Parallel Neural Multiprocessing with Gamma Frequency Latencies

2020 ◽  
Vol 32 (9) ◽  
pp. 1635-1663
Author(s):  
Ruohan Zhang ◽  
Dana H. Ballard
Keyword(s):  
Cortical Cell ◽  
Spike Timing ◽  
System Level ◽  
Neural Responses ◽  
Gamma Frequency ◽  
Neural Processes ◽  
Rate Coding ◽  
Millisecond Range ◽  
Cortex System ◽  
Cell Data

The Poisson variability in cortical neural responses has been typically modeled using spike averaging techniques, such as trial averaging and rate coding, since such methods can produce reliable correlates of behavior. However, mechanisms that rely on counting spikes could be slow and inefficient and thus might not be useful in the brain for computations at timescales in the 10 millisecond range. This issue has motivated a search for alternative spike codes that take advantage of spike timing and has resulted in many studies that use synchronized neural networks for communication. Here we focus on recent studies that suggest that the gamma frequency may provide a reference that allows local spike phase representations that could result in much faster information transmission. We have developed a unified model (gamma spike multiplexing) that takes advantage of a single cycle of a cell's somatic gamma frequency to modulate the generation of its action potentials. An important consequence of this coding mechanism is that it allows multiple independent neural processes to run in parallel, thereby greatly increasing the processing capability of the cortex. System-level simulations and preliminary analysis of mouse cortical cell data are presented as support for the proposed theoretical model.

scholarly journals Cortical spike multiplexing using gamma frequency latencies

2018 ◽  
Author(s):  
Dana H. Ballard ◽  
Ruohan Zhang
Keyword(s):  
Action Potentials ◽  
New Technologies ◽  
Signal Transmission ◽  
Cortical Cell ◽  
General Purpose ◽  
Spike Timing ◽  
Population Coding ◽  
Cortical Cells ◽  
Gamma Frequency

AbstractOne of the fundamental problems in understanding the brain, in particular the cerebral cortex, is that we only have a partial understanding of the basic communication protocols that underlie signal transmission. This makes it difficult to interpret the significance of particular phenomena such as basic firing patterns and oscillations at different frequencies. There are, of course, useful models. Motivated by single-cell recording technology, Poisson statistics of cortical action potentials have long been a basic component in models of signal representation in the cortex. However, it is increasingly difficult to integrate Poisson spiking with spike timing signals in the gamma frequency spectrum. A potential way forward is being sparked by new technologies that allow the exploration of very low-level communication strategies. Specifically, the voltage potential of a cell’s soma now can be recorded with very high fidelity in vivo, allowing correlation of its fine structure to be correlated with behaviors. To interpret this data, we have developed a unified model (gamma spike multiplexing, or GSM) wherein a cell’s somatic gamma frequencies can modulate the generation of action potentials. Such spikes can be seen as the basis for a general-purpose method of modulating fast communication in cortical networks. In particular, the model has several important advantages over traditional formalisms: 1) It allows multiple, independent processes to run in parallel, greatly increasing the processing capability of the cortex 2) Its processing speed is 102 to 103 times faster than population coding methods 3) Its processes are not bound to specific locations, but migrate across cortical cells as a function of time, facilitating the maintenance of cortical cell calibration.

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

2021 ◽  
Vol 17 (4) ◽  
pp. 1-26
Author(s):  
Md Musabbir Adnan ◽  
Sagarvarma Sayyaparaju ◽  
Samuel D. Brown ◽  
Mst Shamim Ara Shawkat ◽  
Catherine D. Schuman ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Recognition Task ◽  
Single Layer ◽  
Process Variations ◽  
Spike Timing ◽  
System Level ◽  
Design Parameters ◽  
Digital Pulse ◽  
Neuromorphic System ◽  
The Impact

