Induced Gamma-band Activity Elicited by Visual Representation of Unattended Objects

Peer reviewedPostprin

On the complex dynamics of intracellular ganglion cell light responses in the cat retina

We recorded intracellular responses from cat retinal ganglion cells to sinusoidal flickering lights and compared the response dynamics to a theoretical model based on coupled nonlinear oscillators. Flicker responses for several different spot sizes were separated in a 'smooth' generator (G) potential and eorresponding spike trains. We have previously shown that the G-potential reveals complex, stimulus dependent, oscillatory behavior in response to sinusoidally flickering lights. Such behavior could be simulated by a modified van der Pol oscillator. In this paper, we extend the model to account for spike generation as well, by including extended Hodgkin-Huxley equations describing local membrane properties. We quantified spike responses by several parameters describing the mean and standard deviation of spike burst duration, timing (phase shift) of bursts, and the number of spikes in a burst. The dependence of these response parameters on stimulus frequency and spot size could be reproduced in great detail by coupling the van der Pol oscillator, and Hodgkin-Huxley equations. The model mimics many experimentally observed response patterns, including non-phase-locked irregular oscillations. Our findings suggest that the information in the ganglion cell spike train reflects both intraretinal processing, simulated by the van der Pol oscillator) and local membrane properties described by Hodgkin-Huxley equations. The interplay between these complex processes can be simulated by changing the coupling coefficients between the two oscillators. Our simulations therefore show that irregularities in spike trains, which normally are considered to be noise, may be interpreted as complex oscillations that might earry information.Whitehall Foundation (S93-24

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R[superscript 2] as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R[superscript 2]~5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.National Institutes of Health (U.S.) (K25 NS052422-02)National Institutes of Health (U.S.) (DP1 ODOO3646

Spatiotemporal multi-resolution approximation of the Amari type neural field model

Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework

A multilayered approach to the automatic analysis of the multifocal electroretinogram

The multifocal electroretinogram (mfERG) provides spatial and temporal information on the retina’s function in an objective manner, making it a valuable tool for monitoring a wide range of retinal abnormalities. Analysis of this clinical test can however be both difficult and subjective, particularly if recordings are contaminated with noise, for example muscle movement or blinking. This can sometimes result in inconsistencies in the interpretation process. An automated and objective method for analysing the mfERG would be beneficial, for example in multi-centre clinical trials when large volumes of data require quick and consistent interpretation. The aim of this thesis was therefore to develop a system capable of standardising mfERG analysis. A series of methods aimed at achieving this are presented. These include a technique for grading the quality of a recording, both during and after a test, and several approaches for stating if a waveform contains a physiological response or no significant retinal function. Different techniques are also utilised to report if a response is within normal latency and amplitude values. The integrity of a recording was assessed by viewing the raw, uncorrelated data in the frequency domain; clear differences between acceptable and unacceptable recordings were revealed. A scale ranging from excellent to unreportable was defined for the recording quality, first in terms of noise resulting from blinking and loss of fixation, and secondly, for muscle noise. 50 mfERG tests of varying recording quality were graded using this method with particular emphasis on the distinction between a test which should or should not be reported. Three experts also assessed the mfERG recordings independently; the grading provided by the experts was compared with that of the system. Three approaches were investigated to classify a mfERG waveform as ‘response’ or ‘no response’ (i.e. whether or not it contained a physiological response): artificial neural networks (ANN); analysis of the frequency domain profile; and the signal to noise ratio. These techniques were then combined using an ANN to provide a final classification for ‘response’ or ‘no response’. Two methods were studied to differentiate responses which were delayed from those within normal timing limits: ANN; and spline fitting. Again the output of each was combined to provide a latency classification for the mfERG waveform. Finally spline fitting was utilised to classify responses as ‘decreased in amplitude’ or ‘not decreased’. 1000 mfERG waveforms were subsequently analysed by an expert; these represented a wide variety of retinal function and quality. Classifications stated by the system were compared with those of the expert to assess its performance. An agreement of 94% was achieved between the experts and the system when making the distinction between tests which should or should not be reported. The final system classified 95% of the 1000 mfERG waveforms correctly as ‘response’ or ‘no response’. Of those said to represent an area of functioning retina it concurred with the expert for 93% of the responses when categorising them as normal or abnormal in terms of their P1 amplitude and latency. The majority of misclassifications were made when analysing waveforms with a P1 amplitude or latency close to the boundary between normal and abnormal. It was evident that the multilayered system has the potential to provide an objective and automated assessment of the mfERG test; this would not replace the expert but can provide an initial analysis for the expert to review

Rapid mapping of visual receptive fields by filtered back-projection: application to multi-neuronal electrophysiology and imaging

