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Learning the contributions of the motor, premotor, and posterior parietal cortices for hand trajectory reconstruction in a brain machine interface 1

Learning the contributions of the motor, premotor, and poste...

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1st International IEEE/EMBS Conference on Neural Engineering

作者： Sanchez, JC Erdogmus, D Rao, Y Principe, JC Nicolelis, M Wessberg, J Univ Florida Dept Biomed Engn Gainesville FL 32611 USA

ISBN: (纸本)0780375793

The ability to record, in real-time, the activity of hundreds of cortical neurons gives the ability to selectively study the function of clusters of cortical neurons in Brain Machine Interface (BMI) experiments. We have demonstrated using a recursive multilayer perceptron (RMLP) that using the appropriate signal processing theory in a well-chosen parsimonious model, we can develop constructs that agree with basic physiological modeling of neural control. By looking through the trained model, we have found interesting relationships between the neuronal firing and the movement. The RMLP allows us to continuously study the relationship between neural activity and behavior without the active interference of the experimenter. The findings presented in this study offer an opportunity for the neuroscience community to compare the cortical interactions as constructed by the RMLP to what is known about motor neurophysiology.

关键词： Brain Machine Interface BMI neural control population coding recurrent neural network neural ensemble motor control

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Computing on Functions Using Randomized Vector Representations (in brief) 2022

Computing on Functions Using Randomized Vector Representatio...

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Annual Neuro-Inspired Computational Elements Conference (NICE)

作者： Frady, E. Paxon Kleyko, Denis Kymn, Christopher J. Olshausen, Bruno A. Sommer, Friedrich T. Intel Labs Santa Clara CA 95054 USA Univ Calif Berkeley Berkeley CA USA Res Inst Sweden Kista Sweden

ISBN: (纸本)9781450395595

Vector space models for symbolic processing that encode symbols by random vectors have been proposed in cognitive science and connectionist communities under the names Vector Symbolic Architecture (VSA), and, synonymously, Hyperdimensional (HD) computing [22, 31, 46]. In this paper, we generalize VSAs to function spaces by mapping continuous-valued data into a vector space such that the inner product between the representations of any two data points approximately represents a similarity kernel. By analogy to VSA, we call this new function encoding and computing framework Vector Function Architecture (VFA). In VFAs, vectors can represent individual data points as well as elements of a function space (a reproducing kernel Hilbert space). The algebraic vector operations, inherited from VSA, correspond to well-defined operations in function space. Furthermore, we study a previously proposed method for encoding continuous data, fractional power encoding (FPE), which uses exponentiation of a random base vector to produce randomized representations of data points and fulfills the kernel properties for inducing a VFA. We show that the distribution from which components of the base vector are sampled determines the shape of the FPE kernel, which in turn induces a VFA for computing with band-limited functions. In particular, VFAs provide an algebraic framework for implementing large-scale kernel machines with random features, extending [51]. Finally, we demonstrate several applications of VFA models to problems in image recognition, density estimation and nonlinear regression. Our analyses and results suggest that VFAs constitute a powerful new framework for representing and manipulating functions in distributed neural systems, with myriad potential applications in artificial intelligence.

关键词： Hyperdimensional Computing Vector Symbolic Architectures Holographic Reduced Representations Kernel Methods population coding Theoretical Neuroscience

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Reasoning With Discrete Factor Graph

Reasoning With Discrete Factor Graph

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IEEE International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (ROBIONETICS)

作者： Sugiarto, Indar Maier, Paul Conradt, Joerg Tech Univ Munich Inst Automat Control Engn D-80290 Munich Germany Univ Munich Ctr Sensorimotor Res Bernstein Ctr Computat Neurosci Munich Munich Germany

ISBN: (纸本)9781479912063

When working with probabilistic graphical models we usually have two options to build the model: either using a Bayesian network (BN) or a Markov random field (MRF). However, there exist one more graphical representation which is able to unify the properties of BN and MRF that is called Factor Graph. This paper describes conceptual methods in working with factor graph especially with discrete random variables, how to learn its parameter and how to perform inference for making a reasoning task with it. Here we use population coding principles to discretize continues values of messages transmitted within the factor graph to update the network's internal belief. We provide several illustrative examples to highlight important aspects when developing a model for factor graphs.

