Inverse problems in computational neuroscience comprise the determination of synaptic weight matrices or kernels for neural networks or neural fields respectively. Here, we reduce multi-dimensional inverse problems to inverse problems in lower dimensions which can be solved in an easier way or even explicitly through kernel construction. In particular, we discuss a range of embedding techniques and analyze their properties. We study the Amari equation as a particular example of a neural field theory. We obtain a solution of the full 2D or 3D problem by embedding 0D or 1D kernels into the domain of the Amari equation using a suitable path parametrization and basis transformations. Pulses are interconnected at branching points via path gluing. As instructive examples we construct logical gates, such as the persistent XOR and binary addition in neural fields. In addition, we compare results of inversion by dimensional reduction with a recently proposed global inversion scheme for neural fields based on
This archive contains source code for the paper Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise by Droste and Lindner, 2017. Specifically, it contains a Python implementation of the analytical formulas derived in that paper (allowing to calculate firing rate, CV and stationary voltage distribution of general integrate-and-fire neurons driven by excitatory shot noise, as well as power spectrum and rate-response of leaky integrate-and-fire neurons with such input) and C++ code implementing a Monte-Carlo simulation to estimate these quantities. A sample Jupyter notebook to play around with the analytics is included, as are scripts to reproduce the figures from the paper ...
This archive contains source code for the paper Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise by Droste and Lindner, 2017. Specifically, it contains a Python implementation of the analytical formulas derived in that paper (allowing to calculate firing rate, CV and stationary voltage distribution of general integrate-and-fire neurons driven by excitatory shot noise, as well as power spectrum and rate-response of leaky integrate-and-fire neurons with such input) and C++ code implementing a Monte-Carlo simulation to estimate these quantities. A sample Jupyter notebook to play around with the analytics is included, as are scripts to reproduce the figures from the paper ...
In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like
In this paper, we demonstrate for the first time an ultrafast fully functional photonic spiking neuron. Our experimental setup constitutes a complete all-optical implementation of a leaky integrate-and-fire neuron, a computational primitive that provides a basis for general purpose analog optical computation. Unlike purely analog computational models, spiking operation eliminates noise accumulation and results in robust and efficient processing. Operating at gigahertz speed, which corresponds to at least 108 speed-up compared with biological neurons, the demonstrated neuron provides all functionality required by the spiking neuron model. The two demonstrated prototypes and a demonstrated feedback operation mode prove the feasibility and stability of our approach and show the obtained performance characteristics.. © 2011 Optical Society of America. Full Article , PDF Article ...
where yi is the output of the i th neuron, xj is the jth input neuron signal, wij is the synaptic weight (or strength of connection) between the neurons i and j, and φ is the activation function. While this model has seen success in machine-learning applications, it is a poor model for real (biological) neurons, because it lacks the time-dependence that real neuron spikes exhibit. Biological models of the integrate-and-fire type take essentially this form; but they have largely been superseded by kinetic models such as the Hodgkin-Huxley model.[citation needed] In the case of modelling a biological neuron, physical analogues are used in place of abstractions such as weight and transfer function. A neuron is filled and surrounded with water containing ions, which carry electric charge. The neuron is bound by an insulating cell membrane and can maintain a concentration of charged ions on either side that determines a capacitance Cm. The firing of a neuron involves the movement of ions into ...
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The Ebb and Flow of Deep Learning: a Theory of Local Learning In a physical neural system, where storage and processing are intertwined, the learning rules for adjusting synaptic weights can only depend on local variables, such as the activity of the pre- and post-synaptic neurons. Thus learning models must specify two things: (1) which variables are to be considered local; and (2) which kind of function combines these local variables into a learning rule. We consider polynomial learning rules and analyze their behavior and capabilities in both linear and non-linear networks. As a byproduct, this framework enables the discovery of new learning rules and important relationships between learning rules and group symmetries. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex ...
The Ebb and Flow of Deep Learning: a Theory of Local Learning In a physical neural system, where storage and processing are intertwined, the learning rules for adjusting synaptic weights can only depend on local variables, such as the activity of the pre- and post-synaptic neurons. Thus learning models must specify two things: (1) which variables are to be considered local; and (2) which kind of function combines these local variables into a learning rule. We consider polynomial learning rules and analyze their behavior and capabilities in both linear and non-linear networks. As a byproduct, this framework enables the discovery of new learning rules and important relationships between learning rules and group symmetries. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex ...
See moreNeural activity is responsible for information processing in the brain. Activity has been found to have natural spatial modes, each of which has a frequency, analogous to the pitch of the notes of a musical instrument. A core aim of this thesis is to analyze large scale brain activity in terms of the eigenmodes of a brain hemisphere and to explore eigenmode dynamics. Electroencephalography (EEG) and evoked response potentials (ERPs) are important measurement techniques used to observe large scale changes in brain activity. EEG is a recording technique, while ERPs are transient electrical responses to brief sensory stimuli. In this thesis analysis of EEG and ERPs is carried out in terms of eigenmodes using an established physiologically based neural field theory, which averages over attribute of neurons to yield a continuum model of brain activity whose parameters are based on the physiology. To explore the effects of boundary conditions and topology, a spherical approximation is used and ...
