Local Lateral Inhibition Research Articles

The Spatial Leaky Competing Accumulator Model

The Leaky Competing Accumulator model (LCA) of Usher and McClelland is able to simulate the time course of perceptual decision making between an arbitrary number of stimuli. Reaction times, such as saccadic latencies, produce a typical distribution that is skewed toward longer latencies and accumulator models have shown excellent fit to these distributions. We propose a new implementation called the Spatial Leaky Competing Accumulator (SLCA), which can be used to predict the timing of subsequent fixation durations during a visual task. SLCA uses a pre-existing saliency map as input and represents accumulation neurons as a two-dimensional grid to generate predictions in visual space. The SLCA builds on several biologically motivated parameters: leakage, recurrent self-excitation, randomness and non-linearity, and we also test two implementations of lateral inhibition. A global lateral inhibition, as implemented in the original model of Usher and McClelland, is applied to all competing neurons, while a local implementation allows only inhibition of immediate neighbors. We trained and compared versions of the SLCA with both global and local lateral inhibition with use of a genetic algorithm, and compared their performance in simulating human fixation latency distribution in a foraging task. Although both implementations were able to produce a positively skewed latency distribution, only the local SLCA was able to match the human data distribution from the foraging task. Our model is discussed for its potential in models of salience and priority, and its benefits as compared to other models like the Leaky integrate and fire network.

The Effect of Serotonin Receptor 5-HT1B on Lateral Inhibition between Spiny Projection Neurons in the Mouse Striatum.

The principal neurons of the striatum, the spiny projection neurons (SPNs), make inhibitory synaptic connections with each other via collaterals of their main axon, forming a local lateral inhibition network. Serotonin, acting via the 5-HT1B receptor, modulates neurotransmitter release from SPN terminals in striatal output nuclei, but the role of 5-HT1B receptors in lateral inhibition among SPNs in the striatum is unknown. Here, we report the effects of 5-HT1B receptor activation on lateral inhibition in the mouse striatum. Whole-cell recordings were made from SPNs in acute brain slices of either sex, while optogenetically activating presynaptic SPNs or fast-spiking interneurons (FSIs). Activation of 5-HT1B receptors significantly reduced the amplitude of IPSCs evoked by optical stimulation of both direct and indirect pathway SPNs. This reduction was blocked by application of a 5-HT1B receptor antagonist. Activation of 5-HT1B receptors did not reduce the amplitude of IPSCs evoked from FSIs. These results suggest a new role for serotonin as a modulator of lateral inhibition among striatal SPNs. The 5-HT1B receptor may, therefore, be a suitable target for future behavioral experiments investigating the currently unknown role of lateral inhibition in the function of the striatum.SIGNIFICANCE STATEMENT We show that stimulation of serotonin receptors reduces the efficacy of lateral inhibition between spiny projection neurons (SPNs), one of the biggest GABAergic sources in the striatum, by activation of the serotonin 5-HT1B receptor. The striatum receives serotonergic input from the dorsal raphe nuclei and is important in behavioral brain functions like learning and action selection. Our findings suggest a new role for serotonin in modulating the dynamics of neural interactions in the striatum, which extends current knowledge of the mechanisms of the behavioral effects of serotonin.

Lateral Inhibition: Are two hands better than one?

This study investigated the differences in amplitude discrimination capacity between two stimuli delivered to adjacent fingertips on the same hand (contralateral delivery) and stimuli delivered to two fingers on opposite hands (bilateral delivery). The measures were obtained in order to study the impact of lateral inhibition via interhemispheric connections on cortical centers on opposite sides of the somatosensory cortex in comparison to lateral inhibition occurring between adjacent cortical centers within the same hemisphere. Using the Cortical Metrics Brain Gauge™ device, amplitude discrimination capacity of 37 healthy subjects was assessed at several different durations, ranging from 40 to 500 msec, of vibrotactile stimulation delivered contralaterally and bilaterally. The results demonstrate a significant difference in amplitude discrimination capacity between the two conditions for stimulus duration of 200ms, with performance being better for the contralateral delivery of the stimuli than the bilateral condition for most tested durations. Task performance was roughly the same for the two conditions at the extremes of short (40ms) and long (500ms) stimulus durations. Amplitude discrimination capacity improved with longer stimulus durations in both bilateral and contralateral conditions. Though slight variation was observed at the level of each individual subject, overall, it is clear that local lateral inhibition plays a role in assessing the two stimuli delivered to the same hand that gives same-handed discrimination an advantage over two-handed discrimination. Additionally, the trends identified may be useful in guiding future experimentation that investigates clinical assessments of deficits in cortical processing that is mediated by callosal connections.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition.

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.

Competition improves robustness against loss of information.

