Biology subjects

Demian Battaglia

Publications and source records attributed to Demian Battaglia.

3 recordsLinked to original sources

bioRxiv2015

Dynamic information routing in complex networks

Abstract Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this generic mechanism specifically for oscillatory dynamics and analyze how individual unit properties, the network topology and external inputs coact to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine non-local network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

Neuroscience↗

bioRxiv2015

Connectome of a model local cortical circuit flexibly shapes layer-dependent multi-frequency oscillations

The role played by interlayer connections in shaping local responses and their long-range coupling has not yet been fully elucidated. Here, we analyze a rate model of a canonic local circuit with realistic anatomy. We find that this circuit generates a rich repertoire of possible dynamical states, including an oscillatory regime in which gamma-and beta-oscillations dominate in superficial and deep layers, respectively, in agreement with experimental observations. This regime stems from non-linear inter-layer interactions, independently from intrinsic resonance properties of distinct layers. Moreover, by connecting two local circuits via cortico-cortical projections, the emergent phase differences define a flexible and frequency-dependent inter-areal hierarchy. Such dynamic patterns generally do not arise in randomized circuits, and the compatible connectomes are rare, although not unique. Altogether, these results suggest that inter-layer connectivity is homeostatically regulated to make local circuits fit to integrate and multiplex signals from several sources in multiple frequency bands.\n\nAuthor SummaryThe local circuit of mammalian cortex presents a characteristic multilayered structure, with feedforward (and feedback) cortico-cortical connections originating from (and targeting) distinct and well defined layers. Here, we model how such a structure fundamentally shapes the dynamical repertoire of local cortical oscillatory states and their long-range interaction. Experimental evidence, matched by our simulations, suggests that different cortical layers oscillate at different frequencies and that neuronal oscillations at different frequencies are exploited for communication in different directions. While this laminar specificity of oscillations is often explained in terms of multiple inhibitory populations with different resonance properties, we show here that it could alternatively emerge as a byproduct of the collective local circuit dynamics. Our modelling study indicates furthermore that the empirically observed multi-frequency oscillatory patterns cannot be reproduced in presence of an arbitrary interlayer connectivity. In this sense, therefore, we believe that the adopted connectome, derived from neuroanatomical reconstructions, is \"special\". Nevertheless, it is not unique, since other, very different connectomes may also lead to a matching dynamical repertoire. This suggests that a multiplicity of non-random canonical circuit templates may share largely overlapping functions, robustly achieved and maintained via functional homeostasis mechanisms.

Neuroscience↗

bioRxiv2015

Model-free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell-and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.

Neuroscience↗