scholarly journals Understanding neural circuit development through theory and models

2017 ◽  
Author(s):  
Leonidas M. A. Richter ◽  
Julijana Gjorgjieva

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.

1993 ◽  
Vol 69 (5) ◽  
pp. 1725-1735 ◽  
Author(s):  
J. L. Schotland ◽  
W. Z. Rymer

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.


2021 ◽  
Author(s):  
Alexei Koulakov ◽  
Sergey Shuvaev ◽  
Anthony Zador

Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome. However, the information capacity of the genome is orders of magnitude smaller than that needed to specify the connectivity of an arbitrary brain circuit, indicating that the rules encoding circuit formation must fit through a "genomic bottleneck" as they pass from one generation to the next. Here we formulate the problem of innate behavioral capacity in the context of artificial neural networks in terms of lossy compression of the weight matrix. We find that several standard network architectures can be compressed by several orders of magnitude, yielding pre-training performance that can approach that of the fully-trained network. Interestingly, for complex but not for simple test problems, the genomic bottleneck algorithm also captures essential features of the circuit, leading to enhanced transfer learning to novel data sets. Our results suggest that compressing a neural circuit through the genomic bottleneck serves as a regularizer, enabling evolution to select simple circuits that can be readily adapted to important real-world tasks. The genomic bottleneck also suggests how innate priors can complement conventional approaches to learning in designing algorithms for artificial intelligence.


2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yunfa Fu ◽  
Jian Chen ◽  
Xin Xiong

Objective. In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach. Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results. The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance. It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.


2016 ◽  
Author(s):  
Nitin Gupta ◽  
Swikriti Saran Singh ◽  
Mark Stopfer

AbstractOscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly tested this idea in the locust olfactory system. We found that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we found that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrated that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.


eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks

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.


SLEEP ◽  
2021 ◽  
Author(s):  
Ullrich Bartsch ◽  
Laura J Corbin ◽  
Charlotte Hellmich ◽  
Michelle Taylor ◽  
Kayleigh E Easey ◽  
...  

Abstract The rs1344706 polymorphism in ZNF804A is robustly associated with schizophrenia and schizophrenia is, in turn, associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. We recruited healthy adult males with no history of psychiatric disorder from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the schizophrenia-associated ‘A’ allele (N=22) or the alternative ‘C’ allele (N=18) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequence task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. Average MST learning and sleep-dependent performance improvements were similar across genotype groups, albeit more variable in the AA group. During sleep after learning, CC participants showed increased slow-wave (SW) and spindle amplitudes, plus augmented coupling of SW activity across recording electrodes. SW and spindles in those with the AA genotype were insensitive to learning, whilst SW coherence decreased following MST training. Accordingly, NREM neurophysiology robustly predicted the degree of overnight motor memory consolidation in CC carriers, but not in AA carriers. We describe evidence that rs1344706 polymorphism in ZNF804A is associated with changes in the coordinated neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of schizophrenia-associated common genetic variants on neural circuit oscillations and function.


Author(s):  
Samantha Hughes ◽  
Tansu Celikel

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.


Author(s):  
Rinat Galiautdinov

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home-based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.


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