A substantial number of works have aimed at modeling the receptive field properties of the primary visual cortex (V1). Their evaluation criterion is usually the similarity of the model response properties to the recorded responses from biological organisms. However, as several algorithms were able to demonstrate some degree of similarity to biological data based on the existing criteria, we focus on the robustness against loss of information in the form of occlusions as an additional constraint for better understanding the algorithmic level of early vision in the brain. We try to investigate the influence of competition mechanisms on the robustness. Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections. Each of those methods is known to be capable of developing receptive fields comparable to those of V1 simple-cells. Since measuring the robustness of methods having simple-cell like receptive fields against occlusion is difficult, we measure the robustness using the classification accuracy on the MNIST hand written digit dataset. For this we trained all methods on the training set of the MNIST hand written digits dataset and tested them on a MNIST test set with different levels of occlusions. We observe that methods which employ competitive mechanisms have higher robustness against loss of information. Also the kind of the competition mechanisms plays an important role in robustness. Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule.

Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1

Lateral inhibition is typically used to repel neural receptive fields. Here we introduce an additional learning mechanism that modifies the plasticity for feedforward synapses based on lateral interactions. We show a model, based on evidence from biological recordings[1], in which a heterosynaptic learning rule in conjunction with lateral inhibition introduces competition among neurons for input features. We demonstrate an STDP learning rule for feedforward connections to a spiking neuron where plasticity is modulated by activity of neighboring neurons. We apply the learning rule to a spiking model of primary visual cortex. In our model, input to V1 cortical neurons comes from the magnocellular pathway of a simulated spiking retina responding to saccades over a natural image. A group of neurons in V1 respond to a particular input feature and convey information that they have spiked to neighboring neurons by way of specialized lateral connections. The connections convey a direct signal of recent spiking activity. Alternatively, lateral inhibitory synapses and the level of inhibition to a neuron can also be used as this signal. If this recent neighbor activity signal is high, feedforward plasticity to the neuron becomes a simple flat depression as opposed to regular exponential STDP, as shown in Figure ​Figure1A.1A. Thus, late spiking neurons are prevented from developing receptive fields similar to those of earlier spiking neurons for a given input feature. This rule in conjunction with lateral inhibition and slow weight updrift ensures competition between neurons for features over a long timescale. Figure 1 A. Detail of the heterosynaptic rule. Neurons N1 and N2 fire early for the input, and their feedforward synapses undergo normal STDP. They send an activity signal to N3 and N4, whose feedforward synaptic weights are then depressed to prevent learning ... Our V1 model is built on a spatially distributed grid of single-compartment spiking neurons with parameters configured to model regular spiking cortical pyramidal cells, as detailed in [2]. The model has plastic excitatory feedforward connections and local lateral inhibition between spatially proximal neurons. In our model, we observe a better overall coverage of orientation selectivity of V1 Layer 4 neurons with our heterosynaptic rule than without it. We find a larger range of orientations and less redundancy of receptive field features between neighboring neurons, as shown in Figure ​Figure1B.1B. Such a rule could be used in a generic spiking cortical architecture to enforce independence of neural receptive fields.

Object-based visual neglect: a computational hypothesis.

Some patients with damage to the right parietal cortex show neglect for the left half of each of a series of objects shown in a horizontal row in the visual field. The neglect is thus not based on failure to see objects in any part of left visual space, but is object-based. We show that in a model of attention with separate V1, object (inferior temporal cortex, IT) and spatial (posterior parietal cortex, PP) modules the effect can arise after graded damage increasing towards the right of the PP module when the lateral inhibition between neurons in the PP and V1 modules is short-range. The local lateral inhibition produces high contrast effects at the edges of each object, and it is when this interacts with gradually increasing damage through the left visual field that the visibility of the left half of each object is especially impaired. This result was found in a formal model completely specified by mean field equations to quantify the dynamical interactions between the modules. This is the first quantitative account of object-based neglect found in humans with right parietal cortex damage, and provides evidence that the model of attention we describe can account for even detailed and extraordinary phenomena that can occur in visual perception.

