Neural population coding and auditory temporal pattern analysis

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.

Neural selectivity to signal duration within the auditory midbrain has been observed in several species and is thought to play a role in signal recognition. Here we examine the effects of signal duration on the coding of individual and concurrent vocal signals in a teleost fish with exceptionally long duration vocalizations, the plainfin midshipman, Porichthys notatus. Nesting males produce long-duration, multi-harmonic signals known as hums to attract females to their nests; overlapping hums produce acoustic beats at the difference frequency of their spectral components. Our data show that all midbrain neurons have sustained responses to long-duration hum-like tones and beats. Overall spike counts increase linearly with signal duration, although spike rates decrease dramatically. Neurons show varying degrees of spike rate decline and hence, differential changes in spike rate across the neuron population may code signal duration. Spike synchronization to beat difference frequency progressively increases throughout long-duration beats such that significant difference frequency coding is maintained in most neurons. The significance level of difference frequency synchronization coding increases by an order of magnitude when integrated over the entirety of long-duration signals. Thus, spike synchronization remains a reliable difference frequency code and improves with integration over longer time spans.

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.

A layered continual population model of primary visual cortex has been constructed, which reproduces a set of experimental data, including postsynaptic responses of single neurons on extracellular electric stimulation and spatially distributed activity patterns in response to visual stimulation. In the model, synaptically interacting excitatory and inhibitory neuronal populations are described by a conductance-based refractory density approach. Populations of two-compartment excitatory and inhibitory neurons in cortical layers 2/3 and 4 are distributed in the 2-d cortical space and connected by AMPA, NMDA and GABA type synapses. The external connections are pinwheel-like, according to the orientation of a stimulus. Intracortical connections are isotropic local and patchy between neurons with similar orientations. The model proposes better temporal resolution and more detailed elaboration than conventional mean-field models. In comparison to large network simulations, it excludes a posteriori statistical data manipulation and provides better computational efficiency and minimal parametrization.

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.

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.

Hemispheric asymmetry of calbindin-positive neurons is associated with successful song imitation

Neurons in the brain form thousands of connections, or synapses, with one another, allowing signals to pass from one cell to the next. To activate a neuron, a high enough activating signal or ‘action potential’ must be reached. However, the accepted view of signal transmission assumes that the great majority of synapses are too weak to activate neurons. This means that often simultaneous inputs from many neurons are required to trigger a single neuron’s activation. However, such coordination is likely unreliable as neurons can react differently to the same stimulus depending on the circumstances. An alternative way of transmitting signals has been reported in turtle brains, where impulses from a single neuron can trigger activity across a network of connections. Furthermore, these responses are reliably repeatable, activating the same neurons in the same order. Riquelme et al. set out to understand the mechanism that underlies this type of neuron activation using a mathematical model based on data from the turtle brain. These data showed that the neural network in the turtle’s brain had many weak synapses but also a few, rare, strong synapses. Simulating this neural network showed that those rare, strong synapses promote the signal’s reliability by providing a consistent route for the signal to travel through the network. The numerous weak synapses, on the other hand, have a regulatory role in providing flexibility to how the activation spreads. This combination of strong and weak connections produces a system that can reliably promote or stop the signal flow depending on the context. Riquelme et al.’s work describes a potential mechanism for how signals might travel reliably through neural networks in the brain, based on data from turtles. Experimental work will need to address whether strong connections play a similar role in other animal species, including humans. In the future, these results may be used as the basis to design new systems for artificial intelligence, building on the success of neural networks.

Grasshoppers, and among them especially the species Chorthippus biguttulus, have been used as a model system to study the neuronal basis of acoustic behavior. Auditory neurons have been described from intracellular recordings. The growing interest to study population activity of neurons has been satisfied so far with artificially combining data from different individuals. Here for the first time multielectrode recordings from the brain of a small grasshopper brain were made. Three 12 µm tungsten wires (combined in a multielectrode) to record from local brain neurons and from a population of auditory neurons entering the brain from the thorax. It was possible to separate up to five units by sorting algorithms. Tungsten wires exhibited stable recordings with higher signal-to-noise ratio than copper wires. Due to the tight temporal coupling of auditory activity to the stimulus spike collisions were frequent and collision analysis retrieved 10–15% of additional spikes. Physiological identification of units described from intracellular recordings was hard to achieve therefore the focus was on comparing individual units. Recording the population activity of auditory neurons in one individual prevents interindividual and trial-to-trial variability which otherwise reduce the validity of the analysis. Decoding the information about the acoustic stimulus was compared between single neurons and set of simultaneously recorded neurons. Information was higher for some data sets with 2 or more simultaneously recorded neurons indicating the existence of a population code inside the brain of grasshopper. Local brain neurons were recorded from lateral protocerebrum, anterior brain and central complex and were separated from ascending neurons based on their longer latencies. One local brain neuron was found discriminating between behaviorally attractive and non-attractive stimuli. Using such multielectrodes, it was also possible to induce singing responses by electrically stimulating different auditory neuropiles in the brain of grasshoppers.

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

Simply complex - the structure of the cerebellar neuronal lineage.

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.