Sparsely Distributed, Pre-synaptic Kv3 K+ Channels Control Spontaneous Firing and Cross-Unit Synchrony via the Regulation of Synaptic Noise in an Auditory Brainstem Circuit.

Spontaneous subthreshold activity in the central nervous system is fundamental to information processing and transmission, as it amplifies and optimizes sub-threshold signals, thereby improving action potential initiation and maintaining reliable firing. This form of spontaneous activity, which is frequently considered noise, is particularly important at auditory synapses where acoustic information is encoded by rapid and temporally precise firing rates. In contrast, when present in excess, this form of noise becomes detrimental to acoustic information as it contributes to the generation and maintenance of auditory disorders such as tinnitus. The most prominent contribution to subthreshold noise is spontaneous synaptic transmission (synaptic noise). Although numerous studies have examined the role of synaptic noise on single cell excitability, little is known about its pre-synaptic modulation owing in part to the difficulties of combining noise modulation with monitoring synaptic release. Here we study synaptic noise in the auditory brainstem dorsal cochlear nucleus (DCN) of mice and show that pharmacological potentiation of Kv3 K+ currents reduces the level of synaptic bombardment onto DCN principal fusiform cells. Using a transgenic mouse line (SyG37) expressing SyGCaMP2-mCherry, a calcium sensor that targets pre-synaptic terminals, we show that positive Kv3 K+ current modulation decreases calcium influx in a fifth of pre-synaptic boutons. Furthermore, while maintaining rapid and precise spike timing, positive Kv3 K+ current modulation increases the synchronization of local circuit neurons by reducing spontaneous activity. In conclusion, our study identifies a unique pre-synaptic mechanism which reduces synaptic noise at auditory synapses and contributes to the coherent activation of neurons in a local auditory brainstem circuit. This form of modulation highlights a new therapeutic target, namely the pre-synaptic bouton, for ameliorating the effects of hearing disorders which are dependent on aberrant spontaneous activity within the central auditory system.

Kv3 K+ currents contribute to spike-timing in dorsal cochlear nucleus principal cells.

Exposure to loud sound increases burst-firing of dorsal cochlear nucleus (DCN) fusiform cells in the auditory brainstem, which has been suggested to be an electrophysiological correlate of tinnitus. The altered activity of DCN fusiform cells may be due to down-regulation of high voltage-activated (Kv3-like) K+ currents. Whole cell current-clamp recordings were obtained from DCN fusiform cells in brain slices from P15-P18 CBA mice. We first studied whether acoustic over-exposure (performed at P15) or pharmacological inhibition of K+ currents with tetraethylamonium (TEA) affect fusiform cell action potential characteristics, firing frequency and spike-timing relative to evoking current stimuli. We then tested whether AUT1, a modulator of Kv3 K+ currents reverses the effects of sound exposure or TEA. Both loud sound exposure and TEA decreased the amplitude of action potential after-hyperpolarization, reduced the maximum firing frequency, and disrupted spike-timing. These treatments also increased post-synaptic voltage fluctuations at baseline. AUT1 applied in the presence of TEA or following acoustic over-exposure, did not affect the firing frequency, but enhanced action potential after-hyperpolarization, prevented the increased voltage fluctuations and restored spike-timing. Furthermore AUT1 prevented the occurrence of bursts. Our study shows that the effect on spike-timing is significantly correlated with the amplitude of the action potential after-hyperpolarization and the voltage fluctuations at baseline. In conclusion, modulation of putative Kv3 K+ currents may restore regular spike-timing of DCN fusiform cell firing following noise exposure, and could provide a means to restore deficits in temporal encoding observed during noise-induced tinnitus.

Temporal features of spike trains in the moth antennal lobe revealed by a comparative time-frequency analysis.

The discrimination of complex sensory stimuli in a noisy environment is an immense computational task. Sensory systems often encode stimulus features in a spatiotemporal fashion through the complex firing patterns of individual neurons. To identify these temporal features, we have developed an analysis that allows the comparison of statistically significant features of spike trains localized over multiple scales of time-frequency resolution. Our approach provides an original way to utilize the discrete wavelet transform to process instantaneous rate functions derived from spike trains, and select relevant wavelet coefficients through statistical analysis. Our method uncovered localized features within olfactory projection neuron (PN) responses in the moth antennal lobe coding for the presence of an odor mixture and the concentration of single component odorants, but not for compound identities. We found that odor mixtures evoked earlier responses in biphasic response type PNs compared to single components, which led to differences in the instantaneous firing rate functions with their signal power spread across multiple frequency bands (ranging from 0 to 45.71 Hz) during a time window immediately preceding behavioral response latencies observed in insects. Odor concentrations were coded in excited response type PNs both in low frequency band differences (2.86 to 5.71 Hz) during the stimulus and in the odor trace after stimulus offset in low (0 to 2.86 Hz) and high (22.86 to 45.71 Hz) frequency bands. These high frequency differences in both types of PNs could have particular relevance for recruiting cellular activity in higher brain centers such as mushroom body Kenyon cells. In contrast, neurons in the specialized pheromone-responsive area of the moth antennal lobe exhibited few stimulus-dependent differences in temporal response features. These results provide interesting insights on early insect olfactory processing and introduce a novel comparative approach for spike train analysis applicable to a variety of neuronal data sets.