BINOCULAR RIVALRY AS A FUNCTION OF SPATIAL QUANTISATION OF THE IMAGES OF FACES: PRECATEGORICAL LEVEL CONTROLS IT

1. Introduction Preamble. The concept of consciousness holds one of the central roles in social science and humanities. But its connotations in modern scientific research may be quite surprising for many philosophers, sociologists, linguists and psychologists. The phenomenal aspects of consciousness that are related to immediate awareness of the perceptual objects have become a subject matter of research of a very active and rather numerous international group of researchers who regard consciousness as an experimental variable that can be studied by neuroscientific and psychophysical methods. Several specialists such as Bernard Baars, Francis Crick, Christof Koch, Gerald Edelman, Nikos Logothetis and others stress the importance of this strategy in taking steps towards clarification of this quite controversial concept. Some leading philosophers like John Searle, Paul Churchland, David Chalmers also believe in importance of empirical research on this subject. Understanding the nature of consciousness is one of the biggest challenges before science. It is still unsurpassed despite many centuries long quest for understanding. In the present article I will show one example how experimental methods can be used for getting empirical data about the ways our brain/mind processes deal with perceptual information so as to create phenomenal experience for some of it and deprive some of it from this property. First, I will introduce the phenomenon. Subsequently an exploratory experiment will be described and its results interpreted. Hopefully, introduction of an exploratory experimental study of consciousness-related processes to the readers who may be more familiar with traditional speculative approaches could enrich their set of perspectives for dealing with this elusive property of living creatures--consciousness--theoretically. The phenomenon. If two sufficiently different images are presented dichoptically--one to the right eye and the other to the left eye--, they will not blend into a combined percept that would include explicit information from both eyes simultaneously. Instead, the images begin to alternate in the awareness of the observer. Conscious perception accepts images one at a time. This phenomenon has been known as binocular or interocular rivalry. Typical rates of alternation or reversal have values from about 0.5 to about several seconds. In order to experience wholistic exclusive rivalry (i.e., where all what is experienced corresponds to the input from one eye only), the sizes of the competing images should not be larger than about 2 degrees of the visual angle. Binocular rivalry has become one of the mainstream experimental paradigms in tackling important problems of visual cognition such as what is the nature of the mechanisms of visual awareness, figure-ground segregation, feature binding, and interactions of perception and attention (Andrews 2001, Blake and Logothetis 2002, Bonneh et al. 2001, Crick 1994, Fries et al. 1997, Kreiman et al. 2002, Leopold and Logothetis 1996, 1999, Sasaki and Gyoba 2002, Sheinberg and Logothetis 1997, Sengpiel 1997, Tong and Engel 2001, Tononi et al. 1998, Wilson et al. 2001, Wolfe 1996). The main emphasis has gradually shifted from the analysis of the involvement of relatively low level mechanisms such as brightness contrast, local lateral inhibition, or mutual inhibition of alternative monocular channels, to the analysis of relatively higher level processes of figural selection, object coding, and spontaneous attention (Logothetis, Leopold and Sheinberg 1996, Sengpiel 1997). Indeed, what seems to be the basis of rivalry in conscious vision is, according to what some authors claim, primarily related to the formation and selection of cognitive representations for unequivocally interpretable objects regardless of from which one of the two eyes the input is coming, rather than to the low-level competition between the sensory signals provided by different eyes (Logothetis Leopold and Sheinberg 1996, Kovacs et al. …

Local lateral connectivity of inhibitory clutch cells in layer 4 of cat visual cortex (area 17).

To characterise spatially a major component of the anatomical basis of local lateral inhibition in layer 4 of cat visual cortex (area 17), we analysed the lateral distribution of neuronal somata postsynaptic to electrophysiologically characterised GABAergic clutch (basket) cell axons (CC1 and CC2). We report two main results. First, the clutch cell axons appear to show isotropic lateral connectivity near their cell body (less than 50 microm radius), but beyond this core region they show anisotropic lateral connectivity, preferring particular angular sectors around their cell body. Second, we estimated the probability of lateral connection for each axon arbor as a function of radial distance from the parent soma. We found that this radial function has a brief rising phase, to a peak at 30-45 microm, and a longer, exponential decaying phase, with a space constant of around 50 microm. The shape of the radial connection probability function suggests that most lateral inhibitory connections of clutch cells are formed with neurons in nearest-neighbour cortical columns. Taken together, the results suggest that these individual layer-4 clutch cell axons may inhibit all (isotropic) nearest-neighbour cortical columns with a relatively high probability of connection, but outside this core region may provide a type of anisotropic lateral inhibition of cortical columns with a radially decreasing probability of connection.

Local lateral inhibition: a key to spike synchronization?

Starting from the idea that neural group activity as such is unlikely to be immediately relevant for neural synchronization, we investigate mechanisms that act at the level of individual nerve impulses (spikes). Hence, we consider populations of formal spike-emitting `leaky integrate and fire' neurons instead of networks built from non-spiking oscillators. After outlining the principle of synchronization for basic forms of recurrent impulse coupling by using a pair of simplified formal neurons, we show that local lateral inhibition results in robust impulse synchronization in networks with nonvanishing transmission delays.

Local lateral inhibition: a key to spike synchronization?

Local lateral inhibition: a key to spike synchronization?

A context-dependent illusion in the perception of velocity

It has so far been assumed that velocity contrast effects can be explained by local lateral inhibition processes which act to enhance the difference in velocity between a target and its immediate surround. A new velocity illusion is here reported that seriously questions this notion.