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作者： Štroffek, Július Charles University of Prague

This work presents biologically motivated neural network model which works as an auto-associative memory. Architecture of the presented model is similar to the architecture of the Hopfield network which might be similar to some parts of the hippocampal network area CA3 (Cornu Amonis). Patterns learned and retrieved are not static but they are periodically repeating sequences of sparse synchronous activities. Patterns were stored to the network using the modified Hebb rule adjusted to store cyclic sequences. Capacity of the model is analyzed together with the numerical simulations. The model is further extended with short term potentiation (STP), which is forming the essential part of the successful pattern recall process. The memory capacity of the extended version of the model is highly increased. The joint version of the model combining both approaches is discussed. The model might be able to retrieve the pattern in short time interval without STP (fast patterns) or in a longer time period utilizing STP (slow patterns). We know from our everyday life that some patterns could be recalled promptly and some may need much longer time to reveal. Keywords auto-associative neural network, Hebbian learning, neural coding, memory, pattern recognition, short-term potentiation 1

关键词： auto-associative neural network Hebbian learning neural coding memory pattern recognition short-term potentiation

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Modeling stimulus-dependent variability improves decoding of population neural responses

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JOURNAL OF neural ENGINEERING 2019年第6期16卷 066018-066018页

作者： Ghanbari, Abed Lee, Christopher M. Read, Heather L. Stevenson, Ian H. Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Univ Connecticut Dept Psychol Sci Storrs CT 06269 USA Connecticut Inst Brain & Cognit Sci Storrs CT 06269 USA

Objective. neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. Approach. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor??=??1). Here we use two alternative models?the Conway?Maxwell?Poisson (CMP) model and negative binomial (NB) model?to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor??>??1) or, for the CMP model, less than the mean (Fano factor??neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%?12% compared to the Poisson model. Significance. Moreover, the uncertainty of the non-Poisson decoders more accu

关键词： neural coding neural variability generalized linear model

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Information representation in an oscillating neural field model modulated by working memory signals

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2024年 17卷 1253234页

作者： Nesse, William H. Clark, Kelsey L. Noudoost, Behrad Univ Utah Dept Math Salt Lake City UT 84112 USA Univ Utah Dept Ophthalmol Salt Lake City UT USA

We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus feature-on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation-the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.

关键词： neural coding phase information theory working memory computational model

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Modern Machine Learning as a Benchmark for Fitting neural Responses

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2018年 12卷 56页

作者： Benjamin, Ari S. Fernandes, Hugo L. Tomlinson, Tucker Ramkumar, Pavan VerSteeg, Chris Chowdhury, Raeed H. Miller, Lee E. Kording, Konrad P. Univ Penn Dept Bioengn Philadelphia PA 19104 USA Northwestern Univ Dept Phys Med & Rehabil Rehabil Inst Chicago Chicago IL 60611 USA Northwestern Univ Dept Physiol Chicago IL 60611 USA Northwestern Univ Dept Neurobiol Evanston IL USA Northwestern Univ Dept Biomed Engn Evanston IL 60208 USA Univ Penn Dept Neurosci Philadelphia PA 19104 USA

Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.

关键词： encoding models neural coding tuning curves machine learning generalized linear model GLM spike prediction

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ProbabilisticinferenceindiscretespacescanbeimplementedintonetworksofLIFneurons

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015年第JAN期9卷 13-13页

作者： Probst, Dimitri Petrovici, Mihai A. Bytschok, Ilja Bill, Johannes Pecevski, Dejan Schemmel, Johannes Meier, Karlheinz Heidelberg Univ Kirchhoff Inst Phys D-69120 Heidelberg Germany Graz Univ Technol Inst Theoret Comp Sci A-8010 Graz Austria

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

关键词： theoretical neuroscience neural coding computational neural models probabilistic models and methods Bayesian theory graphical models MCMC neuromorphic hardware

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Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2016年第AUG期10卷 81页

作者： Metzen, Michael G. Krahe, Rudiger Chacron, Maurice J. McGill Univ Dept Physiol Montreal PQ Canada McGill Univ Dept Biol Montreal PQ Canada

Neurons across sensory systems and organisms often display complex patterns of action potentials in response to sensory input. One example of such a pattern is the tendency of neurons to fire packets of action potentials (i.e., a burst) followed by quiescence. While it is well known that multiple mechanisms can generate bursts of action potentials at both the single-neuron and the network level, the functional role of burst firing in sensory processing is not so well understood to date. Here we provide a comprehensive review of the known mechanisms and functions of burst firing in processing of electrosensory stimuli in gymnotiform weakly electric fish. We also present new evidence from existing data showing that bursts and isolated spikes provide distinct information about stimulus variance. It is likely that these functional roles will be generally applicable to other systems and species.

关键词： burst firing weakly electric fish feature detection directional selectivity neural coding envelope

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Design and Implementation of a Spiking neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition

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INTERNATIONAL JOURNAL OF neural SYSTEMS 2021年第3期31卷 2050073页

作者： Rashvand, Parvaneh Ahmadzadeh, Mohammad Reza Shayegh, Farzaneh Isfahan Univ Technol Dept Elect & Comp Engn Digital Signal Proc Res Lab Esfahan *** Iran

In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the "center-surround" structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.