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

scholarly journals Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 500 ◽  
Author(s):  
Sergey A. Lobov ◽  
Andrey V. Chernyshov ◽  
Nadia P. Krilova ◽  
Maxim O. Shamshin ◽  
Victor B. Kazantsev
Keyword(s):  
Supervised Learning ◽  
Hebbian Learning ◽  
Back Propagation ◽  
Spike Timing ◽  
Temporal Coding ◽  
Competitive Learning ◽  
Neuron Activity ◽  
Synaptic Competition ◽  
Rate Coding ◽  
Emg Patterns

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

2003 ◽  
Vol 15 (10) ◽  
pp. 2399-2418 ◽  
Author(s):  
Zhao Songnian ◽  
Xiong Xiaoyun ◽  
Yao Guozheng ◽  
Fu Zhi
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Visual Information ◽  
Spike Trains ◽  
Spike Timing ◽  
Contour Detection ◽  
System Level ◽  
Neuronal Spike ◽  
Neuronal Spike Trains ◽  
External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

scholarly journals Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

Distinct neural mechanisms of emotional processing in prolonged grief disorder

2020 ◽  
pp. 1-9 ◽  
Author(s):  
Richard A. Bryant ◽  
Elpiniki Andrew ◽  
Mayuresh S. Korgaonkar
Keyword(s):  
Emotional Processing ◽  
Anterior Cingulate ◽  
Neural Responses ◽  
Major Depressive ◽  
Prolonged Grief Disorder ◽  
Prolonged Grief ◽  
Neural Processes ◽  
Dorsolateral Prefrontal ◽  
Subliminal Processing ◽  
The Brain

Abstract Background Prolonged grief disorder (PGD) has recently been recognized as a separate psychiatric diagnosis, despite controversy over the extent to which it is distinctive from posttraumatic stress disorder (PTSD) and major depressive disorder (MDD). Methods This study investigated distinctive neural processes underpinning emotion processing in participants with PGD, PTSD, and MDD with functional magnetic resonance study of 117 participants that included PGD (n = 21), PTSD (n = 45), MDD (n = 26), and bereaved controls (BC) (n = 25). Neural responses were measured across the brain while sad, happy, or neutral faces were presented at both supraliminal and subliminal levels. Results PGD had greater activation in the pregenual anterior cingulate cortex (pgACC), bilateral insula, bilateral dorsolateral prefrontal cortices and right caudate and also greater pgACC–right pallidum connectivity relative to BC during subliminal processing of happy faces. PGD was distinct relative to both PTSD and MDD groups with greater recruitment of the medial orbitofrontal cortex during supraliminal processing of sad faces. PGD were also distinct relative to MDD (but not PTSD) with greater activation in the left amygdala, caudate, and putamen during subliminal presentation of sad faces. There was no distinction between PGD, PTSD, and MDD during processing of happy faces. Conclusions These results provide initial evidence of distinct neural profiles of PGD relative to related psychopathological conditions, and highlight activation of neural regions implicated in reward networks. This pattern of findings validates current models of PGD that emphasize the roles of yearning and appetitive processes in PGD.

scholarly journals Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mingqi Zhao ◽  
Marco Marino ◽  
Jessica Samogin ◽  
Stephan P. Swinnen ◽  
Dante Mantini
Keyword(s):  
Sensorimotor Cortex ◽  
High Spatial Resolution ◽  
Movement Control ◽  
Contralateral Side ◽  
High Density ◽  
The Body ◽  
Body Parts ◽  
Gamma Frequency ◽  
Neural Processes ◽  
Primary Sensorimotor Cortex

AbstractThe primary sensorimotor cortex plays a major role in the execution of movements of the contralateral side of the body. The topographic representation of different body parts within this brain region is commonly investigated through functional magnetic resonance imaging (fMRI). However, fMRI does not provide direct information about neuronal activity. In this study, we used high-density electroencephalography (hdEEG) to map the representations of hand, foot, and lip movements in the primary sensorimotor cortex, and to study their neural signatures. Specifically, we assessed the event-related desynchronization (ERD) in the cortical space. We found that the performance of hand, foot, and lip movements elicited an ERD in beta and gamma frequency bands. The primary regions showing significant beta- and gamma-band ERD for hand and foot movements, respectively, were consistent with previously reported using fMRI. We observed relatively weaker ERD for lip movements, which may be explained by the fact that less fine movement control was required. Overall, our study demonstrated that ERD based on hdEEG data can support the study of motor-related neural processes, with relatively high spatial resolution. An interesting avenue may be the use of hdEEG for deeper investigations into the pathophysiology of neuromotor disorders.