Computational deconvolution of genome wide expression data from Parkinson's and Huntington's disease brain tissues using population-specific expression analysis.

The characterization of molecular changes in diseased tissues gives insight into pathophysiological mechanisms and is important for therapeutic development. Genome-wide gene expression analysis has proven valuable for identifying biological processes in neurodegenerative diseases using post mortem human brain tissue and numerous datasets are publically available. However, many studies utilize heterogeneous tissue samples consisting of multiple cell types, all of which contribute to global gene expression values, confounding biological interpretation of the data. In particular, changes in numbers of neuronal and glial cells occurring in neurodegeneration confound transcriptomic analyses, particularly in human brain tissues where sample availability and controls are limited. To identify cell specific gene expression changes in neurodegenerative disease, we have applied our recently published computational deconvolution method, population specific expression analysis (PSEA). PSEA estimates cell-type-specific expression values using reference expression measures, which in the case of brain tissue comprises mRNAs with cell-type-specific expression in neurons, astrocytes, oligodendrocytes and microglia. As an exercise in PSEA implementation and hypothesis development regarding neurodegenerative diseases, we applied PSEA to Parkinson's and Huntington's disease (PD, HD) datasets. Genes identified as differentially expressed in substantia nigra pars compacta neurons by PSEA were validated using external laser capture microdissection data. Network analysis and Annotation Clustering (DAVID) identified molecular processes implicated by differential gene expression in specific cell types. The results of these analyses provided new insights into the implementation of PSEA in brain tissues and additional refinement of molecular signatures in human HD and PD.

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex.

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.

Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks.

Neural responses to odor blends often exhibit non-linear interactions to blend components. The first olfactory processing center in insects, the antennal lobe (AL), exhibits a complex network connectivity. We attempt to determine if non-linear blend interactions can arise purely as a function of the AL network connectivity itself, without necessitating additional factors such as competitive ligand binding at the periphery or intrinsic cellular properties. To assess this, we compared blend interactions among responses from single neurons recorded intracellularly in the AL of the moth Manduca sexta with those generated using a population-based computational model constructed from the morphologically based connectivity pattern of projection neurons (PNs) and local interneurons (LNs) with randomized connection probabilities from which we excluded detailed intrinsic neuronal properties. The model accurately predicted most of the proportions of blend interaction types observed in the physiological data. Our simulations also indicate that input from LNs is important in establishing both the type of blend interaction and the nature of the neuronal response (excitation or inhibition) exhibited by AL neurons. For LNs, the only input that significantly impacted the blend interaction type was received from other LNs, while for PNs the input from olfactory sensory neurons and other PNs contributed agonistically with the LN input to shape the AL output. Our results demonstrate that non-linear blend interactions can be a natural consequence of AL connectivity, and highlight the importance of lateral inhibition as a key feature of blend coding to be addressed in future experimental and computational studies.

Stimulus and network dynamics collide in a ratiometric model of the antennal lobe macroglomerular complex.

Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.

Improvement in hippocampal kindling analysis through computational processing data.

The kindling phenomenon is classically investigated in epileptology research. The present study aims to provide further information about hippocampal kindling through computational processing data. Adult Wistar rats were implanted with dorsal hippocampal and frontal neocortical electrodes to perform the experiment. The processing data was obtained using the Spike2 and Matlab softwares. An inverse relationship between the number of 'wet dog shakes' and the Racine's motor stages development was found. Moreover it was observed a significant increase in the afterdischarge (AD) duration and its frequency content. The highest frequencies were, however, only reached at the beginning of behavioral seizures. During the primary AD, fast transients (ripples) were registered in both hippocampi superimposed to slower waves. This experiment highlights the usefulness of computational processing applied to animal models of temporal lobe epilepsy and supports a relevant role of the high frequency discharges in temporal epileptogenesis.