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Our estimate of the average cortical spread of the LFP is similar to an estimate in layer 2/3 of cat V1 reported recently (Katzner et al., 2009) with a different approach, but is much smaller than many others. Our result seems to contradict a number of studies (Mitzdorf, 1987; Kruse and Eckhorn, 1996; Kreiman et al., 2006; Liu and Newsome, 2006; Berens et al., 2008) that concluded that the spread of LFP signals ranges from ∼500 μm to a few millimeters. However, all such estimates rely on a model associating the LFP with functional properties of cortical neurons. For most studies (Mitzdorf, 1987; Kruse and Eckhorn, 1996; Kreiman et al., 2006; Liu and Newsome, 2006; Berens et al., 2008; Katzner et al., 2009), estimates of the cortical spread of the LFP were indirect and were associated with columnar structures of certain feature selectivities, such as orientation, direction, or speed selectivity. Because the feature selectivity of the LFP is usually broader than that of single unit activity or ...
We argue that current theories of multisensory representations are inconsistent with the existence of a large proportion of multimodal neurons with gain fields and partially shifting receptive fields. Moreover, these theories do not fully resolve the recoding and statistical issues involved in multisensory integration. An alternative theory, which we have recently developed and review here, has important implications for the idea of frame of reference in neural spatial representations. This theory is based on a neural architecture that combines basis functions and attractor dynamics. Basis function units are used to solve the recoding problem, whereas attractor dynamics are used for optimal statistical inferences. This architecture accounts for gain fields and partially shifting receptive fields, which emerge naturally as a result of the network connectivity and dynamics.
Figure 3: The effect of synaptic noise of various synaptic types. (a) Visualization of spike train groups analyzed under synaptic noise. The ordinate represents the AP rank of APs fired at times shown on abscissa. The braided lines in the main graph represent different spike trains of individual trials and demonstrate an interesting preference of the spiking mechanism preserving AP times well aligned despite different numbers of fired APs in trials. The inset focuses on the region of an AP cluster, demonstrating the cluster method used to analyze APs: the dotted blue rectangle in the inset represents the 10 ms time window moving along aligned spike trains and collecting only those AP clusters having within the 10 ms window number of spikes larger than 70% the value of ...
We have shown that intensity-selective and intensity-nonselective neurons differ in excitatory tuning profile and absolute excitatory strength. Intuitively, the differential intensity tuning profile of excitation may fully account for the functional difference between intensity-selective and intensity-nonselective neurons. In the intensity-selective neuron, excitation is close to saturation at the intensity threshold (it reaches ,80% of the peak level), whereas at this intensity level inhibition is nearly the lowest (Fig. 4A,B). This may lead to a strong output response at the intensity threshold. With intensity further increasing, inhibition catches up, and the level of spike response may gradually fall.. To demonstrate how much the observed excitatory and inhibitory tuning profiles can account for the intensity tuning profile of output responses in a quantitative manner, we used a conductance-based single-compartment integrate-and-fire neuron model (Liu et al., 2007; Zhou et al., 2012). ...
Paolo Puggioni. Bayesian inference of synaptic inputs given an in vivo voltage clamp trace. Voltage clamp recordings in awake animals directly measure the excitatory (or inhibitory) synaptic current flowing in a single neuron.. The current results from the summation of thousands of synaptic inputs of variable size per second. Recent methods based on Kalman filtering [Paninski2012, Lankarany2013] give a good estimation of the time-varying synaptic conductance, that is a function of input rate and size. However, inferring how the distribution of input size changes during behaviour can be important to understand computational mechanisms used by our brain. Here I propose a simple hierarchical model that is able to estimate both frequency and probability distribution of the synaptic weights. Firstly I test the validity of my model by using generated data, showing that it is robust to high frequency noise and slow fluctuations in the inputs rate typical of in vivo recordings. Thus, I apply it to ...
