Mesoscopic dynamics of pitch processing in human auditory cortex.

Pitch is a perceptual correlate of sound periodicity elicited by vibrating bodies; it plays a crucial role in music and speech. Although perceptual phenomenology of pitch has been studied for centuries, a detailed understanding of its underlying neural mechanisms is still lacking. Early theories suggesting that pitch is decoded in the peripheral auditory system fail to explain the perception of complex stimuli. More recent mechanistic models, focused on how subcortical structures process periodic discharges of the auditory nerve activity, are unable to explain fully key aspects of the processing dynamics observed during electrophysiological recordings. In this thesis, we propose a novel theory describing how subcortical representations of pitch-related information are integrated in cortex and how this integratory process gives rise to the dynamics observed in magnetoencephalographic (MEG) experimental recordings. Auditory evoked fields recorded with MEG reveal a systematic deflection around 100 ms after stimulus’ onset known as the N100m. This deflection consists of several components reflecting the onset of different perceptual dimensions of auditory stimuli such as pitch, timbre and loudness. The exact latency of the component elicited by pitch onset, known as the pitch onset response (POR), shows a strong linear relationship with the pitch of the stimulus. Our theory links the POR latency with processing time and explains, in a quantitative manner, the substrate of the relationship between processing time and pitch. Cortical integration is described using a model of neural ensembles located in two adjacent areas, putatively located along the lateral portion of Heschl’s Gyrus in human auditory cortex. Cortical areas are hierarchically structured and communicate with each other in a top-down fashion. Pitch processing is modelled as a multi-attractor system whose dynamics are driven by subcortical input. After tone onset, the system evolves from an initial equilibrium position to a new equilibrium state that represents the pitch elicited by the tone. A computational implementation of the model shows that: 1) the transient dynamics between equilibrium points explains the POR; 2) the latency of the transient is directly linked with the time required to reach the new equilibrium state; and 3) that such processing time depends linearly on the pitch of the stimuli. Our theory also addresses the problem of how tones with several simultaneous pitch values are processed in cortex. In Western music, dyads comprising tones with different pitch values are judged as more consonant or more dissonant depending on the ratio of the periods of the involved sounds. The latency of the POR evoked by such dyads also presents a strong correlation with the perceived consonance: dissonant dyads generate later PORs than consonant dyads. Our theory of pitch processing describes consonance (dissonance) as a direct effect of harmonic collaboration (competition) during the cortical integration process: the cortical model shows that harmonic collaboration facilitates convergence, explaining why dissonant dyads require longer processing times and evoke later PORs than consonant dyads

Basis Decomposition Discriminant ICA

In this Master's Thesis, we introduce the methodology Basis-Decomposition Discriminant ICA (BD-DICA), capable of finding the most discriminant Independent Components to characterise a high-dimensional dataset. The algorithm provides for this characterisation for several components with the same structure as the inputs. An adaptation of the algorithm for Feature Extraction is derived in the conclusions of this report. BD-DICA is constructed as a combination of the Basis-Decomposition ICA (BD-ICA), an architecture for ICA used in fMRI data analysis, and the Basis-Decomposition Fisher's Linear Discriminant (BDFLD), a modified version of the classical FLD introduced in this work. BDDICA is originally designed to deal with fMRI Data analysis, in which often we have data of about 10-5- 10-6- dimensions and a much smaller number of instances. BD-DICA finds interesting projections in the data whose output show a high discriminant power while maximising independence among the obtained projectors. Additional strategies based in a high restriction over the search subspace reduce highly the chances of overfitting. Experiments with synthetic data show that the method is robust to noise and that it is capable of successfully finding the discriminant generators of the data. Experiments performed with real fMRI data show that the method offers good results with Resting-State fMRI data. Unfortunately, no conclusive results were obtained for Task-Based fMRI data. A Gradient-Ascend approach to BD-DICA is exposed in detail along the report, including all needed derivatives. In addition, the implementation we used for the experimentation is publicly available running under MATLAB in www.github/qtabs/bddica. Compatibility with Octave is possible with a few adaptations regarding external libraries used by the algorithm

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Insights on the Neuromagnetic Representation of Temporal Asymmetry in Human Auditory Cortex.

Communication sounds are typically asymmetric in time and human listeners are highly sensitive to this short-term temporal asymmetry. Nevertheless, causal neurophysiological correlates of auditory perceptual asymmetry remain largely elusive to our current analyses and models. Auditory modelling and animal electrophysiological recordings suggest that perceptual asymmetry results from the presence of multiple time scales of temporal integration, central to the auditory periphery. To test this hypothesis we recorded auditory evoked fields (AEF) elicited by asymmetric sounds in humans. We found a strong correlation between perceived tonal salience of ramped and damped sinusoids and the AEFs, as quantified by the amplitude of the N100m dynamics. The N100m amplitude increased with stimulus half-life time, showing a maximum difference between the ramped and damped stimulus for a modulation half-life time of 4 ms which is greatly reduced at 0.5 ms and 32 ms. This behaviour of the N100m closely parallels psychophysical data in a manner that: i) longer half-life times are associated with a stronger tonal percept, and ii) perceptual differences between damped and ramped are maximal at 4 ms half-life time. Interestingly, differences in evoked fields were significantly stronger in the right hemisphere, indicating some degree of hemispheric specialisation. Furthermore, the N100m magnitude was successfully explained by a pitch perception model using multiple scales of temporal integration of auditory nerve activity patterns. This striking correlation between AEFs, perception, and model predictions suggests that the physiological mechanisms involved in the processing of pitch evoked by temporal asymmetric sounds are reflected in the N100m

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong