Slow GABAA mediated synaptic transmission in rat visual cortex

<p>Abstract</p> <p>Background</p> <p>Previous reports of inhibition in the neocortex suggest that inhibition is mediated predominantly through GABA_Areceptors exhibiting fast kinetics. Within the hippocampus, it has been shown that GABA_Aresponses can take the form of either fast or slow response kinetics. Our findings indicate, for the first time, that the neocortex displays synaptic responses with slow GABA_Areceptor mediated inhibitory postsynaptic currents (IPSCs). These IPSCs are kinetically and pharmacologically similar to responses found in the hippocampus, although the anatomical specificity of evoked responses is unique from hippocampus. Spontaneous slow GABA_AIPSCs were recorded from both pyramidal and inhibitory neurons in rat visual cortex.</p> <p>Results</p> <p>GABA_Aslow IPSCs were significantly different from fast responses with respect to rise times and decay time constants, but not amplitudes. Spontaneously occurring GABA_Aslow IPSCs were nearly 100 times less frequent than fast sIPSCs and both were completely abolished by the chloride channel blocker, picrotoxin. The GABA_Asubunit-specific antagonist, furosemide, depressed spontaneous and evoked GABA_Afast IPSCs, but not slow GABA_A-mediated IPSCs. Anatomical specificity was evident using minimal stimulation: IPSCs with slow kinetics were evoked predominantly through stimulation of layer 1/2 apical dendritic zones of layer 4 pyramidal neurons and across their basal dendrites, while GABA_Afast IPSCs were evoked through stimulation throughout the dendritic arborization. Many evoked IPSCs were also composed of a combination of fast and slow IPSC components.</p> <p>Conclusion</p> <p>GABA_Aslow IPSCs displayed durations that were approximately 4 fold longer than typical GABA_Afast IPSCs, but shorter than GABA_B-mediated inhibition. The anatomical and pharmacological specificity of evoked slow IPSCs suggests a unique origin of synaptic input. Incorporating GABA_Aslow IPSCs into computational models of cortical function will help improve our understanding of cortical information processing.</p

A computational study on altered theta-gamma coupling during learning and phase coding

There is considerable interest in the role of coupling between theta and gamma oscillations in the brain in the context of learning and memory. Here we have used a neural network model which is capable of producing coupling of theta phase to gamma amplitude firstly to explore its ability to reproduce reported learning changes and secondly to memory-span and phase coding effects. The spiking neural network incorporates two kinetically different GABAA receptor-mediated currents to generate both theta and gamma rhythms and we have found that by selective alteration of both NMDA receptors and GABAA,slow receptors it can reproduce learning-related changes in the strength of coupling between theta and gamma either with or without coincident changes in theta amplitude. When the model was used to explore the relationship between theta and gamma oscillations, working memory capacity and phase coding it showed that the potential storage capacity of short term memories, in terms of nested gamma-subcycles, coincides with the maximal theta power. Increasing theta power is also related to the precision of theta phase which functions as a potential timing clock for neuronal firing in the cortex or hippocampus

Adaptive Filtering Enhances Information Transmission in Visual Cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depend on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio

Local biases drive, but do not determine, the perception of illusory trajectories

When a dot moves horizontally across a set of tilted lines of alternating orientations, the dot appears to be moving up and down along its trajectory. This perceptual phenomenon, known as the slalom illusion, reveals a mismatch between the veridical motion signals and the subjective percept of the motion trajectory, which has not been comprehensively explained. In the present study, we investigated the empirical boundaries of the slalom illusion using psychophysical methods. The phenomenon was found to occur both under conditions of smooth pursuit eye movements and constant fixation, and to be consistently amplified by intermittently occluding the dot trajectory. When the motion direction of the dot was not constant, however, the stimulus display did not elicit the expected illusory percept. These findings confirm that a local bias towards perpendicularity at the intersection points between the dot trajectory and the tilted lines cause the illusion, but also highlight that higher-level cortical processes are involved in interpreting and amplifying the biased local motion signals into a global illusion of trajectory perception

Local biases drive, but do not determine, the perception of illusory trajectories