Many hormones are released in pulsatile patterns. This pattern can be modified, for instance by changing pulse frequency, to encode relevant physiological information. Often other properties of the pulse pattern will also change with frequency. How do signaling pathways of cells targeted by these hormones respond to different input patterns? In this study, we examine how a given dose of hormone can induce different outputs from the target system, depending on how this dose is distributed in time. We use simple mathematical models of feedforward signaling motifs to understand how the properties of the target system give rise to preferences in input pulse pattern. We frame these problems in terms of frequency responses to pulsatile inputs, where the amplitude or duration of the pulses is varied along with frequency to conserve input dose. We find that the form of the nonlinearity in the steady state input-output function of the system predicts the optimal input pattern. It does so by selecting an optimal
A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches-on the other hand-contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper
Too bad that paper wasnt adequately reviewed. The authors present no rationale for using cm*dVs/dt to represent the effect of extracellular stimulation on their model cells. Nor do they explain how they relate their Vs to cellular morphology, orientiation, or location within the cranium, or to the actual extracellular field induced in the brain by the stimulating electrodes (whatever the location or geometry of those electrodes might be). But of course none of those considerations could be taken into account because their model cells are single compartment models, which wouldnt respond to a realistic extracellular stimulus anyway. Apparently Vs is just assigned whatever values the authors found necessary to alter the firing activity of their single compartment model neurons ...
sim_isp_orig ====== This simulation is the adapted from the network used to generate Figure 4 in [1]. It simulates a balanced network of 10,000 sparsely connected integrate-and-fire neurons[2] and features plastic inhibitory-exciatory synapses. {{:examples:isp_fig4.png?300,}} [1] Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786. [[http://www.ncbi.nlm.nih.gov/pubmed/16291952,PubMed]] [2] Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C., Gerstner, W., 2011. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 334, 1569 -1573. [[http://www.ncbi.nlm.nih.gov/pubmed/22075724,PubMed]] ===== Running the program ===== To run the program after [[manual:start#first steps,compilation]] it suffices to call it as follows ,code shell, ./sim_isp_orig ,/code, this will trigger a 1000s simulation which creates the following output files ,code shell, 4 -rw-r--r-- 1 zenke lcn1 ...
In our approach, we choose to approximate P from the contribution coming from the most probable trajectory for the potential for each cell i, referred to as V i *(t). This approximation is exact when the amplitude σ of the noise is small. The determination of V i *(t) was done numerically by Paninski for one cell in [4]. We have found a fast algorithm to determine V i *(t) analytically in a time growing linearly with the number of spikes and quadratically with the number of neurons, which allows us to process recordings with tens of neurons easily. The algorithm is based on a detailed and analytical resolution of the coupled equations for the optimal potential V i *(t) and the associated optimal noise η i *(t) through (1), and is too complex to be explained in this abstract. ...
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log in you can tag this publication with additional keywords A publication can refer to another publication (outgoing references) or it can be referred to by other publications (incoming references).. ...
Two limitations of Dynamic clamp are entirely technical: it has to be fast and temporally consistent. The time required for reading the membrane potential and calculating the current to inject has to be faster than the fastest time constant in the real neuron. This becomes problematic when the model is computationally intensive (as with stochastic Markov models). With ever increasing computer processor speeds, the speed limitation of dynamic clamp is constantly being pushed back. The update interval of the dynamic clamp has to be reliable and reproducible. Modern operating systems (OS) such as Linux, Mac OS, and Windows suffer from the inability of a program running on the OS to be guaranteed a requested operation will be performed precisely when it is requested. Operating systems that can guarantee a request is performed within a well defined time window are Linux with the RTLinux kernel extension, LabView with the real time module, and DOS. A version of dynamic clamp developed by R. Pinto ...
Gain modulation and the voltage dependence of channel activation.(A) The firing rate of the model neuron plotted against the average membrane potential, illustr
Designing Model-Based and Model-Free Reinforcement Learning Tasks without Human Guidance. Shin, Jae Hoon; Lee, JEE HANG; Tong, Shuangyi; Kim, Sang Whan; Lee, Sang Wanresearcher, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Neural Information Processing Systems Foundation, 2019- ...
The cerebral cortex is a highly complex network comprised of billions of excitable nerve cells. The coordinated dynamic interactions of these cells underlie our thoughts, memories, and sensory perceptions. A healthy brain carefully regulates its neural excitability to optimize information processing and avoid brain disorders. If excitability is too low, neural interactions are too weak and signals fail to propagate through the brain network. On the other hand, high excitability can result in excessively strong interactions and, in some ca
it is used in synaptic interactions, and in integrate-and-fire models it also leads to the execution of one or more reset statements.. Sometimes, it can be useful to define additional events, e.g. when an ion concentration in the cell crosses a certain threshold. This can be done with the custom events system in Brian, which is illustrated in this diagram.. ...
Author: Boussinot, Guillaume et al.; Genre: Journal Article; Published in Print: 2014-06-30; Title: Achieving realistic interface kinetics in phase field models with a diffusional contrast
G. Agarwal, I. H. Stevenson, A. Berényi, K. Mizuseki, G. Buzsáki, F. T. Sommer: Spatially distributed local fields in the hippocampus encode rat position. Science 344 (2014): 626-630. pdf Supplement J. P. Crutchfield, R. G. James, S. Marzen and D. P. Varn, Understanding and Designing Complex Systems: Response to A framework for optimal high-level descriptions in science and engineering---preliminary report, (2014). arXiv link J. H. Engel, S. B. Eryilmaz, SangBum Kim, M. BrightSky, Chung Lam, H.-L. Lung, B. A. Olshausen, H.-S.P. Wong (2014) Capacity optimization of emerging mem- ory systems: A shannon-inspired approach to device characterization, Electron Devices Meeting (IEDM), 2014 IEEE International , vol., no., pp.29.4.1,29.4.4, 15- 17 Dec. 2014 link A. K. Fletcher and S. Rangan, Scalable Inference for Neuronal Connectivity from Calcium Imaging, Proc. 28th Ann. Conf. Neural Information Processing Systems, NIPS (2014). Harper NS, Scott BH, Semple MN, McAlpine D (2014) The neural code ...
