatosensory cortex. Moreover, calcium imaging in head-fixed mice performing a whisker-dependent texture discrimination task shows that novel textures elicit responses from a greater proportion of neurons in the somatosensory cortex than do familiar textures. Such differential response is diminished by UMS and is restored by TBG. Together, our study reveals the effects of UMS on cortical neuronal circuit activity patterns and demonstrate that TBG combats the detrimental effects of stress by modulating basal and stimulus-dependent neural activity in cortical networks.

scholarly journals 2θ-Burster for Rhythm-Generating Circuits

2020 ◽  
Vol 6 ◽  
Author(s):  
Aaron Kelley ◽  
Andrey Shilnikov
Keyword(s):  
Dynamical System ◽  
Neural Networks ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Network Dynamics ◽  
Slow Phase ◽  
System Framework ◽  
Computation Cost ◽  
Return Maps ◽  
Phase Lags

We propose a minimalistic model called the 2θ-burster due to two slow phase characteristics of endogenous bursters, which when coupled in 3-cell neural circuits generate a multiplicity of stable rhythmic outcomes. This model offers the benefits of simplicity for designing larger neural networks along with an acute reduction in the computation cost. We developed a dynamical system framework for explaining the existence and robustness of phase-locked states in activity patterns produced by small rhythmic neural circuits. Several 3-cell configurations, from multifunctional to monostable, are considered to demonstrate the versatility of the proposed approach, allowing the network dynamics to be reduced to the examination of 2D Poincaré return maps for the phase lags between three constituent 2θ-bursters.

scholarly journals A model of flexible motor sequencing through thalamic control of cortical dynamics

2019 ◽  
Author(s):  
Laureline Logiaco ◽  
L.F. Abbott ◽  
Sean Escola
Keyword(s):  
Basal Ganglia ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Thalamic Neurons ◽  
Cortical Dynamics ◽  
Sustained Activity ◽  
Electrophysiological Recordings ◽  
Complex Motor ◽  
Output Neurons ◽  
Noise Robust

AbstractThe mechanisms by which neural circuits generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. During movement sequences, electrophysiological recordings of basal ganglia output neurons show sustained activity patterns that switch at the boundaries between motifs. Thus, we model these inhibitory patterns as silencing some thalamic neurons while leaving others disinhibited and free to interact with cortex during specific motifs. We show that a small number of disinhibited thalamic neurons can control cortical dynamics to generate specific motor output in a noise robust way. If the thalamic units associated with each motif are segregated, many motor outputs can be learned without interference and then combined in arbitrary orders for the flexible production of long and complex motor sequences.

scholarly journals Learning probabilistic representations with randomly connected neural circuits

2018 ◽  
Author(s):  
Ori Maoz ◽  
Gašper Tkacčik ◽  
Mohamad Saleh Esteki ◽  
Roozbeh Kiani ◽  
Elad Schneidman
Keyword(s):  
Structural Changes ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Learning Rule ◽  
Complex Stimuli ◽  
Neural Computations ◽  
Motor Actions ◽  
Neuronal Computation ◽  
Sparse Connectivity ◽  
Random Connectivity

AbstractThe brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficiently learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable or better than that of current models. Importantly, the model can be learned using a small number of samples, and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

Neuronale Netzwerke im Rampenlicht: Mit leuchtenden Proteinen zelluläre Aktivitätsmuster entschlüsseln

e-Neuroforum ◽  
2013 ◽  
Vol 19 (2) ◽  
Author(s):  
Fritjof Helmchen ◽  
Mark Hübener
Keyword(s):  
Neuronal Networks ◽  
Neural Circuits ◽  
Calcium Influx ◽  
Fluorescent Proteins ◽  
Activity Patterns ◽  
Cell Activity ◽  
Network Activity ◽  
Cellular Activity ◽  
Dynamic Activity ◽  
Neuronal Network Activity

AbstractNeuronal networks in the spotlight: deciphering cellular activity patterns with fluo­rescent proteins.The brain’s astounding achievements regarding movement control and sensory pro­cessing are based on complex spatiotemporal activity patterns in the relevant neuronal networks. Our understanding of neuronal network activity is, however, still poor, not least because of the experimental difficulties to directly observe neural circuits at work in the living brain (in vivo). Over the last decade, new opportunities have emerged - especially utilizing 2-photon microscopy - to investigate neuronal networks in action. Central to this progress was the development of fluorescent proteins that change their emission depending on cell activity, enabling the visualization of dynamic activity pat­terns in local neuronal populations. Currently, genetically encoded calcium indicators, proteins which indicate neuronal activity based on action potential-evoked calcium influx, are becoming increasingly used. Long-term expression of these indicators allows repeated monitoring of the same neurons over weeks and months, such that stability and plasticity of their functional properties can be characterized. Furthermore, permanent indicator expression facilitates the correlation of cellular activity patterns and behavior in awake animals. Using examples from recent studies of information processing in mouse neocortex, we review in this article these fascinating new possibilities and discuss the great potential of fluorescent proteins to elucidate the mysteries of neural circuits.

scholarly journals Understanding neural circuit development through theory and models

2017 ◽  
Author(s):  
Leonidas M. A. Richter ◽  
Julijana Gjorgjieva
Keyword(s):  
Quantitative Methods ◽  
Computational Models ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Activity Patterns ◽  
Data Sets ◽  
Topographic Map ◽  
Theoretical And Computational Models ◽  
Classical Models ◽  
Sets Theory

AbstractHow are neural circuits organized and tuned to achieve stable function and produce robust behavior? The organization process begins early in development and involves a diversity of mechanisms unique to this period. We summarize recent progress in theoretical neuroscience that has substantially contributed to our understanding of development at the single neuron, synaptic and network level. We go beyond classical models of topographic map formation, and focus on the generation of complex spatiotemporal activity patterns, their role in refinements of particular circuit features, and the emergence of functional computations. Aided by the development of novel quantitative methods for data analysis, theoretical and computational models have enabled us to test the adequacy of specific assumptions, explain experimental data and propose testable hypotheses. With the accumulation of larger data sets, theory and models will likely play an even more important role in understanding the development of neural circuits.

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