关键词： discrete factor graph population coding belief propagation

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Disparity Energy Model Using a Trained Neuronal population

Disparity Energy Model Using a Trained Neuronal Population

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Martins, Jaime A. Rodrigues, J. M. F. du Buf, J. M. H. Univ Algarve Inst Syst & Robot Vis Lab FCT Faro Portugal

ISBN: (纸本)9781467307536

Depth information using the biological Disparity Energy Model can be obtained by using a population of complex cells. This model explicitly involves cell parameters like their spatial frequency, orientation, binocular phase and position difference. However, this is a mathematical model. Our brain does not have access to such parameters, it can only exploit responses. Therefore, we use a new model for encoding disparity information implicitly by employing a trained binocular neuronal population. This model allows to decode disparity information in a way similar to how our visual system could have developed this ability, during evolution, in order to accurately estimate disparity of entire scenes.

关键词： disparity population coding learning biological model

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A Neural Network Model for Categorical Effects in Color Memory

A Neural Network Model for Categorical Effects in Color Memo...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Imai, Chihiro Tajima, Satohiro Aihara, Kazuyuki Suzuki, Hideyuki Univ Tokyo Meguro Ku 4-6-1 Komaba Tokyo 1538505 Japan NHK Japan Broadcasting Corp Nagano Stn Nagano Nagano 3808502 Japan

ISBN: (纸本)9784990288051

Human color perception is categorical. Previous experimental studies have shown that the color category has profound effects on cortical neural responses, perceptual color discrimination and color memory. However, existing theoretical studies are not enough to provide an inclusive model accounting for those categorical effects in color perception and memory. In this study, we propose a computational model for categorical color processing, where the color memory is represented by a population of color selective neurons in cortex Our model reproduces the characteristics of color memory reported in the previous experimental studies. Furthermore, it explains the properties of neurons in IT cortex, which change the activity depending on whether the task demands is color categorization or discrimination. This study suggests that perceptual biases found in color processing and task-dependent modulations of neural responses may be explained as a natural consequence of statistically optimal estimation.

关键词： color memory categorical color population coding

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Dynamic Cluster Formation using populations of Spiking Neurons

Dynamic Cluster Formation using Populations of Spiking Neuro...

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International Joint Conference on Neural Networks (IJCNN)

作者： Belatreche, Ammar Paul, Rakesh Univ Ulster Intelligent Syst Res Ctr Sch Comp & Intelligent Syst Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9781467314909

This paper introduces a novel neuro-dynamic system for adaptive online clustering using populations of spiking neurons and spike-timing dependent plasticity (STDP). Real-valued data samples are temporally encoded into spike events, used by biological neurons to encode information and communicate with one another, and clusters are represented by spiking neuron populations of varying size. The number of clusters is unknown a priori and clusters are learned in an online fashion where each data sample is provided only once. The coincidence detection capability of spiking neurons is utilized for data clustering and clusters are dynamically formed. The structure of the spiking neural network is constantly adjusted through adding and pruning of neuron populations. Besides, the number of neurons within each population constantly adapts as new data arrives. STDP is employed to adjust the strength of synaptic connections and enhance the selectivity of each population to its corresponding group of data. Preliminary experiments were carried out on synthetic and selected benchmark datasets to evaluate the performance of the proposed system. Promising results were obtained, which indicate the viability of spike-based population coding for online data clustering.

关键词： Spiking Neurons Unsupervised Learning Online Clustering population coding STDP Spike Response Model

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Learning Complex population-Coded Sequences

Learning Complex Population-Coded Sequences

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19th International Conference on Artificial Neural Networks (ICANN 2009)

作者： Byadarhaly, Kiran V. Perdoor, Mithun Vasa, Suresh Fernandez, Emmanuel Minai, Ali A. Univ Cincinnati Dept Elect & Comp Engn Cincinnati OH 45221 USA

ISBN: (纸本)9783642042737

In humans and primates, the sequential structure of complex actions is apparently learned at an abstract "cognitive" level in several regions of the frontal cortex, independent of the control of the immediate effectors by the motor system. At this level, actions are represented in terms of kinematic parameters especially direction of end effector movement and encoded using population codes. Muscle force signals are generated from this representation by downstream systems in the motor cortex and the spinal cord. In this paper, we consider the problem of learning population-coded kinematic sequences in an abstract neural network model of the medial frontal cortex. For concreteness, the sequences are represented as line drawings in a two-dimensional workspace. Learning such sequences presents several challenges because of the internal complexity of the individual sequences and extensive overlap between sequences. We show that, by using a simple module-selection mechanism, our model is capable of learning multiple sequences with complex structure and very high cross-sequence similarity.