G. Agarwal, I. H. Stevenson, A. Berényi, K. Mizuseki, G. Buzsáki, F. T. Sommer: Spatially distributed local fields in the hippocampus encode rat position. Science 344 (2014): 626-630. pdf Supplement J. P. Crutchfield, R. G. James, S. Marzen and D. P. Varn, Understanding and Designing Complex Systems: Response to A framework for optimal high-level descriptions in science and engineering---preliminary report, (2014). arXiv link J. H. Engel, S. B. Eryilmaz, SangBum Kim, M. BrightSky, Chung Lam, H.-L. Lung, B. A. Olshausen, H.-S.P. Wong (2014) Capacity optimization of emerging mem- ory systems: A shannon-inspired approach to device characterization, Electron Devices Meeting (IEDM), 2014 IEEE International , vol., no., pp.29.4.1,29.4.4, 15- 17 Dec. 2014 link A. K. Fletcher and S. Rangan, Scalable Inference for Neuronal Connectivity from Calcium Imaging, Proc. 28th Ann. Conf. Neural Information Processing Systems, NIPS (2014). Harper NS, Scott BH, Semple MN, McAlpine D (2014) The neural code ...
On Thu, May 12, 2005 at 10:47:13AM +0200, Karim Belabas wrote: , Indeed. As for the ridiculous accuracy of %3 above, we have conflicting , specifications: , 1) PARI functions give as precise a result as is possible from the input, , 2) floating point computations are meant to foster speed by truncating , operands. , , Only 1) is specified in the documentation, 2) is only a general understanding. , And a rather misleading one as far as PARI is concerned; it is a common , source of misapprehension to assume that , , * realprecision is the relative accuracy used to truncate operands in 2). , Which it is not: it is used to convert exact objects to inexact ones. , , * operands with n digits of accuracy will yield a result with at most the , same accuracy. Which is wrong: indeed 1 + 1e-50000 may be computed to , more than 50000 digits of accuracy. The IEEE754 specification and the MPFR extension say the the correct result is the representable number the closest to the actual result, assuming the ...
Spike-timing dependent plasticity is a learning mechanism used extensively within neural modelling. The learning rule has been shown to allow a neuron to find the onset of a spatio-temporal pattern repeated among its afferents. In this thesis, the first question addressed is what does this neuron learn? With a spiking neuron model and linear prediction, evidence is adduced that the neuron learns two components: (1) the level of average background activity and (2) specific spike times of a pattern. Taking advantage of these findings, a network is developed that can train recognisers for longer spatio-temporal input signals using spike-timing dependent plasticity. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feedforwardly connected in such a way that both the correct stimulus and the ...
Computational neuroscience is the science of how the brain computes, that is, how the brain performs cognitive functions such as recognizing a face or walking. Here I will argue that most models of cognition developed in the field, especially as regards sensory systems, are actually not biological models but hybrid models consisting of a neural model together with an abstract model.. First of all, many neural models are not meant to be models of cognition. For example, there are models that are developed to explain the irregular spiking of cortical neurons, or oscillations. I will not consider them. According to the definition above, I categorize them in theoretical neuroscience rather than computational neuroscience. Here I consider for example models of perception, memory, motor control.. An example that I know well is the problem of localizing a sound source from timing cues. There are a number of models, including a spiking neuron model that we have developed (Goodman and Brette, 2010). ...
Information theory provides a natural set of statistics to quantify the amount of knowledge a neuron conveys about a stimulus. A related work (Kennel, Shlens, Abarbanel, & Chichilnisky, 2005) demonstrated how to reliably estimate, with a Bayesian confidence interval, the entropy rate from a discrete, observed time series. We extend this method to measure the rate of novel information that a neural spike train encodes about a stimulus-the average and specific mutual information rates. Our estimator makes few assumptions about the underlying neural dynamics, shows excellent performance in experimentally relevant regimes, and uniquely provides confidence intervals bounding the range of information rates compatible with the observed spike train. We validate this estimator with simulations of spike trains and highlight how stimulus parameters affect its convergence in bias and variance. Finally, we apply these ideas to a recording from a guinea pig retinal ganglion cell and compare results to a ...
