594 research outputs found
State Dependence of Stimulus-Induced Variability Tuning in Macaque MT
Behavioral states marked by varying levels of arousal and attention modulate
some properties of cortical responses (e.g. average firing rates or pairwise
correlations), yet it is not fully understood what drives these response
changes and how they might affect downstream stimulus decoding. Here we show
that changes in state modulate the tuning of response variance-to-mean ratios
(Fano factors) in a fashion that is neither predicted by a Poisson spiking
model nor changes in the mean firing rate, with a substantial effect on
stimulus discriminability. We recorded motion-sensitive neurons in middle
temporal cortex (MT) in two states: alert fixation and light, opioid
anesthesia. Anesthesia tended to lower average spike counts, without decreasing
trial-to-trial variability compared to the alert state. Under anesthesia,
within-trial fluctuations in excitability were correlated over longer time
scales compared to the alert state, creating supra-Poisson Fano factors. In
contrast, alert-state MT neurons have higher mean firing rates and largely
sub-Poisson variability that is stimulus-dependent and cannot be explained by
firing rate differences alone. The absence of such stimulus-induced variability
tuning in the anesthetized state suggests different sources of variability
between states. A simple model explains state-dependent shifts in the
distribution of observed Fano factors via a suppression in the variance of gain
fluctuations in the alert state. A population model with stimulus-induced
variability tuning and behaviorally constrained information-limiting
correlations explores the potential enhancement in stimulus discriminability by
the cortical population in the alert state.Comment: 36 pages, 18 figure
Supervised Learning in Multilayer Spiking Neural Networks
The current article introduces a supervised learning algorithm for multilayer
spiking neural networks. The algorithm presented here overcomes some
limitations of existing learning algorithms as it can be applied to neurons
firing multiple spikes and it can in principle be applied to any linearisable
neuron model. The algorithm is applied successfully to various benchmarks, such
as the XOR problem and the Iris data set, as well as complex classifications
problems. The simulations also show the flexibility of this supervised learning
algorithm which permits different encodings of the spike timing patterns,
including precise spike trains encoding.Comment: 38 pages, 4 figure
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Causal contribution and dynamical encoding in the striatum during evidence accumulation.
A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicate that cortical regions that are prominently associated with accumulating evidence, such as the posterior parietal cortex and the frontal orienting fields, may not be directly involved in this computation. Which, then, are the regions involved? Regions that are directly involved in evidence accumulation should directly influence the accumulation-based decision-making behavior, have a graded neural encoding of accumulated evidence and contribute throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy the three criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation
Advances in point process filters and their application to sympathetic neural activity
This thesis is concerned with the development of techniques for analyzing the sequences of stereotypical electrical impulses within neurons known as spikes. Sequences of spikes, also called spike trains, transmit neural information; decoding them often provides details about the physiological processes generating the neural activity. Here, the statistical theory of event arrivals, called point processes, is applied to human muscle sympathetic spike trains, a peripheral nerve signal responsible for cardiovascular regulation. A novel technique that uses observed spike trains to dynamically derive information about the physiological processes generating them is also introduced.
Despite the emerging usage of individual spikes in the analysis of human muscle sympathetic nerve activity, the majority of studies in this field remain focused on bursts of activity at or below cardiac rhythm frequencies. Point process theory applied to multi-neuron spike trains captured both fast and slow spiking rhythms. First, analysis of high-frequency spiking patterns within cardiac cycles was performed and, surprisingly, revealed fibers with no cardiac rhythmicity. Modeling spikes as a function of average firing rates showed that individual nerves contribute substantially to the differences in the sympathetic stressor response across experimental conditions. Subsequent investigation of low-frequency spiking identified two physiologically relevant frequency bands, and modeling spike trains as a function of hemodynamic variables uncovered complex associations between spiking activity and biophysical covariates at these two frequencies. For example, exercise-induced neural activation enhances the relationship of spikes to respiration but does not affect the extremely precise alignment of spikes to diastolic blood pressure.
