The EMBO workshop on the The Assembly and Function of Neuronal Circuits was held at the Centro Stefano Franscini in Monte Verit, Ascona, Switzerland, from 25 to 30 Sep 2005. The meeting was the first in a new series t...
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The EMBO workshop on the The Assembly and Function of Neuronal Circuits was held at the Centro Stefano Franscini in Monte Verit, Ascona, Switzerland, from 25 to 30 Sep 2005. The meeting was the first in a new series that intends to bring together both developmental and systems neuroscientists. The talks covered a wide range of areas in neurobiology in many model organisms from worms to primates.
We describe an integrative model that encodes associations between related concepts in the human hippocampal formation, constituting the skeleton of episodic memories. The model, based on partially overlapping assembl...
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We describe an integrative model that encodes associations between related concepts in the human hippocampal formation, constituting the skeleton of episodic memories. The model, based on partially overlapping assemblies of "concept cells," contrast markedly with the well-established notion of pattern separation, which relies on conjunctive, context dependent single neuron responses, instead of the invariant, context independent responses found in the human hippocampus. We argue that the model of partially overlapping assemblies is better suited to cope with memory capacity limitations, that the finding of different types of neurons and functions in this area is due to a flexible and temporary use of the extraordinary machinery of the hippocampus to deal with the task at hand, and that only information that is relevant and frequently revisited will consolidate into long-term hippocampal representations, using partially overlapping assemblies. Finally, we propose that concept cells are uniquely human and that they may constitute the neuronal underpinnings of cognitive abilities that are much further developed in humans compared to other species.
Purpose: Transcranial direct current stimulation (tDCS) has been studied in humans for its effects on enhancement of learning, amelioration of psychiatric disorders, and modification of other behaviors for over 50 yea...
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Purpose: Transcranial direct current stimulation (tDCS) has been studied in humans for its effects on enhancement of learning, amelioration of psychiatric disorders, and modification of other behaviors for over 50 years. Typical treatments involve injecting 2 mA current through scalp electrodes for 20 minutes, sometimes repeated weekly for two to five sessions. Little is known about the direct effects of tDCS at the neural circuit or the cellular level. This study assessed the effects of tDCS-like currents on the central nervous system by recording effects on retinal ganglion cell responsiveness using the rabbit retina eyecup preparation. Materials and methods: We examined changes in firing to On and Off light stimuli during and after brief applications of a range of currents and polarity and in different classes of ganglion cells. Results: The responses of Sustained cells were consistently suppressed during the first round of current application, but responses could be enhanced after subsequent rounds of stimulation. The observed first round suppression was independent of current polarity, amplitude, or number of trials. However, the light responses of Transient cells were more likely to be enhanced by negative currents and unaffected or suppressed by first round positive currents. Short-duration currents, that is, minutes, as low as 2.5 mu A produced a remarkable persistency of firing changes, for up to 1.5 hours, after cessation of current. Conclusion: The results are consistent with postulated tDCS alteration of central nervous system function, which outlast the tDCS session and provide evidence for the isolated retina as a useful model to understand tDCS actions at the neuronal level.
The information transmission properties of ensembles of muscle spindles (MSs) and the effect of the motor muscle spindle innervation (fusimotor or gamma system) on these properties were studied: A random stimulus was ...
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ISBN:
(纸本)0780375793
The information transmission properties of ensembles of muscle spindles (MSs) and the effect of the motor muscle spindle innervation (fusimotor or gamma system) on these properties were studied: A random stimulus was applied to a muscle in the hind limb of a cat, while spike trains from several MSs were recorded simultaneously. The stimulus was administered twice, with an operative and a disconnected gamma system. The Shannon information rate between the stimulus and spike trains, as well as other information theoretic measures, were estimated. The estimation method was based upon the sliding window Lempel-Ziv algorithm with an extension to the conditional case. The information rate of ensembles of MSs increased with increasing ensemble size. However, with an operative gamma system the "ensemble effect" was much higher. In addition, the ensemble effect was influenced by the stimulus spectrum. The results indicate that the gamma system has an important role in enhancing information transmission from ensembles of MSs to the spinal cord.
The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation betwe...
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The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation between interactions among multiple areas, and if they are fixed or dynamic. The structural connectivity provides a substrate for these interactions, but anatomical connectivity is not known in sufficient detail and it only gives us a static picture. Using the Allen Brain Observatory Visual coding Neuropixels dataset, which includes simultaneous recordings of spiking activity from up to 6 hierarchically organized mouse cortical visual areas, we estimate the functional connectivity between neurons using a linear model of responses to flashed static grating stimuli. We characterize functional connectivity between populations via interaction subspaces. We find that distinct subspaces of a source area mediate interactions with distinct target areas, supporting the notion that cortical areas use distinct channels to communicate. Most importantly, using a piecewise linear model for activity within each trial, we find that these interactions evolve dynamically over tens of milliseconds following a stimulus presentation. Inter-areal subspaces become more aligned with the intra-areal subspaces during epochs in which a feedforward wave of activity propagates through visual cortical areas. When the short-term dynamics are averaged over, we find that the interaction subspaces are stable over multiple stimulus blocks. These findings have important implications for understanding how information flows through biological neural networks composed of interconnected modules, each of which may have a distinct functional specialization.
Low-pass filtering techniques have been suggested as a simple approach to recover the original input modulating information from an integral pulse frequency modulation (IPFM) process mimicking neural physiological enc...