Computational neuroscience is an approach to understanding the development and function of nervous systems at many different structural scales, including the biophysical, the circuit, and the systems levels. Methods include theoretical analysis and modeling of neurons, networks, and brain systems and are complementary to empirical techniques in neuroscience. Areas and topics of particular interest to this book series include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, computational approaches to biological motor control, and models of learning and memory. Further topics of interest include the intersection of computational neuroscience with engineering, from representation and dynamics, to observation and control.. ...
This paper describes the performance of a neuronal network model of the input layer 4Cα of macaque V1. This model differs from others in the literature in several ways. (i) It is designed largely from data for the anatomy and physiology of layer 4Cα of macaque (i.e., length scales and patterning of connectivity, and pinwheel centers). (ii) It uses cortical coordinates rather than idealized coordinates as in ring models (7, 8, 28) or near-ring models (9), whose coordinate labels are angles of orientation preference, rather than cortical locations within the layer. (iii) It has only short-range local inhibition, which is consistent with anatomical data, rather than an inhibition which is explicitly long-range in orientation preference, as is standard for many models (7-9, 31). (iv) It uses membrane potential, driven by synaptic conductances, as the fundamental variables, rather than activities or mean firing rates (7, 8, 32), or a probabilistic population-density representation (31, 33, ...
The aim of this work is to introduce and study simple neuron models with a dynamic threshold: The integrate and fire with dynamic threshold and a resonate and fire with dynamic threshold. The last model is a new model raised from an initial study of the integrate and fire with a dynamic threshold. The characteristics of the new model are studied from a mathematical point of view specially concerning the dynamical systems theory of the non-smooth systems. . In this project we will study analytically and computationally integrate-and-fire models with dynamic threshold, reproducing behaviors observed in the real nervoussystems, using techniques from non-smooth systems. Our goal is to describe the types of spiking patterns that can generate in response to steady and periodic current ...
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Background. Synaptic plasticity is thought to be the cellular correlate for the formation of memory traces in the brain. Recently, spike-timing dependent plasticity has gained increased interest as a plausible physiological mechanism for the activity-dependent modification of synaptic strength. It might be fundamental for circuit refinement, map plasticity and the explanation of higher brain functions. It is not clear if spike-timing dependent plasticity is a universal learning rule based on simple biophysical mechanisms. The molecular signalling pathways involved are quite diverse and apparently use-dependent. The fundamental question is what determines the molecular machinery at a synaptic contact that translates electrical activity into a change in synaptic strength.Specific Aims. (1) The influence of active dendritic properties, which can result in the generation of local dendritic spikes, on changes in synaptic strength will be studied. They will have an important impact on the local ...
WK7 - Hebbian Learning. CS 476: Networks of Neural Computation WK7 - Hebbian Learning Dr. Stathis Kasderidis Dept. of Computer Science University of Crete Spring Semester, 2009. Contents. Introduction to Hebbian Learning Definitions on Pattern Association Pattern Association Network Slideshow 6779351 by sylvana-silas
To contribute, see :pencil2: code of contribution. Computational neuroscience is a multidisciplinary science that joins biology/neuroscience, medicine, biophysics, psychology, computer science, mathematics, and statistics to study the nervous system using computational approaches.. This list of schools and researchers in computational neuroscience, theoretical neuroscience, (and systems neuroscience) aims to give a global perspective of researchers in the field, make it easier to apply to the listed institutions, and also provide a reasonable way to find an advisor.. In addition to names of PIs, excerpts of their academic biographies, and links to their publications, many of the researchers are qualified with a small scale +/=/- computational. The metric is subjective to the editor of that material but it generally breaks down as: (+) refers to a researcher the university identifies as a computational neuroscientist, their bio consistently identifies a significant component of their research ...
Buy, download and read Computational Neuroscience ebook online in PDF format for iPhone, iPad, Android, Computer and Mobile readers. Author: Jianfeng Feng. ISBN: 9780203494462. Publisher: CRC Press. How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues
Many sounds of ecological importance, such as communication calls, are characterised by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamocortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based ...
Here, based on our previous work on linear synaptic filtering [1-3], we build a general theory for the stationary firing response of integrate-and-fire (IF) neurons receiving stochastic inputs filtered by one, two or multiple synaptic channels each characterized by an arbitrary timescale. The formalism applies to arbitrary IF model neurons, and to arbitrary forms of input noise (i.e., not required to be Gaussian or to have small amplitude), as well as to any form of synaptic filtering (linear or non-linear). The theory determines with exact analytical expressions the firing rate of an IF neuron for long synaptic time constants using the adiabatic approach. The correlated spiking (cross-correlations function) of two neurons receiving common as well as independent sources of noise is also described (see figure 1). The theory is exemplified using leaky, quadratic and noise thresholded IF neurons (LIF, QIF, NTIF). Although the adiabatic approach is exact when at least one of the synaptic timescales ...
Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right.
