Conductance-based refractory density model of primary visual cortex

Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a key enzyme in long-term plasticity in many neurons, including in the nociceptive circuitry of the spinal dorsal horn. However, although the role of CaMKII heterooligomers in neuronal plasticity is isoform-dependent, the distribution and co-localization of CaMKII isoforms in the dorsal horn have not been comprehensively investigated. Here, quantitative immunofluorescence analysis was used to examine the distribution of the two major neuronal CaMKII isoforms, α and β, in laminae I–III of the rat dorsal horn, with reference to inhibitory interneurons and neuronal populations defined by expression of parvalbumin, calretinin, and calbindin D28k. Unexpectedly, all or nearly all inhibitory and excitatory neurons showed both CaMKIIα and CaMKIIβ immunoreactivity, although at highly variable levels. Lamina III neurons showed less CaMKIIα immunoreactivity than laminae I–II neurons. Whereas CaMKIIα immunoreactivity was found at nearly similar levels in inhibitory and excitatory neurons, CaMKIIβ generally showed considerably lower immunoreactivity in inhibitory neurons. Distinct populations of inhibitory calretinin neurons and excitatory parvalbumin neurons exhibited high CaMKIIα-to-CaMKIIβ immunoreactivity ratios. CaMKIIα and CaMKIIβ immunoreactivity showed positive correlation at GluA2+ puncta in pepsin-treated tissue. These results suggest that, unlike the forebrain, the dorsal horn is characterized by similar expression of CaMKIIα in excitatory and inhibitory neurons, whereas CaMKIIβ is less expressed in inhibitory neurons. Moreover, CaMKII isoform expression varies considerably within and between neuronal populations defined by laminar location, calcium-binding protein expression, and transmitter phenotype, suggesting differences in CaMKII function both between and within neuronal populations in the superficial dorsal horn.

The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

Computational analyses of in-vivo cerebral activity show that gamma oscillations in the olfactory cortex originate from local feedback inhibition following each sniff and serve to segregate competing odor representations through a winner-take-all process, providing an optimal window to decode odor.

N-methyl-D-aspartate receptor (NMDAR) modulates the structural plasticity of dendritic spines by impacting cytoskeletal organization and kinase signaling. In the developing nervous system, activation of NMDAR is pertinent for neuronal migration, neurite differentiation, and cellular organization. Given that small conductance potassium channels (SK2/3) repress NMDAR ionotropic signaling, this study highlights the impact of neonatal SK channel potentiation on adult cortical and hippocampal organization. Neonatal SK channel potentiation was performed by one injection of SK2/3 agonist (CyPPA) into the pallium of mice on postnatal day 2 (P2). When the animals reached adulthood (P55), the hippocampus and cortex were examined to assess neuronal maturation, lamination, and the distribution of synaptic cytoskeletal proteins. Immunodetection of neuronal markers in the brain of P2-treated P55 mice revealed the presence of immature neurons in the upper cortical layers (layers II–IV) and CA1 (hippocampus). Also, layer-dependent cortical-cell density was attenuated due to the ectopic localization of mature (NeuN⁺) and immature (Doublecortin⁺ [DCX⁺]) neurons in cortical layers II–IV. Similarly, the decreased count of NeuN⁺ neurons in the CA1 is accompanied by an increase in the number of immature DCX⁺ neurons. Ectopic localization of neurons in the upper cortex and CA1 caused the dramatic expression of neuron-specific cytoskeletal proteins. In line with this, structural deformity of neuronal projections and the loss of postsynaptic densities suggests that postsynaptic integrity is compromised in the SK2/3⁺ brain. From these results, we deduced that SK channel activity in the developing brain likely impacts neuronal maturation through its effects on cytoskeletal formation.

Recent evidence suggests that synaptic refinement, the reorganization of synapses and connections without significant change in their number or strength, is important for the development of the visual system of juvenile rodents. Other evidence in rodents and humans shows that there is a marked drop in sleep slow-wave activity (SWA) during adolescence. Slow waves reflect synchronous transitions of neuronal populations between active and inactive states, and the amount of SWA is influenced by the connection strength and organization of cortical neurons. In this study, we investigated whether synaptic refinement could account for the observed developmental drop in SWA. To this end, we employed a large-scale neural model of primary visual cortex and sections of the thalamus, capable of producing realistic slow waves. In this model, we reorganized intralaminar connections according to experimental data on synaptic refinement: during prerefinement, local connections between neurons were homogenous, whereas in postrefinement, neurons connected preferentially to neurons with similar receptive fields and preferred orientations. Synaptic refinement led to a drop in SWA and to changes in slow-wave morphology, consistent with experimental data. To test whether learning can induce synaptic refinement, intralaminar connections were equipped with spike timing-dependent plasticity. Oriented stimuli were presented during a learning period, followed by homeostatic synaptic renormalization. This led to activity-dependent refinement accompanied again by a decline in SWA. Together, these modeling results show that synaptic refinement can account for developmental changes in SWA. Thus sleep SWA may be used to track noninvasively the reorganization of cortical connections during development.

