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Several studies have proposed constraints under which a low dimensional representation can be derived from large-scale real-world networks exhibiting complex nonlinear dynamics. Typically, these representations are formulated under certain assumptions, such as when solutions converge to attractor states using linear stability analysis or using projections of large-scale dynamical data into a set of lower dimensional modes that are selected heuristically. Here, we propose a generative framework for selection of lower dimensional modes onto which the entire network dynamics can be projected based on the symmetry of the input distribution for a large-scale network driven by external inputs, thus relaxing the heuristic selection of modes made in the earlier reduction approaches. The proposed mode reduction technique is tractable analytically and applied to different kinds of real-world large-scale network scenarios with nodes comprising of a) Van der Pol oscillators b) Hindmarsh-Rose neurons. These two demonstrations elucidate how order parameter is conserved at original and reduced descriptions thus validating our proposition.

The success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20-200 milliseconds, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training.

Astrocytes affect neural transmission by a tight control via glutamate transporters on glutamate concentrations in direct vicinity to the synaptic cleft and by extracellular glutamate. Their relevance for information representation has been supported by in-vivo studies in ferret and mouse primary visual cortex. In ferret blocking glutamate transport pharmacologically broadened tuning curves and enhanced the response at preferred orientation. In knock-out mice with reduced expression of glutamate transporters sharpened tuning was observed. It is however unclear how focal and ambient changes in glutamate concentration affect stimulus representation. Here we develop a computational framework, which allows the investigation of synaptic and extrasynaptic effects of glutamate uptake on orientation tuning in recurrently connected network models with pinwheel-domain (ferret) or salt-and-pepper (mouse) organization. This model proposed that glutamate uptake shapes information representation when it affects the contribution of excitatory and inhibitory neurons to the network activity. Namely, strengthening the contribution of excitatory neurons generally broadens tuning and elevates the response. In contrast, strengthening the contribution of inhibitory neurons can have a sharpening effect on tuning. In addition local representational topology also plays a role: In the pinwheel-domain model effects were strongest within domains - regions where neighboring neurons share preferred orientations. Around pinwheels but also within salt-and-pepper networks the effects were less strong. Our model proposes that the pharmacological intervention in ferret increases the contribution of excitatory cells, while the reduced expression in mouse increases the contribution of inhibitory cells to network activity.

There are numerous studies which suggest that perhaps music is truly the language of emotions. Music seems to have an almost willful, evasive quality, defying simple explanation, and indeed requires deeper neurophysiological investigations to gain a better understanding. The current study makes an attempt in that direction to explore the effect of context on music perception. To investigate the same, we measured Galvanic Skin Responses (GSR) and self-reported emotion on 18 participants while listening to different Ragas (musical stimulus) composed of different Rasa's (emotional expression) in the different context (Neutral, Pleasant, and Unpleasant). The IAPS pictures were used to induce the emotional context in participants. Our results from this study suggest that the context can modulate emotional response in music perception but only for a shorter time scale. Interestingly, here we demonstrate by combining GSR and self-reports that this effect gradually vanishes over time and shows emotional adaptation irrespective of context. The overall findings suggest that specific context effects of music perception are transitory in nature and gets saturated on a longer time scale.

Primary visual cortex (V1) provides crucial insights into the selectivity and emergence of specific output features such as orientation tuning. Tuning and selectivity of cortical neurons in mouse visual cortex is not equivocally resolved so far. While many in-vivo experimental studies found inhibitory neurons of all subtypes to be broadly tuned for orientation other studies report inhibitory neurons that are as sharply tuned as excitatory neurons. These diverging findings about the selectivity of excitatory and inhibitory cortical neurons prompted us to ask the following questions: (1) How different or similar is the cortical computation with that in previously described species that relies on map? (2) What is the network mechanism underlying the sharpening of orientation selectivity in the mouse primary visual cortex? Here, we investigate the above questions in a computational framework with a recurrent network composed of Hodgkin-Huxley (HH) point neurons. Our cortical network with random connectivity alone could not account for all the experimental observations, which led us to hypothesize, (a) Orientation dependent connectivity (b) Feedforward afferent specificity to understand orientation selectivity of V1 neurons in mouse. Using population (orientation selectivity index) OSI as a measure of neuronal selectivity to stimulus orientation we test each hypothesis separately and in combination against experimental data. Based on our analysis of orientation selectivity (OS) data we find a good fit of network parameters in a model based on afferent specificity and connectivity that scales with feature similarity. We conclude that this particular model class best supports data sets of orientation selectivity of excitatory and inhibitory neurons in layer 2/3 of primary visual cortex of mouse., Comment: 39 pages, 12 figures