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The responses of neurons to time-varying injected currents are reproducible on a trial-by-trial basis in vitro, but when a constant current is injected, small variances in interspike intervals across trials add up, eventually leading to a high variance in spike timing. It is unclear whether this difference is due to the nature of the input currents or the intrinsic properties of the neurons. Neuron responses can fail to be reproducible in two ways: dynamical noise can accumulate over time and lead to a desynchronization over trials, or several stable responses can exist, depending on the initial condition. Here we show, through simulations and theoretical considerations, that for a general class of spiking neuron models, which includes, in particular, the leaky integrate-and-fire model as well as nonlinear spiking models, aperiodic currents, contrary to periodic currents, induce reproducible responses, which are stable under noise, change in initial conditions and deterministic perturbations of ...
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a ...
The brain orchestrates perceptions, thoughts, and actions through the ensemble spiking activity of its neurons. Biological processes underlying spike transformations across brain regions, including synaptic transmissions, dendritic integrations, and spike generations, are highly nonlinear dynamical processes and are often nonstationary. For example, it is well established that certain forms of plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), occur in response to specific input patterns, and plasticity is manifested as a change in input-output function that can be viewed as a system nonstationarity. Quantitative studies of how such functions of information transmission across brain regions evolve during behavior are required in order to understand the brain. In this project, we developed a nonstationary modeling framework for the multiple-spike activity propagations between brain regions. ❧ In this study, in order to analyze the nonlinear dynamics underlying ...
The brain orchestrates perceptions, thoughts, and actions through the ensemble spiking activity of its neurons. Biological processes underlying spike transformations across brain regions, including synaptic transmissions, dendritic integrations, and spike generations, are highly nonlinear dynamical processes and are often nonstationary. For example, it is well established that certain forms of plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), occur in response to specific input patterns, and plasticity is manifested as a change in input-output function that can be viewed as a system nonstationarity. Quantitative studies of how such functions of information transmission across brain regions evolve during behavior are required in order to understand the brain. In this project, we developed a nonstationary modeling framework for the multiple-spike activity propagations between brain regions. ❧ In this study, in order to analyze the nonlinear dynamics underlying ...
Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in ...
Multistability remains an intriguing phenomenon for neuroscience. The functional roles of multistability in neural systems are not well understood. On the other hand, its pathological roles are widely discussed in recent studies. Knowledge of the dynamic mechanisms supporting multistability opens new horizons for medical applications. It has been intensively targeted in a search for new treatments of the medical conditions which are caused by malfunction of the dynamics of the CNS. Sudden infant death syndrome, epilepsy and Parkinsons disease are examples of such conditions. Recent progress in the modern technology of computer-brain interface based on real time systems allows to utilize complex feedback stimulation algorithms suppressing pathological regime co-existing with the normal one. It still remains a challenge to identify the scenarios leading to the multistability in the neuronal dynamics and discuss what are potential roles of the multistability in the operation of the central nervous ...
The GL model was defined in 2013 by mathematicians Antonio Galves and Eva Löcherbach.[1] Its inspirations included Frank Spitzers interacting particle system and Jorma Rissanens notion of stochastic chain with memory of variable length. Another work that influenced this model was Bruno Cessacs study on the leaky integrate-and-fire model, who himself was influenced by Hédi Soula.[4] Galves and Löcherbach referred to the process that Cessac described as a version in a finite dimension of their own probabilistic model. Prior integrate-and-fire models with stochastic characteristics relied on including a noise to simulate stochasticity.[5] The Galves-Löcherbach model distinguishes itself because it is inherently stochastic, incorporating probabilistic measures directly in the calculation of spikes. It is also a model that may be applied relatively easily, from a computational standpoint, with a good ratio between cost and efficiency. It remains a non-Markovian model, since the probability ...
A system and method for detecting aberrant network behavior. One embodiment provides a system of detecting aberrant network behavior behind a network access gateway comprising a processor, a first network interface coupled to the processor, a second network interface coupled to the processor, a storage media accessible by the processor and a set of computer instructions executable by the processor. The computer instructions can be executable to observe network communications arriving at the first network interface from multiple clients and determine when the traffic of a particular client is indicative of malware infection or other hostile network activity. If the suspicious network communication is determined to be of a sufficient volume, type, or duration the computer instructions can be executable to log such activity to storage media, or to notify an administrative entity via either the first network interface or second network interface, or to make the computer instructions be executable to perform
Correlated neuronal activity and the flow of neural information. Jaeseung Jeong, Ph.D Department of Bio and Brain Engineering. Nonlinear information transmission of the cerebral cortex. Conventional measure: cross-correlation. Slideshow 6735842 by alana-fulton
We present a peripheral vision model inspired by the cortical architecture discovered by Hubel and Wiesel. As with existing cortical models, this model contains alternating layers of simple cells, which employ tuning functions to increase specificity, and complex cells, which pool over simple cells to increase invariance. To extend the traditional cortical model, we introduce the option of eccentricity-dependent pooling and tuning parameters within a given model layer. This peripheral vision system can be used to model physiological data where receptive field sizes change as a function of eccentricity. This gives the user flexibility to test different theories about filtering and pooling ranges in the periphery. In a specific instantiation of the model, pooling and tuning parameters can increase linearly with eccentricity to model physiological data found in different layers of the visual cortex. Additionally, it can be used to introduce pre-cortical model layers such as retina and LGN. We have ...