Neurons in the visual system vary widely in the spatiotemporal properties of their receptive fields (RFs), and understanding these variations is key to elucidating how visual information is processed. We present a new approach for mapping RFs based on the filtered back projection (FBP), an algorithm used for tomographic reconstructions. To estimate RFs, a series of bars were flashed across the retina at pseudo‐random positions and at a minimum of five orientations. We apply this method to retinal neurons and show that it can accurately recover the spatial RF and impulse response of ganglion cells recorded on a multi‐electrode array. We also demonstrate its utility for in vivo imaging by mapping the RFs of an array of bipolar cell synapses expressing a genetically encoded Ca2+ indicator. We find that FBP offers several advantages over the commonly used spike‐triggered average (STA): (i) ON and OFF components of a RF can be separated; (ii) the impulse response can be reconstructed at sample rates of 125 Hz, rather than the refresh rate of a monitor; (iii) FBP reveals the response properties of neurons that are not evident using STA, including those that display orientation selectivity, or fire at low mean spike rates; and (iv) the FBP method is fast, allowing the RFs of all the bipolar cell synaptic terminals in a field of view to be reconstructed in under 4 min. Use of the FBP will benefit investigations of the visual system that employ electrophysiology or optical reporters to measure activity across populations of neurons

A role for spindles in the onset of rapid eye movement sleep.

Sleep spindle generation classically relies on an interplay between the thalamic reticular nucleus (TRN), thalamo-cortical (TC) relay cells and cortico-thalamic (CT) feedback during non-rapid eye movement (NREM) sleep. Spindles are hypothesized to stabilize sleep, gate sensory processing and consolidate memory. However, the contribution of non-sensory thalamic nuclei in spindle generation and the role of spindles in sleep-state regulation remain unclear. Using multisite thalamic and cortical LFP/unit recordings in freely behaving mice, we show that spike-field coupling within centromedial and anterodorsal (AD) thalamic nuclei is as strong as for TRN during detected spindles. We found that spindle rate significantly increases before the onset of rapid eye movement (REM) sleep, but not wakefulness. The latter observation is consistent with our finding that enhancing spontaneous activity of TRN cells or TRN-AD projections using optogenetics increase spindle rate and transitions to REM sleep. Together, our results extend the classical TRN-TC-CT spindle pathway to include non-sensory thalamic nuclei and implicate spindles in the onset of REM sleep

Delta rhythms as a substrate for holographic processing in sleep and wakefulness

PhD ThesisWe initially considered the theoretical properties and benefits of so-called holographic processing in a specific type of computational problem implied by the theories of synaptic rescaling processes in the biological wake-sleep cycle. This raised two fundamental questions that we attempted to answer by an experimental in vitro electrophysiological approach. We developed a comprehensive experimental paradigm based on a pharmacological model of the wake-sleep-associated delta rhythm measured with a Utah micro-electrode array at the interface between primary and associational areas in the rodent neocortex. We first verified that our in vitro delta rhythm model possessed two key features found in both in vivo rodent and human studies of synaptic rescaling processes in sleep: The first property being that prior local synaptic potentiation in wake leads to increased local delta power in subsequent sleep. The second property is the reactivation in sleep of neural firing patterns observed prior to sleep. By reproducing these findings we confirmed that our model is arguably an adequate medium for further study of the putative sleep-related synaptic rescaling process. In addition we found important differences between neural units that reactivated or deactivated during delta; these were differences in cell types based on unit spike shapes, in prior firing rates and in prior spike-train-to-local-field-potential coherence. Taken together these results suggested a mechanistic chain of explanation of the two observed properties, and set the neurobiological framework for further, more computationally driven analysis. Using the above experimental and theoretical substrate we developed a new method of analysis of micro-electrode array data. The method is a generalization to the electromagnetic case of a well-known technique for processing acoustic microphone array data. This allowed calculation of: The instantaneous spatial energy flow and dissipation in the neocortical areas under the array; The spatial energy source density in analogy to well-known current source density analysis. We then refocused our investigation on the two theoretical questions that we hoped to achieve experimental answers for: Whether the state of the neocortex during a delta rhythm could be described by ergodic statistics, which we determined by analyzing the spectral properties of energy dissipation as a signature of the state of the dynamical system; A more explorative approach prompting an investigation of the spatiotemporal interactions across and along neocortical layers and areas during a delta rhythm, as implied by energy flow patterns. We found that the in vitro rodent neocortex does not conform to ergodic statistics during a pharmacologically driven delta or gamma rhythm. We also found a delta period locked pattern of energy flow across and along layers and areas, which doubled the processing cycle relative to the fundamental delta rhythm, tentatively suggesting a reciprocal, two-stage information processing hierarchy similar to a stochastic Helmholtz machine with a wake-sleep training algorithm. Further, the complex valued energy flow might suggest an improvement to the Helmholtz machine concept by generalizing the complex valued weights of the stochastic network to higher dimensional multi-vectors of a geometric algebra with a metric particularity suited for holographic processes. Finally, preliminary attempts were made to implement and characterize the above network dynamics in silico. We found that a qubit valued network does not allow fully holographic processes, but tentatively suggest that an ebit valued network may display two key properties of general holographic processing