When a dot moves horizontally across a set of tilted lines of alternating orientations, the dot appears to be moving up and down along its trajectory. This perceptual phenomenon, known as the slalom illusion, reveals a mismatch between the veridical motion signals and the subjective percept of the motion trajectory, which has not been comprehensively explained. In the present study, we investigated the empirical boundaries of the slalom illusion using psychophysical methods. The phenomenon was found to occur both under conditions of smooth pursuit eye movements and constant fixation, and to be consistently amplified by intermittently occluding the dot trajectory. When the motion direction of the dot was not constant, however, the stimulus display did not elicit the expected illusory percept. These findings confirm that a local bias towards perpendicularity at the intersection points between the dot trajectory and the tilted lines cause the illusion, but also highlight that higher-level cortical processes are involved in interpreting and amplifying the biased local motion signals into a global illusion of trajectory perception

Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

Background: The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion activity. In contrast, selectionist models focus on the motion computation at positions with 2D features. Methodology/Principal Findings: Recent experiments revealed that neither of the two concepts alone is sufficient to explain all experimental data and that most of the existing models cannot account for the complex behaviour found. MT pattern selectivity changes over time for stimuli like type II plaids from vector average to the direction computed with an intersection of constraint rule or by feature tracking. Also, the spatial arrangement of the stimulus within the receptive field of a MT cell plays a crucial role. We propose a recurrent neural model showing how feature integration and selection can be combined into one common architecture to explain these findings. The key features of the model are the computation of 1D and 2D motion in model area V1 subpopulations that are integrated in model MT cells using feedforward and feedback processing. Our results are also in line with findings concerning the solution of the aperture problem. Conclusions/Significance: We propose a new neural model for MT pattern computation and motion disambiguation that i

Using Evolutionary Algorithms for Fitting High-Dimensional Models to Neuronal Data

In the study of neurosciences, and of complex biological systems in general, there is frequently a need to fit mathematical models with large numbers of parameters to highly complex datasets. Here we consider algorithms of two different classes, gradient following (GF) methods and evolutionary algorithms (EA) and examine their performance in fitting a 9-parameter model of a filter-based visual neuron to real data recorded from a sample of 107 neurons in macaque primary visual cortex (V1). Although the GF method converged very rapidly on a solution, it was highly susceptible to the effects of local minima in the error surface and produced relatively poor fits unless the initial estimates of the parameters were already very good. Conversely, although the EA required many more iterations of evaluating the model neuron’s response to a series of stimuli, it ultimately found better solutions in nearly all cases and its performance was independent of the starting parameters of the model. Thus, although the fitting process was lengthy in terms of processing time, the relative lack of human intervention in the evolutionary algorithm, and its ability ultimately to generate model fits that could be trusted as being close to optimal, made it far superior in this particular application than the gradient following methods. This is likely to be the case in many further complex systems, as are often found in neuroscience

A Neurophysiologically Plausible Population Code Model for Feature Integration Explains Visual Crowding

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called “crowding”. Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, “compulsory averaging”, and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality

Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia

Cyclic and Sleep-Like Spontaneous Alternations of Brain State Under Urethane Anaesthesia

Background: Although the induction of behavioural unconsciousness during sleep and general anaesthesia has been shown to involve overlapping brain mechanisms, sleep involves cyclic fluctuations between different brain states known as active (paradoxical or rapid eye movement: REM) and quiet (slow-wave or non-REM: nREM) stages whereas commonly used general anaesthetics induce a unitary slow-wave brain state. Methodology/Principal Findings: Long-duration, multi-site forebrain field recordings were performed in urethaneanaesthetized rats. A spontaneous and rhythmic alternation of brain state between activated and deactivated electroencephalographic (EEG) patterns was observed. Individual states and their transitions resembled the REM/nREM cycle of natural sleep in their EEG components, evolution, and time frame (,11 minute period). Other physiological variables such as muscular tone, respiration rate, and cardiac frequency also covaried with forebrain state in a manner identical to sleep. The brain mechanisms of state alternations under urethane also closely overlapped those of natural sleep in their sensitivity to cholinergic pharmacological agents and dependence upon activity in the basal forebrain nuclei that are the major source of forebrain acetylcholine. Lastly, stimulation of brainstem regions thought to pace state alternations in sleep transiently disrupted state alternations under urethane. Conclusions/Significance: Our results suggest that urethane promotes a condition of behavioural unconsciousness tha