Biology subjects

Jordi Garcia-Ojalvo

Publications and source records attributed to Jordi Garcia-Ojalvo.

3 recordsLinked to original sources

bioRxiv2015

Transition between functional regimes in an integrate-and-fire network model of the thalamus

The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 - 15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different information-processing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and gabaergic reticular (RE) neurons described by adaptative integrate-and-fire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TC-RE loops generate spindle-like oscillations and that a critical value of clustering in the RE-RE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarization-rebound-driven firing to depolarization-driven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.\n\nAuthor SummaryThe thalamus is known to exhibit two clearly distinct dynamical regimes with different functionalities. During slow-wave sleep the thalamus is dominated by internal activity and is hardly sensitive to external stimuli. In contrast, in the awake state, the thalamus modulates its activity according to the stimuli coming from the periphery. Here we study the conditions regulating the transition between these two states. To that end we implement a simple yet biologically realistic neuronal network model of the thalamus, based on single-neuron models that reproduce the properties of the two prominent types of thalamic neurons, namely thalamocortical relay cells and reticular neurons. We found that when reticular neurons are clustered the network exhibits two distinct dynamical regimes; one dominated by oscillations and insensitive to external stimuli (like sleep) and one sensitive to them (like wake). Moreover we found that the transition between the two regimes is due to the increase of the external excitatory input (corresponding to stronger sensory stimuli).

Neuroscience↗

bioRxiv2015

Diversity of fate outcomes in cell pairs under lateral inhibition

Cell fate determination by lateral inhibition via Notch/Delta signalling has been extensively studied. Most formalised models consider Notch/Delta interactions in fields of cells, with parameters that typically lead to symmetry breaking of signalling states between neighbouring cells, commonly resulting in salt-and-pepper fate patterns. Here we consider the case of signalling between isolated cell pairs, and find that the bifurcation properties of a standard mathematical model of lateral inhibition can lead to stable symmetric signalling states. We apply this model to the adult intestinal stem cell (ISC) of Drosophila, whose fate is stochastic but dependent on the Notch/Delta pathway. We observe a correlation between signalling state in cell pairs and their contact area. We interpret this behaviour in terms of the properties of our model in the presence of population variability in signalling thresholds. Our results suggest that the dynamics of Notch/Delta signalling can contribute to explain stochasticity in stem cell fate decisions, and that the standard model for lateral inhibition can account for a wider range of developmental outcomes than previously considered.\n\nSummary statementNotch/Delta-mediated lateral inhibition in cell pairs can result in symmetric signalling depending on the activation threshold, which can modulate cell-fate decisions depending on contact area.

Developmental Biology↗

bioRxiv2014

State-dependent information processing in gene regulatory networks

Cellular information processing is generally attributed to the complex networks of genes and proteins that regulate cell behavior. It is still unclear, however, what are the main features of those networks that allow a cell to encode and interpret its ever changing environment. Here we address this question by studying the computational capabilities of the transcriptional regulatory networks of five evolutionary distant organisms. We identify in all cases a cyclic recurrent structure, formed by a small core of genes, that is essential for dynamical encoding and information integration. The recent history of the cell is encoded by the transient dynamics of this recurrent reservoir of nodes, while the rest of the network forms a readout layer devoted to decode and interpret the high-dimensional dynamical state of the recurrent core. This separation of roles allows for the integration of temporal information, while facilitating the learning of new environmental conditions and preventing catastrophic interference between those new inputs and the previously stored information. This resembles the reservoir-computing paradigm recently proposed in computational neuroscience and machine learning. Our results reveal that gene regulatory networks act as echo-state networks that perform optimally in standard memory-demanding tasks, and confirms that most of their memory resides in the recurrent reservoir. We also show that the readout layer can learn to decode the information stored in the reservoir via standard evolutionary strategies. Our work thus suggests that recurrent dynamics is a key element for the processing of complex time-dependent information by cells.\n\nSummaryCells must monitor the dynamics of their environment continuously, in order to adapt to present conditions and anticipate future changes. But anticipation requires processing temporal information, which in turn requires memory. Here we propose that cells can perform such dynamical information processing via the reservoir computing paradigm. According to this concept, a structure with recurrent (cyclic) paths, known as the reservoir, stores in its dynamics a record of the cells recent history. A much simpler feedforward structure then reads and decodes that information. We show that the transcriptional gene regulatory networks of five evolutionary distant organisms are organized in this manner, allowing them to store complex time-dependent signals entering the cell in a biologically realistic manner.

Synthetic Biology↗