Improvement in hippocampal kindling analysis through computational processing data / Aprimorando a análise do modelo de kindling hipocampal com o auxílio de processamento computacional

The kindling phenomenon is classically investigated in epileptology research. The present study aims to provide further information about hippocampal kindling through computational processing data. Adult Wistar rats were implanted with dorsal hippocampal and frontal neocortical electrodes to perform the experiment. The processing data was obtained using the Spike2 and Matlab softwares. An inverse relationship between the number of "wet dog shakes" and the Racine's motor stages development was found. Moreover it was observed a significant increase in the afterdischarge (AD) duration and its frequency content. The highest frequencies were, however, only reached at the beginning of behavioral seizures. During the primary AD, fast transients (ripples) were registered in both hippocampi superimposed to slower waves. This experiment highlights the usefulness of computational processing applied to animal models of temporal lobe epilepsy and supports a relevant role of the high frequency discharges in temporal epileptogenesis.

O fenômeno de kindling é classicamente utilizado no campo da epileptologia experimental. Este trabalho objetiva aprofundar a análise do modelo kindling hipocampal através de processamento computacional. Ratos wistar adultos receberam eletrodos hipocampais dorsais e neocorticais frontais para a realização do experimento. O processamento dos dados encontrados foi realizado pelos softwares Matlab e Spike2. Encontrou-se uma relação inversa entre wet dog shakes e o desenvolvimento dos estágios motores de Racine. A duração e o conteúdo de freqüência das pós-descargas hipocampais aumentaram durante o processo, sendo observadas descargas de alta freqüência (ripples) em ambos os hipocampos durante as pós-descargas primárias, superimpostas a ondas lentas. As mais altas freqüências, entretanto, foram apenas atingidas com o início das crises epilépticas. A utilização de sistemas computacionais para a confecção e análise do modelo de epilepsia temporal é ressaltada e reforça-se a relevância do papel das altas freqüências na epileptogênese temporal.

The electric fish Brachyhypopomus pinnicaudatus produces jamming avoidance responses to signals that are harmonically related to its own discharges.

Jamming avoidance responses (JARs) are exhibited by pairs of pulse type electric fish that discharge with similar frequencies whenever their individual pulses are about to coincide: responses consist of the transient shortenings in inter-discharge intervals in the fish with the higher frequency. This study describes and models novel forms of JARs observed in sexually mature male or female Brachyhypopomus pinnicaudatus. One novel JAR was observed in male-female pairs in their natural habitat. It happened when the baseline frequencies were not similar but, rather, when one was almost twice that of the other; moreover, the transient interval shortenings occurred not in the fish with the higher frequency but in the slower one. Transient interval shortenings similar to those in all natural JARs were observed in individual fish in tanks and submitted to periodic electrical pulse trains. They happened not only when pulse frequencies were slightly lower than the unperturbed frequency emitted by the fish but also when slightly lower than the frequency's sub- or higher harmonics (e.g. one half or twice). The proposed model satisfactorily reproduces all experimental observations. In it, forthcoming inter-pulse intervals reflect the differences between the cophases of pulses that arrive within the 'sensitive windows' belonging to either consecutive (i.e. one and the next) or alternating (e.g. every other, every three) intervals. Paired pulse fish embody interacting oscillators, and, in particular, JARs embody either quasiperiodic phase walk-throughs and intermittencies or periodic and locked forms. Hence, their study would profit by the powerful theories and approaches advanced by nonlinear dynamics.

Noise autocorrelation and jamming avoidance performance in pulse type electric fish.

The amplitude and the autocorrelation level of the noise affecting the interval between successive electric organ discharges were estimated in isolated fish and in socially interacting fish of the species Gymnotus carapo. Both quantities increased in the fish with the slower discharging rate of the pair during the interaction, and we aim to assess whether they have some functional implication for the efficiency of the jamming avoidance response performed by the fish having the faster discharging rate of the pair. For this purpose, the noisy variability of the intervals around its mean value was simulated using autoregressive models estimated from experimental recordings of isolated and interacting fish. The simulation was implemented using two autoregressive models, each representing one fish of the pair. The jamming avoidance response was included by adding transient interval shortenings to the train simulating the fish of the pair that discharges at a faster rate whenever the two trains were close to discharge simultaneously. The number of double coincidences (i.e., simultaneous discharges occurring in two successive firing cycles) of the two simulated trains was used to measure the efficiency of the jamming avoidance. This quantity was evaluated separately as a function of the autocorrelation level and amplitude of the simulated variability, in realizations with and without jamming avoidance response. Only if jamming avoidance response was included in the simulation have we found that (i) the number of coincidences decreased with the increasing of the autocorrelation and (ii) the increase in the amplitude determined a growth of the coincidence number at a rate that is inversely proportional to the autocorrelation level. We argue that the persistent correlations of the fish variability constitute an adaptation that improves the efficiency of transient interval shortenings as a jamming avoidance strategy. The long autocorrelation time prevents the disruption of the jamming avoidance performance due to increases in the variability amplitude.