关键词： Sequence learning population coding motor system

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Motion encoding in the salamander retina

Motion encoding in the salamander retina

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作者： Norma Krystyna Kuhn Georg-August-University Gottingen

学位级别：博士

The detection and correct interpretation of motion in visual scenes is important in everyday tasks, e. g., for avoiding cars when crossing the street or for assessing the optic flow, induced by self-motion, when navigating through a room. The processing of visual motion starts in the retina where specialized neural circuits integrate the incoming signals and extract relevant features. Retinal ganglion cells, the output neurons of the retina, send the processed information to downstream brain areas. Here, the retinal encoding of motion signals was studied in the salamander, a widely used model system for analyzing retinal function. Signals from up to 400 ganglion cells were recorded simultaneously from the in-vitro retina with multi- electrode arrays, allowing the classification of cell types and thorough population analyses. In the salamander retina, object-motion sensitive (OMS) ganglion cells have been identified which respond to the differential motion of an object on a moving background but are suppressed by global image motion. These cells might be relevant for detecting moving objects even during self-motion. Fur- thermore, many vertebrates possess direction-selective (DS) ganglion cells which preferably respond to a certain direction of drifting motion. They are thought to provide important information about the optic flow to higher brain areas. Yet, direction-selective ganglion cells have been absent in previous characterizations of the salamander retina. Here, direction-selective ganglion cells could be identified in the retina of the axolotl salamander (Ambystoma mexicanum). Further, two distinct types of direction-selective ganglion cells could be discriminated. One might play a role in processing global image motion (standard DS cells), while the other is especially sensitive to object motion and may assist in detecting a moving object's direction (OMS-DS cells). Standard DS and OMS-DS cells differed in many fundamental properties, as their area of spati

关键词： retina motion encoding direction selectivity object-motion sensitivity population coding linear decoding salamander

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A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks

A Brain-Inspired Causal Reasoning Model Based on Spiking Neu...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fang, Hongjian Zeng, Yi Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9780738133669

In today's field of artificial intelligence, the plausibility of neural networks still lacks breakthrough. We believe one reason is that the current deep neural network method based on the framework of statistical learning, in essence, only uses the correlation between the data to make predictions, different from human beings who complete reasoning and decision-making by invariably induce the causality between propositions. To solve this problem, previous researchers have proposed some causal reasoning approaches based on the causal graphs. Inspired by the human brain, we propose Causal Reasoning Spiking Neural Network(CRSNN) to implement the causal reasoning with STDP learning rule and population coding mechanism. After the verification experiment in the basic case, we show the possibility of implementation causal reasoning with SNN. As far as we know, this is the first time that SNN is used to complete causal reasoning tasks, which is an essential topic both in cognitive neuroscience and artificial intelligence.

关键词： Brain-Inspired AI Causal Reasoning population coding Spiking Neural Network

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Dimension change, coarse grained coding and pattern recognition in spatio-temporal nonlinear systems.

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Journal of integrative neuroscience 2003年第1期2卷 71-102页

作者： Demaris, David IBM Corporation 11400 Burnet Rd Austin 78758 TX United States

Several research programs employing spatio-temporal recurrent dynamics and changes in dimensionality have extended the dialog on neural computation and coding beyond classical frameworks such as feed forward and attractor neural networks and feature detectors. Some have emphasized spiking networks, while others emphasize oscillations and synchronization as the locus of computation and coding. In this paper, the formalism of locally connected homogeneous coupled map lattices is described. Its deployment in an extended version of the dynamical recognizer framework is described, and is compared with density coding, computational mechanics, and liquid state machine frameworks for neural computation. A population coding strategy based on coarse graining the continuous valued distribution of all sites in the lattice is developed and examined as a form of dimension reduction. Results on recognition of 3-D objects are reported. In order to better understand the dynamics supporting recognition, measures suggested by these other research programs and computational frameworks were examined. Dynamics trajectories from object recognition trials were examined for correlation with recognition rates and measures of the distance of the representation space statistics between the target objects and noise initial conditions, and the intrinsic separation between different objects in the set to be classified were performed. These results raise questions about the efficacy of density coding as an explanation for the results, and on the validity of recent criticisms that chaotic systems cannot satisfy separation requirements required for real time computation.

关键词： population code coupled map lattice object recognition population coding liquid state machine computational mechanics chaotic neural network synchronization density coding symbolic dynamics

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