Additionally, a novel method of utilizing point process observations to estimate an internal state process with partially linear dynamics was introduced. Separation of the linear components of the process model and reduction of the sampled space dimensionality improved the computational efficiency of the estimator. The method was tested on an established biophysical model by concurrently computing the dynamic electrical currents of a simulated neuron and estimating its conductance properties. Computational load reduction, improved accuracy, and applicability outside neuroscience establish the new technique as a valuable tool for decoding large dynamical systems with linear substructure and point process observations
Information Encoding by Individual Neurons and Groups of Neurons in the Primary Visual Cortex
How is information about visual stimuli encoded into the responses of neurons in the cerebral cortex? In this thesis, I describe the analysis of data recorded simultaneously from groups of up to eight nearby neurons in the primary visual cortices of anesthetized macaque monkeys. The goal is to examine the degree to which visual information is encoded into the times of action potentials in those responses (as opposed to the overall rate), and also into the identity of the neuron that fires each action potential (as opposed to the average activity across a group of nearby neurons). The data are examined with techniques modified from systems analysis, statistics, and information theory. The results are compared with expectations from simple statistical models of action-potential firing and from models that are more physiologically realistic. The major findings are: (1) that cortical responses are not renewal processes with time-varying firing rates, which means that information can indeed be encoded in the detailed timing of action potentials; (2) that these neurons encode the contrast of visual stimuli primarily into the time difference between stimulus and response onset, which is known as the latency; (3) that this so-called temporal coding serves as a mechanism by which the brain might discriminate among stimuli that evoke similar firing rates; (4) that action potentials preceded by interspike intervals of different durations can encode different features of a stimulus; (5) that the rate of overall information transmission can depend on the type of stimulus in a manner that differs from one neuron to the next; (6) that the rate at which information is transmitted specifically about stimulus contrast depends little on stimulus type; (7) that a substantial fraction of the information rate can be confounded among multiple stimulus attributes; and, most importantly, (8) that averaging together the responses of multiple nearby neurons leads to a significant loss of information that increases as more neurons are considered. These results should serve as a basis for direct investigation into the cellular mechanisms by which the brain extracts and processes the information carried in neuronal responses
Rewiring Neural Interactions by Micro-Stimulation
Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. In vitro, associative pairing of presynaptic spiking and stimulus-induced postsynaptic depolarization causes changes in the synaptic efficacy of the presynaptic neuron, when activated by extrinsic stimulation. In vivo, such paradigms can alter the responses of whole groups of neurons to stimulation. Here, we used in vivo spike-triggered stimulation to drive plastic changes in rat forelimb sensorimotor cortex, which we monitored using a statistical measure of functional connectivity inferred from the spiking statistics of the neurons during normal, spontaneous behavior. These induced plastic changes in inferred functional connectivity depended on the latency between trigger spike and stimulation, and appear to reflect a robust reorganization of the network. Such targeted connectivity changes might provide a tool for rerouting the flow of information through a network, with implications for both rehabilitation and brain–machine interface applications
Point Process Analysis Techniques: Theory and Applications to Complex Neurophysiological Systems
The main objective of this thesis is the development of analytical techniques and computational procedures for the analysis of complex neuronal networks. The techniques are applied to data obtained from elements of neurophysiological systems and simulated models to illustrate different aspects of these analysis tools. The nerve signals that occur within neuromuscular control systems are widely accepted to be stochastic in nature and are characterised by the times of occurrence of events, typically 1 msec, in duration of fixed amplitude, within the process. This provides the basis for considering these processes as stochastic point processes. The analytical approach adopted is similar to that used in ordinary time series and requires an inter-disciplinary approach involving linear and non-linear system analysis, estimation theory, probability theory and statistical inference. In this thesis a considerable amount of work is devoted to the discussion of these various areas related to the point process analysis techniques. In addition, neurophysiological concepts are discussed to provide a basis for the application of these techniques. These techniques are applied to the analysis of real data obtained from physiological experiments and simulated data generated by model neuronal networks of different complexities. Finally, some possibilities for future work opened up by the present investigation are considered. An introduction together with some historical notes are given in Chapter 1. The objectives of this thesis are set down and some general ideas of a point process and neurophysiology are introduced. The historical notes at the end of Chapter 1 are intended to give a picture of the trend of developments concerning point processes. Chapter 2 presents a simplified account of the relevant neurophysiological background. Some features of the neuromuscular system which lead to the use of point process analysis techniques are discussed. This is followed by a brief description of the organisation of neuromuscular system and some of its elements. The idea that the generation of an action potential occurs when the membrane potential at the trigger zone of a neurone exceeds the threshold forms the basis for the neurone model used in this thesis . The multiple input and output nature of neuromuscular systems in addition to the short duration of an action potential justify the realisation of a spike train as stochastic point process. Chapter 2 is concluded by considering some findings from the application of point process analysis techniques to data recorded from neuromuscular elements. The details of the techniques are then explained in Chapter 3-5. Chapter 3 gives a development of the theory of linear point process system analysis. The formal definitions of the assumptions involved, namely stationarity, mixing, and orderliness are explained. These assumptions are important in simplifying the theories involved and are seen to be valid in our applications. Theories for univariate, bivariate and multi-variate point processes are considered. The asymptotic value of the auto- spectrum of a point process is shown to be a non-zero constant, which marks the distinction from the auto-spectrum of an ordinary time series. Various quantities in both time and frequency domains are introduced and, among them, the coherence function and its partial and multiple forms are explained in particular details. The application of coherence is emphasised in Chapter 6. Since the processes involved are stochastic in nature, appropriate estimation procedures for the time and frequency domain quantities should be used. Chapter 4 is devoted to explaining the estimation procedure used and the statistical properties of these estimates. Also the Poisson point process - which possesses similar properties to Gaussian white noise in the case of ordinary time series - is introduced. The importance of the Poisson point process lies on the fact that it may be used as a 'reference process' to indicate departure of independence within a point process. At the end of Chapter 4, the confidence intervals of the time and frequency domain estimates under the hypothesis of independence are developed. The confidence interval approach forms the basis of inferring whether there is any significant association between processes or within a process. Chapter 5 describes briefly the implementation of the neurophysiological and simulation experiments. The digital algorithm for generating the exponential and Gaussian variables to provide the required stimuli in the experiment are explained. The neurone model, which is the building block of more complicated neuronal networks, is also described. Chapter 6 presents results and discussion. First some simulated spike trains of different structures are analysed using histogram, auto-intensity and auto-spectrum. The histogram is found to be least sensitive in revealing significant information concerning the processes. (Abstract shortened by ProQuest.)
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