Neural field model of binocular rivalry waves

We present a neural field model of binocular rivalry waves in visual cortex. For each eye we consider a one–dimensional network of neurons that respond maximally to a particular feature of the corresponding image such as the orientation of a grating stimulus. Recurrent connections within each one-dimensional network are assumed to be excitatory, whereas connections between the two networks are inhibitory (cross-inhibition). Slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We derive an analytical expression for the speed of a binocular rivalry wave as a function of various neurophysiological parameters, and show how properties of the wave are consistent with the wave–like propagation of perceptual dominance observed in recent psychophysical experiments. In addition to providing an analytical framework for studying binocular rivalry waves, we show how neural field methods provide insights into the mechanisms underlying the generation of the waves. In particular, we highlight the important role of slow adaptation in providing a “symmetry breaking mechanism” that allows waves to propagate

The effects of noise on binocular rivalry waves: a stochastic neural field model

We analyse the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how multiplicative noise in the activity variables leads to a diffusive–like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. The multiplicative noise also renormalizes the mean speed of the wave. We use our analysis to calculate the first passage time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation leads to quenched disorder in the neural fields during propagation of a wave

Attentional control via synaptic gain mechanisms in auditory streaming

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: All experimental data and model code are available in the github repository james-rankin/auditory-streaming: https://github.com/james-rankin/auditory-streamingAttention is a crucial component in sound source segregation allowing auditory objects of interest to be both singled out and held in focus. Our study utilizes a fundamental paradigm for sound source segregation: a sequence of interleaved tones, A and B, of different frequencies that can be heard as a single integrated stream or segregated into two streams (auditory streaming paradigm). We focus on the irregular alternations between integrated and segregated that occur for long presentations, so-called auditory bistability. Psychaoustic experiments demonstrate how attentional control, a listener's intention to experience integrated or segregated, biases perception in favour of different perceptual interpretations. Our data show that this is achieved by prolonging the dominance times of the attended percept and, to a lesser extent, by curtailing the dominance times of the unattended percept, an effect that remains consistent across a range of values for the difference in frequency between A and B. An existing neuromechanistic model describes the neural dynamics of perceptual competition downstream of primary auditory cortex (A1). The model allows us to propose plausible neural mechanisms for attentional control, as linked to different attentional strategies, in a direct comparison with behavioural data. A mechanism based on a percept-specific input gain best accounts for the effects of attentional control.Engineering and Physical Sciences Research Council (EPSRC)Swartz FoundationNY

Dynamical characteristics common to neuronal competition models.

Shpiro A, Curtu R, Rinzel J, Rubin N. Dynamical characteristics common to neuronal competition models. J Neurophysiol 97: [462][463][464][465][466][467][468][469][470][471][472][473] 2007. First published October 25, 2006; doi:10.1152/jn.00604.2006. Models implementing neuronal competition by reciprocally inhibitory populations are widely used to characterize bistable phenomena such as binocular rivalry. We find common dynamical behavior in several models of this general type, which differ in their architecture in the form of their gain functions, and in how they implement the slow process that underlies alternating dominance. We focus on examining the effect of the input strength on the rate (and existence) of oscillations. In spite of their differences, all considered models possess similar qualitative features, some of which we report here for the first time. Experimentally, dominance durations have been reported to decrease monotonically with increasing stimulus strength (such as Levelt's "Proposition IV"). The models predict this behavior; however, they also predict that at a lower range of input strength dominance durations increase with increasing stimulus strength. The nonmonotonic dependency of duration on stimulus strength is common to both deterministic and stochastic models. We conclude that additional experimental tests of Levelt's Proposition IV are needed to reconcile models and perception. I N T R O D U C T I O N Binocular rivalry occurs when two different images are presented to the two eyes. With such ambiguous stimuli, only one of the images is perceived at any given moment, with dominance switching between the two images in a haphazard manner. The average dominance durations are typically a few seconds. Several stimulus parameters have been shown to influence the dynamical characteristics of the perceptual alternations. In particular, increasing the contrast of the rivaling images has been shown to increase the frequency of percept switching, which implies a decrease in the mean dominance times, an observation known as "Levelt's Proposition IV" Reciprocal inhibition architecture is widely used to describe binocular rivalry and bistable perception in general. The dominant side of the system exerts a strong inhibitory influence on the competing side, so that the latter is suppressed. The switching in dominance between the two sides is realized by a slow negative feedback process, such as spike-frequency adaptation or synaptic depression, that weakens the inhibition either by decreasing the activity of the dominant side or by decreasing the connectivity between the sides and allows the suppressed population to become active. These general principles have been incorporated in numerous mathematical models of binocular rivalry In addition to studying these two models as formulated, we also consider two variations of the model of We identify the parameter regimes where each model shows behavior that is consistent with Levelt's Proposition IV. In addition, we demonstrate that all the models predict previously unreported types of behavior. Using stimulation strength as the control variable, we focus on its effect on the existence and rate of oscillations. In spite of the differences in architecture and mathematical formulation of the explored models, we find substantial generalities in their behavior. In all models, for very high stimulus strengths the two populations are simultaneously active at a high level. Just below this regime is a range for stimulus strength where the behavior of the system is oscillatory, with the dominance period of each percept decreasing as stimulus strength increases, in accordance with Levelt's Proposition IV (decreasing duration, or DD behavior). However, for input strengths below this range new regimes of behavior are discovered: first, a winner-take-all (nonrivaling steady dominance) behavior appears. Next, as stimulus strength is further reduced, another range of rivalry (oscillatory) behavior appears, but this time with the dominance periods increasing with increasing input (increasing duration, or ID behavior). Finally, at very low input strengths there is again a range where the two populations are simultaneously active, this time at a low leve