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ISBN:
(纸本)0780379438
Low-pass filtering techniques have been suggested as a simple approach to recover the original input modulating information from an integral pulse frequency modulation (IPFM) process mimicking neural physiological encoding mechanisms. However, due to the non-linearity of the IPFM model and the difficulty in estimating the threshold value of neurons in vivo, it is practically implausible to recover precisely the input modulation amplitude or intensity information through simple low-pass filtering. Instead, it is found through a mathematical analysis that such demodulation is of special importance for studying the firing characteristics of neural and muscle cells. In this paper, with a single sinusoidal signal as the input modulation signal to the IPFM model, an expression for the relationship between the instantaneous firing rate and the intensity of input signals is derived. The results show that the firing rate function in terms of the modulating signal and the threshold can be reconstructed with reasonable accuracy by simple low-pass filtering.
Electrophysiological recordings of brain activity include point process spike trains as well as continuous valued signals such as electroencephalograms (EEG), electrocorticograms (ECoG), and local field potentials (LF...
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ISBN:
(纸本)9781424414833
Electrophysiological recordings of brain activity include point process spike trains as well as continuous valued signals such as electroencephalograms (EEG), electrocorticograms (ECoG), and local field potentials (LFP). The brain represents information about the outside world in neural spiking activity, which is reflected in each of these signal modalities. An important problem in neuroscience data analysis involves estimating dynamic biological and behavioral signals from neural recordings. Here, we develop an adaptive filtering paradigm for estimating dynamic state processes from mixed observation processes that contain both point process and continuous valued observations. In our analysis of these filtering algorithms, we draw analogies to well-studied linear estimation algorithms such as the Kalman and Extended Kalman filters. We demonstrate the application of this mixed filtering paradigm to the problem of estimating a reaching movement trajectory from simulated simultaneously recorded motor cortical spiking and LFP activity. We demonstrate that the mixed filter is better able to capture information about the movement trajectory than are filters based on the spiking activity or LFPs alone.
We review the hypothesis that the brain uses a generative model to explain the causes of sensory inputs, using prediction schemes that operate based upon assimilation of time-series sensory data. We put this hypothesi...
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We review the hypothesis that the brain uses a generative model to explain the causes of sensory inputs, using prediction schemes that operate based upon assimilation of time-series sensory data. We put this hypothesis in the context of psychopathology, in particular, schizophrenia's positive symptoms. Building upon work of Helmholtz and upon theories in computational cognitive processing, we hypothesize that delusions in schizophrenia can be explained in terms of false inference. An impairment in inferring appropriate information from the sensory input reflects upon the ability to assess the environment and predict outcomes. Although the inference mechanism likely involves both conscious and unconscious processes, we hypothesize that the trigger of delusions may lie within the unconscious neural pathways. A collection of computational predictive codes have been proposed for modeling perception. We discuss two examples, which may be eligible as substrates for intuitive coding. We argue that failure of the psychotic patient to choose the correct computational scheme, or the optimal range of parameters, may readily lead to an altered reconstruction of the object and false inference, feeding into the delusion mechanism. We finally propose using these models in conjunction with cognitive and imaging data, in order to obtain more testable predictions.
neural coding is one of the central questions in neuroscience for converting visual information into spike patterns. However, the existing encoding techniques require a preset time window and lack effective learning. ...
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ISBN:
(纸本)9783031301070;9783031301087
neural coding is one of the central questions in neuroscience for converting visual information into spike patterns. However, the existing encoding techniques require a preset time window and lack effective learning. In order to overcome these two problems, we design an adaptive convolutional auto-encoder based on spiking neurons in this paper. We first exploit the spike pixel mapping decoding approach to find the optimal value of the time window automatically. Next, we design a deep convolutional neural network to adapt the learning parameters by reconstruction errors to realize the spike encoding process. Then we can naturally get coding pre-training parameters for unifying the convolutional spike coding layer with back-end deep spiking neural networks (SNNs) for recognition tasks. Simulation results demonstrate that the proposed method can achieve better performance compared with other encoding methods.
Although it is well known that neurons receive, process and transmit signals via sequences of sudden stereotyped electrical events, called action potentials or spikes, many analyses of neural data ignore the highly lo...
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ISBN:
(纸本)9781424414970
Although it is well known that neurons receive, process and transmit signals via sequences of sudden stereotyped electrical events, called action potentials or spikes, many analyses of neural data ignore the highly localized nature of these events. We discuss a point process modeling framework for neural systems to perform inference, assess goodness-of-fit, and estimate a state variable from spiking observations. Under this framework, we develop state space estimation and inference algorithms by constructing state models that describe the stochastic evolution of the signals to estimate, and conditional intensity models that define the probability distribution of observing a particular sequence of spike times for a neuron or ensemble. Posterior densities can then be computed using a recursive Bayesian framework combined with the Chapman-Kolmogorov system of equations for discrete-time analyses or the forward Kolmogorov equation for continuous-time analyses. This allows us to derive a toolbox of estimation algorithms and adaptive filters to address questions of static and dynamic encoding and decoding. We discuss the application of these modeling and estimation methods to the problem of predicting an intended reaching arm movement from simulated neurons in primate primary motor cortex. We show that a Bayesian approximate Gaussian filter is able to maintain accurate estimates of intended arm trajectories.
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