Background: There is evidence that patients with anorexia nervosa (AN) demonstrate specific cerebral activation patterns in response to visual food stimulation. We postulated that cerebral activation patterns could represent different perceptions of high-calorie images during hunger and satiety and could be determined by patients’ subjective ratings. Methods: After 6 h of starvation and also in a state of satiety, 12 female patients with AN and 12 normal-weight women were assessed by use of fMRI with high-calorie food images. All patients suffered from a restrictive type of AN. Heart rates, subjective ratings of satiety and valences of the visual stimuli were assessed. Results: Food stimuli presented during a state of hunger were associated with significant activation of the anterior cingulate cortex and insula in the control group and of the prefrontal and central cortices and insula in the AN group. During the hunger state activation in AN of the dorsal posterior cingulate cortex was revealed compared to the controls. In the state of satiety, activation of the left insula was observed in the AN group. Use of the food valence judgment as a covariate confirmed the insula activation and revealed additional activation of the orbitofrontal, cingulate and medial temporal cortices. Conclusion: Our results indicate differences in cerebral activation patterns due to different perceptions of high-calorie food images, modulated by feelings of hunger or satiety, among AN patients with modulation by subjective ratings of food valence.

Orientation selectivity is ubiquitous in the primary visual cortex (V1) of mammals. In cats and monkeys, V1 displays spatially ordered maps of orientation preference. Instead, in mice, squirrels, and rats, orientation selective neurons in V1 are not spatially organized, giving rise to a seemingly random pattern usually referred to as a salt-and-pepper layout. The fact that such different organizations can sharpen orientation tuning leads to question the structural role of the intracortical connections; specifically the influence of plasticity and the generation of functional connectivity. In this work, we analyze the effect of plasticity processes on orientation selectivity for both scenarios. We study a computational model of layer 2/3 and a reduced one-dimensional model of orientation selective neurons, both in the balanced state. We analyze two plasticity mechanisms. The first one involves spike-timing dependent plasticity (STDP), while the second one considers the reconnection of the interactions according to the preferred orientations of the neurons. We find that under certain conditions STDP can indeed improve selectivity but it works in a somehow unexpected way, that is, effectively decreasing the modulated part of the intracortical connectivity as compared to the non-modulated part of it. For the reconnection mechanism we find that increasing functional connectivity leads, in fact, to a decrease in orientation selectivity if the network is in a stable balanced state. Both counterintuitive results are a consequence of the dynamics of the balanced state. We also find that selectivity can increase due to a reconnection process if the resulting connections give rise to an unstable balanced state. We compare these findings with recent experimental results.

The function of spike synchrony has long been debated in the neuroscience community. Some researchers view synchrony as a byproduct of brain activity, while others argue that it serves as a mechanism for binding perceptual features. We, however, argue for an alternative view on the computational role of synchrony: it can serve as a mechanism for estimating the prior probability of incoming stimuli. In V1, this probability can be estimated by comparing the input stimulus with previously acquired visual experience. Such experience is encoded in horizontal intracortical connections, as they are subject to change, learn, and adapt to incoming visual stimuli throughout the course of life. Thus, the V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on the synchrony of neuronal firings in V1. In our work, we used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found significant differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation.Author SummaryNeurons in the brain fire synchronously in different contexts, in response to different stimuli. However, the function of synchrony is very debatable in the neuroscience community. We suggest that synchrony plays an important role in brain computations: it can estimate how well a network is familiar with the incoming stimulus. We tested our hypothesis on the simulation of neuronal activity in a model of primary visual cortex - a brain region which is processing simple visual features, such as lines of different angle orientation. The connections between neurons in this brain area store the acquired visual experience in the form of its statistics, such as edge cooccurrences. We built the connectivity in our model in accordance with principles of connectivity in primary visual cortex, assuming that it reflects natural images statistics. We tested our model on artificial stimuli, which represented more and less experienced visual features. Our results showed that spike synchrony is a global phenomenon which emerges across the large parts of the network, and it reflects the familiarity of the network with the stimulus. Thus, it can serve as a form of recognition memory and has a potential for usage in various applications which require estimation of input familiarity.

Genetically identified neurons in avian auditory pallium mirror core principles of their mammalian counterparts

Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.

Simply complex - the structure of the cerebellar neuronal lineage.

Neural population coding and auditory temporal pattern analysis

Requirement of keratan sulfate proteoglycan phosphacan with a specific sulfation pattern for critical period plasticity in the visual cortex

In the last decades great advances have been made in mapping neural circuitry of the brain. This has been facilitated by novel experimental techniques for studies both at the single-cell and systems levels. It still remains, though, to combine all the pieces in the puzzle to a coherent picture of brain function. While single nerve cells (neurons) are fairly well understood, the signal - processing properties of the nerve-cell networks in cortex are still obscure. The growth of experimental data has led to a revival of so - called rate equation models for cell networks in nervous tissue (neural networks). In these models, the probability for firing action potentials, the key information carriers in the brain, is the main dynamical variable. These models assume the form of coupled integral and integro - differential equations, and they describe non-linearand nonlocal interactions between the population of excitatory and inhibitory neurons. In the present talk I will discuss the properties of the continuum limit of a 2 population nonlocal Hopfield type of neuronal network model, with spatial periodic microstructure incorporated in the connectivity strength. The modelling framework is derived by means of a homogenization procedure, where the spatial nonlocalities are dealt with by means of Visintins theorem for 2-scale convergence of convolution integrals. I will discuss existence and stability of stationary localized solutions (bumps) and pattern formation though Turing type of instabilities within the framework of this model.

In this work, I overview some novel results concerning the theoretical calculation of a non‐equilibrium potential function for a biologically motivated model of a neural network. Such model displays competition between different populations of excitatory and inhibitory neurons, which is known to originate synchronous dynamics, fast activity oscillations, and other nontrivial behavior in more sophisticated models of neural media.