Alberto Capurro

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 Parkinsons and Huntingtons 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.

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.

Design and Implementation of a Modular Biomimetic Infochemical Communication System

We describe here the design and implementation of a novel biomimetic infochemical communication system that employs airborne molecules alone to communicate over space and time. The system involves the design and fabrication of a microsystem capable of producing and releasing a precise mix of biosynthetic compounds and a sensor system capable of detecting and decoding the ratiometrically encoded chemical information. The research inspired by biology has been based upon the biosynthetic pathways of infochemical production and information processing within the insect world. In this novel approach, the functional equivalents of the nanoscale biological machinery are implemented by combining the latest advances and convergence of expertise in the fields of biochemistry, molecular biology, neuroscience, micro- and nanofabrication, materials science, and smart sensor and microcircuit design. The biomimetic system comprises a micromachined bio-reactor mimicking the sex gland of the female insect that releases a blend of pheromones in precisely controlled ratios, together with a cell-based biosensor system, mimicking the antennae of the male insect. The signals from the biosensors are classified and ratios decoded using a field-programmable gate array implementation of a neuromorphic model of the antenna lobe of the insect. We believe that this novel, smart infochemical communication system, inspired by the insects behavior, could eventually be implemented in VLSI technology at low cost and low power with possible application in the fields of automatic identification and data capturing, product labeling, search and rescue, environmental monitoring, and pest control

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.

Ratiometric Chemical Blend Processing with a Neuromorphic Model of the Insect Macroglomerular Complex

We present a dynamical spiking neuromorphic model constrained by the known biology of the insect antennal lobe (AL) macroglomerular complex (MGC) implemented in a field programmable gate array (FPGA). When driven by polymer coated quartz‐crystal microbalance (QCM) chemosensors at its input, the dynamical trajectories of the model’s projection neuron (PN) output population activity encode the concentration ratios of binary odour mixtures. We demonstrate that it is possible to recover blend ratio information from the early transient phase of QCM responses that would otherwise be difficult to separate directly from chemosensor data using classical approaches. Our results demonstrate the potential of insect‐based neuromorphic signal processing methods for the rapid and efficient classification of ratiometrically encoded chemical blends.

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.

Design and implementation of a modular biomimetic infochemical communication system

We describe here the design and implementation of a novel biomimetic infochemical communication system that employs airborne molecules alone to communicate over space and time. The system involves the design and fabrication of a microsystem capable of producing and releasing a precise mix of biosynthetic compounds and a sensor system capable of detecting and decoding the ratiometrically encoded chemical information. The research inspired by biology has been based upon the biosynthetic pathways of infochemical production and information processing within the insect world. In this novel approach, the functional equivalents of the nanoscale biological machinery are implemented by combining the latest advances and convergence of expertise in the fields of biochemistry, molecular biology, neuroscience, micro- and nanofabrication, materials science, and smart sensor and microcircuit design. The biomimetic system comprises a micromachined bio-reactor mimicking the sex gland of the female insect that releases a blend of pheromones in precisely controlled ratios, together with a cell-based biosensor system, mimicking the antennae of the male insect. The signals from the biosensors are classified and ratios decoded using a field-programmable gate array implementation of a neuromorphic model of the antenna lobe of the insect. We believe that this novel, smart infochemical communication system, inspired by the insects behavior, could eventually be implemented in VLSI technology at low cost and low power with possible application in the fields of automatic identification and data capturing, product labeling, search and rescue, environmental monitoring, and pest control

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

ABSTRACT 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. HighlightsWhole cell recordings were performed in dorsal cochlear nucleus fusiform cells.Spike‐timing is dependent on the action potential after‐hyperpolarization.Spike‐timing is dependent on synaptic baseline voltage fluctuations.Inhibition of K+ currents using TEA or acoustic over‐exposure disrupt spike‐timing.AUT1, a Kv3.1/3.2 K+ current modulator, counteracts the disruptive effects on spike‐timing.

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

*Correspondence: Timothy C. Pearce, Department of Engineering, University Road, University of Leicester, Leicester LE1 7RH, UK e-mail: t.c.pearce@le.ac.uk †Present address: Alberto Capurro, Cell Physiology and Pharmacology, University of Leicester, Leicester, UK 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.