The role of apoptosis in <i>in vitro</i> and <i>in vivo</i> models of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease leading to motor neurons death. Although many studies were performed, the aetiology and the features of cellular death occurring in ALS are poorly understood, and studies carried out from autoptic samples do not allow to clarify early events occurring in ALS. To understand the basic mechanisms leading to motor neuronal death in ALS and to detect the different steps involved in motor neuron degeneration, alternative models are required. The wobbler mouse is one of the most reliable models of human motor neurodegenerative diseases. In the wobbler mouse neuromuscular deficits that are related to a selective vulnerability of cervical spinal cord motor neurons have an early onset and progress rapidly. In my project of thesis I first characterized apoptosis in primary cultures of motor neurons exposed to detrimental stimuli, and then I evaluated the possible involvement of apoptosis in the cervical spinal cord neurons of early symptomatic wobbler mice. In primary cultures of motor neurons I found that: I) BDNF deprivation produced apoptotic cell death, II) AMPA and kainate induced apoptotic or non apoptotic cell death depending on the experimental conditions utilized, III) EPO selectively protected motor neurons committed to dye apoptically. In wobbler mice I found that: I) mitochondrial activity is reduced but caspase9 -mediated apoptosis does not seem involved, II) glutamate induced excitotoxicity does not seem involved in motor neuron degeneration, III) markers of neuroinflammation, such as activated microglia and TNF-a, appeared highly increased in the cervical region of early symptomatic wobbler mice but this process does not seem to induce the activation of caspase 8-mediated apoptosis. Pharmacological treatments confirmed that an antiglutamatergic agent (RPR 119990) and an antiapoptotic agent (EPO) were ineffective, while riluzole showed protective effects. Although alternative cell death pathways should be investigated, my study excludes apoptosis as the mechanism of death in cervical motor neurons of wobbler mice

Analysis of Oscillations in a Reciprocally Inhibitory Network with Synaptic Depression

We present and analyze a model of a two-cell reciprocally inhibitory network that oscillates. The principal mechanism of oscillation is short-term synaptic depression. Using a simple model of depression, and analyzing the system in certain limits, we can derive analytical expressions for various features of the oscillation, including the parameter regime in which stable oscillations occur as well as the period and amplitude of these oscillations. These expressions are functions of three parameters: the time constant of depression, the synaptic strengths, and the amount of tonic excitation the cells receive. We compare our analytical results with the output of numerical simulations, and obtain good agreement between the two. Based on our analysis, we conclude that the oscillations in our network are qualitatively di#erent from those in networks which oscillate due to postinhibitory rebound, spike-frequency adaptation, or other intrinsic (rather than synaptic) adaptational mechanisms. In particular, our network can only oscillate via the synaptic escape mode of Skinner, Kopell and Marder (1994)