关键词： Spiking neural networks training algorithm neural coding image recognition IF neuron model

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More than the end: OFF response plasticity as a mnemonic signature of a sound's behavioral salience

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2022年 16卷 974264页

作者： Anandakumar, Dakshitha B. Liu, Robert C. Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA USA Emory Univ Atlanta GA 30322 USA Emory Univ Dept Biol Atlanta GA 30322 USA Emory Univ Ctr Translat Social Neurosci Atlanta GA 30322 USA

In studying how neural populations in sensory cortex code dynamically varying stimuli to guide behavior, the role of spiking after stimuli have ended has been underappreciated. This is despite growing evidence that such activity can be tuned, experience-and context-dependent and necessary for sensory decisions that play out on a slower timescale. Here we review recent studies, focusing on the auditory modality, demonstrating that this so-called OFF activity can have a more complex temporal structure than the purely phasic firing that has often been interpreted as just marking the end of stimuli. While diverse and still incompletely understood mechanisms are likely involved in generating phasic and tonic OFF firing, more studies point to the continuing post-stimulus activity serving a short-term, stimulus-specific mnemonic function that is enhanced when the stimuli are particularly salient. We summarize these results with a conceptual model highlighting how more neurons within the auditory cortical population fire for longer duration after a sound's termination during an active behavior and can continue to do so even while passively listening to behaviorally salient stimuli. Overall, these studies increasingly suggest that tonic auditory cortical OFF activity holds an echoic memory of specific, salient sounds to guide behavioral decisions.

关键词： gap detection auditory cortex working memory echoic memory plasticity OFF responses neural coding

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Optimal Localist and Distributed coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2018年 12卷 74页

作者： Masquelier, Timothee Kheradpisheh, Saeed R. Univ Toulouse 3 CNRS UMR5549 Ctr Rech Cerveau & Cognit Toulouse France Univ Seville CSIC Inst Microelect Sevilla IMSE CNM Seville Spain Kharazmi Univ Fac Math Sci & Comp Dept Comp Sci Tehran Iran

Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper (Masquelier, 2017), which was limited to localist coding. More specifically, we computed analytically the signal-to-noise ratio (SNR) of a multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire (LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson inputs. Surprisingly, when increasing the number of patterns, the SNR decreases slowly, and remains acceptable for several tens of independent patterns. In addition, we investigated whether spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimal SNR. To this aim, we simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input spike patterns, embedded in equally dense Poisson spike trains. The LIF progressively became selective to every repeating pattern with no supervision, and stopped discharging during the Poisson spike trains. Furthermore, tuning certain STDP parameters, the resulting pattern detectors were optimal. Tens of independent patterns could be learned by a single neuron using a low adaptive threshold, in contrast with previous studies, in which higher thresholds led to localist coding only. Taken together these results suggest that coincidence detection and STDP are powerful mechanisms, fully compatible with distributed coding. Yet we acknowledge that our theory is limited to single neurons, and thus also applies to feed-forward networks, but not to recurrent ones.

关键词： neural coding localist coding distributed coding coincidence detection leaky integrate-and-fire neuron spatiotemporal spike pattern unsupervised learning spike-timing-dependent plasticity (STDP)

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Spatial resolution of local field potential signals in macaque V4

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JOURNAL OF neural ENGINEERING 2020年第2期17卷 026003-026003页

作者： Foroushani, Armin Najarpour Neupane, Sujaya Pastor, Pablo De Heredia Pack, Christopher C. Sawan, Mohamad Polytech Montreal Inst Biomed Engn PolyStim Neurotechnol Lab Montreal PQ Canada McGill Univ Montreal Neurol Inst & Hosp Montreal PQ Canada Westlake Univ Sch Engn Hangzhou 310024 Peoples R China Westlake Inst Adv Study Inst Adv Study Hangzhou 310024 Peoples R China MIT McGovern Inst Brain Res 77 Massachusetts Ave Cambridge MA 02139 USA Univ Politecn Madrid Escuela Tecn Ingn Ind E-28040 Madrid Spain

Objective. An important challenge for the development of cortical visual prostheses is to generate spatially localized percepts of light, using artificial stimulation. Such percepts are called phosphenes, and the goal of prosthetic applications is to generate a pattern of phosphenes that matches the structure of the retinal image. A preliminary step in this process is to understand how the spatial positions of phosphene-like visual stimuli are encoded in the distributed activity of cortical neurons. The spatial resolution with which the distributed responses discriminate positions puts a limit on the capability of visual prosthesis devices to induce phosphenes at multiple positions. While most previous prosthetic devices have targeted the primary visual cortex, the extrastriate cortex has the advantage of covering a large part of the visual field with a smaller amount of cortical tissue, providing the possibility of a more compact implant. Here, we studied how well ensembles of Local Field Potentials (LFPs) and Multiunit activity (MUA) responses from extrastriate cortical visual area V4 of a behaving macaque monkey can discriminate between two-dimensional spatial positions. Approach. We used support vector machines (SVM) to determine the capabilities of LFPs and MUA to discriminate responses to phosphene-like stimuli (probes) at different spatial separations. We proposed a selection strategy based on the combined responses of multiple electrodes and used the linear learning weights to find the minimum number of electrodes for fine and coarse discriminations. We also measured the contribution of correlated trial-to-trial variability in the responses to the discrimination performance for MUA and LFP. Main results. We found that despite the large receptive field sizes in V4, the combined responses from multiple sites, whether MUA or LFP, are capable of fine and coarse discrimination of positions. Our electrode selection procedure significantly increased discrimination

关键词： spatial discrimination local field potential V4 neural coding support vector machines correlation cortical visual prosthesis

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