Numerous methods have already been developed to estimate the information contained in single spike trains. In this article we explore efficient methods for estimating the information contained in the simultaneous firi...
详细信息
Numerous methods have already been developed to estimate the information contained in single spike trains. In this article we explore efficient methods for estimating the information contained in the simultaneous firing activity of hundreds of neurons. Obviously such methods are needed to analyze data from multi-unit recordings. We test these methods on generic neural microcircuit models consisting of 800 neurons, and analyze the temporal dynamics of information about preceding spike inputs in such circuits. It turns out that information spreads with high speed in Such generic neural microcircuit models, thereby supporting-without the postulation of any additional neural or synaptic mechanisms-the possibility of ultra-rapid computations on the first input spikes. (c) 2005 Elsevier Ltd. All rights reserved.
In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known f...
详细信息
In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 mu s). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.
neural coding is a key problem in neuroscience aimed to understand the information processing mechanism in brain. Among the classical theories of neural coding, population rate coding has been studied widely in many w...
详细信息
neural coding is a key problem in neuroscience aimed to understand the information processing mechanism in brain. Among the classical theories of neural coding, population rate coding has been studied widely in many works. In computational studies, neurons are usually classified into excitatory or inhibitory ones. Excitatory neurons have excitatory output synapses, and inhibitory neurons have inhibitory output synapses. However, according to physiological observations, neurons potentially have both types of output synapses. Thus, in this paper, neuronal networks consisting of neurons with mixed excitatory-inhibitory synapses are constructed to investigate the population rate coding fidelity of neuronal systems. It is revealed that, under intermediate values of recurrent probability, inhibitory-excitatory strength ratio, and noise intensity, the performance of population rate coding could be improved by both excitatory synaptic strength and synaptic time constant. It is indicated that external stimuli can be encoded in the form of population firing rate by the studied neuronal networks very well. What is more exciting is that we find the neuronal networks considered in our work have higher coding efficiency than the traditional ones. Therefore, neurons with mixed excitatory-inhibitory synapses may be much more rational.
We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a pos...
详细信息
We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning.
A frequency domain approach and a time domain approach have been combined in an investigation of the behaviour of the primary and secondary endings of an isolated muscle spindle in response to the activity of two stat...
详细信息
A frequency domain approach and a time domain approach have been combined in an investigation of the behaviour of the primary and secondary endings of an isolated muscle spindle in response to the activity of two static fusimotor axons when the parent muscle is held at a fixed length and when it is subjected to random length changes. The frequency domain analysis has an associated error process which provides a measure of how well the input processes can be used to predict the output processes and is also used to specify how the interactions between the recorded processes contribute to this error. Without assuming stationarity of the input, the time domain approach uses a sequence of probability models of increasing complexity in which the number of input processes to the model is progressively increased. This feature of the time domain approach was used to identify a preferred direction of interaction between the processes underlying the generation of the activity of the primary and secondary endings. In the presence of fusimotor activity and dynamic length changes imposed on the muscle, it was shown that the activity of the primary and secondary endings carried different information about the effects of the inputs imposed on the muscle spindle. The results presented in this work emphasise that the analysis of the behaviour of complex systems benefits from a combination of frequency and time domain methods.
The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledg...
详细信息
The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors. In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution. Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level. (C) 2013 Elsevier Ltd. All rights reserved.
One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand t...
详细信息
One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.
There have been various suggestions about how information is encoded in neural spike trains: by the number of spikes, by the temporal correlations, or by complete patterns. The latter scheme is most general, and encom...
详细信息
There have been various suggestions about how information is encoded in neural spike trains: by the number of spikes, by the temporal correlations, or by complete patterns. The latter scheme is most general, and encompasses many others. However, the search for pattern codes requires exponentially more data than the search for mean rate or correlation codes. Here we describe a method that enables optimal use of whatever quantity of data is available. This method allows spike trains to be studied with variable, non-uniform temporal precision. Precision is optimized to provide a best lower bound for the information content of spike patterns given the available data. (C) 2001 Elsevier Science B.V. All rights reserved.
We consider the problem of estimating Shannon's entropy H from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor process, a generalization of Dirich...
详细信息
We consider the problem of estimating Shannon's entropy H from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor process, a generalization of Dirichlet process, provides a tractable prior distribution over the space of countably infinite discrete distributions, and has found major applications in Bayesian non-parametric statistics and machine learning. Here we show that it provides a natural family of priors for Bayesian entropy estimation, due to the fact that moments of the induced posterior distribution over H can be computed analytically. We derive formulas for the posterior mean (Bayes' least squares estimate) and variance under Dirichlet and Pitman-Yor process priors. Moreover, we show that a fixed Dirichlet or Pitman-Yor process prior implies a narrow prior distribution over H, meaning the prior strongly determines the entropy estimate in the under-sampled regime. We derive a family of continuous measures for mixing Pitman-Yor processes to produce an approximately flat prior over H. We show that the resulting "Pitman-Yor Mixture" (PYM) entropy estimator is consistent for a large class of distributions. Finally, we explore the theoretical properties of the resulting estimator, and show that it performs well both in simulation and in application to real data.
作者：
Tezuka, TaroUniv Tsukuba
Ctr Artificial Intelligence Res 1-1-1 Tennodai Tsukuba Ibaraki Japan Univ Tsukuba
Fac Lib Informat & Media Sci 1-1-1 Tennodai Tsukuba Ibaraki Japan
A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike tra...
详细信息
A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. A novel way of extending any single spike train kernel to multineuron spike trains is proposed. Subclasses of a kernel having fewer parameters are introduced, making optimization more feasible. Gaussian process regression with the kernel outperformed other existing decoding methods. (c) 2018 Elsevier Ltd. All rights reserved.
暂无评论