This 12-month course is designed to provide you with in-depth training into the core problems in systems neuroscience, and will develop your understanding of the disciplines and techniques used to address these problems such as computer simulation modelling, data visualisation and neuroanatomy.. In semester one youll build on your existing knowledge, giving you a thorough understanding of the fundamentals of neuroscience, computational neuroscience and mathematical modelling. Once youve developed a solid foundation in these areas at the core of systems neuroscience, semester two will be devoted to advanced modules where youll tailor your learning and choose to specialise in one of two distinct routes of study: pathway 1 or pathway 2.. Pathway 1 will train you in methods for visualising brain networks, including electrophysiology, optical imaging, and functional magnetic resonance imaging, as well as experimental protocols. Pathway 2 will build on your theoretical skills in this area of ...
The totality of mechanisms involved in the process of information transmission is immensely complex, comprised as it is of biochemical, biophysical, and ultrastructural elements. More-over, these are...
The study is designed to identify specific patterns of brain functional activity associated with chronic, moderate to severe tinnitus through the use of resting-state MEG scans. Robust patterns identified in this study will be used as a biomarker for subsequent clinical evaluation of experimental drug treatments for tinnitus. This study will conduct MEG scans on approximately 30 to 75 subjects with tinnitus and approximately 15 healthy control subjects. MEG scans will be obtained for each subject following screening, clinical and tinnitus evaluations. A subset of 6 subjects from the tinnitus cohort will be invited to undergo evoked auditory assessment during an extended MEG scan session to identify cortical regions that respond to the auditory stimulus. These six subjects also will be evaluated with a single structural MRI scan to support high-resolution mapping of the localized cortical regions. MEG data will be analyzed to identify patterns of brain activity that are specifically associated ...
Biological neurons are good examples of a threshold deviceâ€this is why neural systems are in the focus when looking for realization of Stochastic Resonance (SR) and Spatiotemporal Stochastic Resonance (STSR) phenomena. There are two different ways to simulate neural systemsâ€one based on differential equations, the other based on a simple threshold model. In this talk the effect of noise on neural systems will be discussed using both ways of modelling. The results so far suggest that SR and STSR do occur in models of neural systems. However, how significant is the role played by these phenomena and what implications might they have on neurobiology is still a question. © 2000 American Institute of Physics ...
It is our great pleasure to welcome you to the 11th International Conference on Neural Information Processing (ICONIP 2004) to be held in Calcutta. ICONIP 2004 is organized jointly by the Indian Stati
20) Spike-timing dependent plasticity: Getting the brain from correlation to causation (Levy - 1983, Sakmann - 1994, Bi & Poo - 1998, Dan - 2002). Hebbs original proposal was worded as such: When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that As efficiency, as one of the cells firing B, is increased. [emphasis added]. The phrase takes part in firing implies causation of Bs activity via As activity, not simply a correlation of the two.. There are several ways to go beyond correlation to infer causation. One method is to observe that one event (e.g., cell As activity) comes just before the caused event (e.g., cell Bs activity).. In 1983 Levy showed with hippocampal slices that electrically stimulating cell A to fire before cell B will cause long-lasting strengthening of the synapse from cell A to cell B. However, when the opposite occurs, and ...
Scientists from the Max Planck Institute for Biological Cybernetics have now developed a novel multimodal methodology called neural event-triggered functional magnetic resonance imaging (NET-fMRI) and presented the very first results obtained using it in experiments with both anesthetized and awake, behaving monkeys. The new methodology uses multiple-contact electrodes in combination with functional magnetic resonance imaging (fMRI) of the entire brain to map widespread networks of neurons that are activated by local, structure-specific neural events. Many invasive studies in nonhuman primates and clinical investigations in human patients have demonstrated that the hippocampus, one of the oldest, most primitive brain structures, is largely responsible for the long term retention of information regarding places, specific events, and their contexts, that is, for the retention of so-called declarative memories. Without the hippocampus a person may be able to learn a manual task over a period of ...
RA1 will take a leading role in the development of the neural circuit model. This will be a spiking neural model that copies one-to-one the neurons in the olfactory learning pathway of Drosophila larvae. It will be interfaced to a behavioural simulation, and to a robot model, and will be used to test hypotheses about the key mechanisms and locations of synaptic modification, and how the acquisition of associations interacts with behavioural control. Predictions from the model will be tested by our project partners using neurogenetic methods to measure and alter neural function in freely behaving animals ...