scholarly journals Contribution of self- and other-regarding motives to (dis)honesty

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Anastasia Shuster ◽  
Dino J. Levy
Keyword(s):  
Individual Differences ◽  
Neural Responses ◽  
Potential Harm ◽  
Self And Other ◽  
Potential Value ◽  
Neural Processes ◽  
Self Interest ◽  
Harm To Others ◽  
Potential Losses ◽  
Other Regarding

Abstract Why would people tell the truth when there is an obvious gain in lying and no risk of being caught? Previous work suggests the involvement of two motives, self-interest and regard for others. However, it remains unknown if these motives are related or distinctly contribute to (dis)honesty, and what are the neural instantiations of these motives. Using a modified Message Game task, in which a Sender sends a dishonest (yet profitable) or honest (less profitable) message to a Receiver, we found that these two motives contributed to dishonesty independently. Furthermore, the two motives involve distinct brain networks: the LPFC tracked potential value to self, whereas the rTPJ tracked potential losses to other, and individual differences in motives modulated these neural responses. Finally, activity in the vmPFC represented a balance of the two motives unique to each participant. Taken together, our results suggest that (dis)honest decisions incorporate at least two separate cognitive and neural processes—valuation of potential profits to self and valuation of potential harm to others.

Duality of Rate Coding and Temporal Coding in Multilayered Feedforward Networks

2003 ◽  
Vol 15 (1) ◽  
pp. 103-125 ◽  
Author(s):  
Naoki Masuda ◽  
Kazuyuki Aihara
Keyword(s):  
Alternative Hypothesis ◽  
Spike Timing ◽  
Temporal Coding ◽  
Model Parameters ◽  
Synchronous Firing ◽  
Feedback Strength ◽  
Common Framework ◽  
Rate Coding ◽  
Single Neural Network ◽  
Population Rate

A functional role for precise spike timing has been proposed as an alternative hypothesis to rate coding. We show in this article that both the synchronous firing code and the population rate code can be used dually in a common framework of a single neural network model. Furthermore, these two coding mechanisms are bridged continuously by several modulatable model parameters, including shared connectivity, feedback strength, membrane leak rate, and neuron heterogeneity. The rates of change of these parameters are closely related to the response time and the timescale of learning.

Memory Encoding by Theta Phase Precession in the Hippocampal Network

2003 ◽  
Vol 15 (10) ◽  
pp. 2379-2397 ◽  
Author(s):  
Naoyuki Sato ◽  
Yoko Yamaguchi
Keyword(s):  
Spike Timing ◽  
Temporal Sequence ◽  
Memory Storage ◽  
Trial Experience ◽  
Phase Precession ◽  
Rate Coding ◽  
Hippocampal Network ◽  
Theta Phase ◽  
Theta Phase Precession

Recent experimental evidence on spike-timing-dependent plasticity and on phase precession (i.e., the theta rhythm dependent firing of rat hippocampalcells) associates the contribution of phase precession to episodic memory. This article aims at clarifying the role of phase precession in memory storage. Computer simulations show that the memory storage in the behavioral timescale varies in timescale of the temporal sequence from half a second to several seconds. In contrast, the memory storage caused by traditional rate coding is restricted to the temporal sequence within 40 ms. During phase precession, memory storage of a single trial experience is possible, even in the presence of noise. It is therefore concluded that encoding by phase precession is appropriate for memory storage of the temporal sequence in the behavioral timescale.

Sign in / Sign up

Export Citation Format

Share Document