Connect and collaborate with Trevor Bekolay, with research interests in Long-term memory and Spike-timing dependent plasticity, on Mendeley.
In particular, we would like to understand how neurons are optimized for their computational task. Therefore, we investigate the relationship between intrinsic neuronal properties and the signaling mechanisms shaped by them, as well as their relevance for network behavior. The aim is to link the molecular level of ion channels to characteristics of neuronal firing, focusing on topics like information transfer, synchronization, energy efficiency, and temperature robustness. To identify computational principles, conductance-based and phenomenological models are used in combination with data evaluation techniques and analytical approaches. We closely cooperate with experimental groups in order to test theoretical predictions ...
schmitt at cs.unc.edu (Charles Schmitt) wrote: ,Can anyone suggest what would be good review articles or book (chapters) ,describing how measured postsynaptic spike trains have been observed ,to be dependent on presynaptic spike trains. What Im searching for ,is to strengthen my knowledge of what statistical measures of spike trains, ,such as mean frequency, inter-spike interval, etc..., have been used ,to describe postsynaptic spike trains and how these statistics have any ,observed dependency on the same presynaptic statistics. Im particularly ,interested in such observed dependencies in visual cortex. ,Thanks, Charlie. The initial work here was done for the spinomotor system. A good paper is by Cope, Fetz, and Matsumura, Cross-correlation assessment of synaptic strength of single Ia fibre connections with triceps surae motorneurons in cats, J. Physiol., 390:161-188, 1982. This is not an easy read, at first, but is worth it. (There is an older literature for this system: search Kirkwood, ...
In my ongoing Ph.D. project, titled Exploring the effective communication of brain network dynamics under different states, we carried the first research to demonstrate significant brain-wide integration changes during three cognitive tasks using intracranial EEG [1]. In a second study, we provided the first demonstration of a whole-brain model integrating anatomical connectivity, functional activity and neuromodulator receptor density from multimodal imaging of healthy human participants [2], and in a third study, we investigated the relevant timescale for understanding spatiotemporal dynamics across the whole brain. We introduced a novel way to generate whole-brain neural dynamical activity at the millisecond scale from fMRI signals, and using the independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we showed that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data [3].. 1. Cruzat, J., Deco, ...
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When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity that is fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. We investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions using magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.
Retinal waves are bursts of activity occurring spontaneously in the developing retina of vertebrate species, contributing to the shaping of the visual system organization and disappear short after birth. Waves during development are a transient process which evolves dynamically exhibiting different features. Based on our previous modelling work [1,2], we now propose a classification of stage II retinal waves patterns as a function of acetylcholine coupling strength and a possible mechanism for waves generation. Our model predicts that spatiotemporal patterns evolve upon maturation or pharmacological manipulation and that waves emerge from a complete homogeneous state without the need of the variability of a neural population. Context & Motivation SACs dynamically change their synaptic coupling upon maturation Coupled cholinergic SACs [3] Cholinergic current evolution upon maturation [4]-70 mV 60mV 0 0.8 88 nM 300 nM 0.2 0.4 0.4 0.2 Bursting sAHP Increase of Calcium load during bursting Calcium controls
The Integrative Pharmacology and Systems Neuroscience Groups main line of research is the development of pharmacological and non-pharmacological (cognitive/electrical stimulation) approaches for the treatment of diseases that cause intellectual disability or cognitive impairment and psychiatric disorders (including Down syndrome, drug addiction, depression, schizophrenia, or dementia). Our emphasis is on understanding the underlying mechanisms of action and on developing treatments, biomarkers of exposure, efficacy and toxicity.. Moreover, the group is responsible for providing instruction in pharmacology, chemistry and toxicology to graduate and undergraduate students of both human biology and medicine tracks (Pompeu Fabra University, Autonomous University of Barcelona), and to train pre-doctoral and postdoctoral researchers.. The Integrative Pharmacology and Systems Neuroscience Group and the Proteomics Research Group from the Pompeu Fabra University constitute the BAPP (Bioanalysis, ...
Get this from a library! Systems neuroscience. [Albert C H Yu; Lina Li;] -- This edition of Advances in Neurobiology brings together experts in the emerging field of Systems Neuroscience to present an overview of this area of research. Topics covered include: how different ...
Acimovic, J., Mäki-Marttunen, T., & Linne, M-L. (2011). Computational study of structural changes in neuronal networks during growth: a model of dissociated neocortical cultures. teoksessa J-M. Fellous, & A. Prinz (Toimittajat), Twentieth Annual Computational Neuroscience Meeting: CNS*2011 (Vuosikerta 12 (Suppl 1), Sivut P203). [P203] (Annual Computational Neuroscience Meeting CNS; Vuosikerta 12). Stockholm: BioMed Central. https://doi.org/10.1186/1471-2202-12-S1-P203 ...