Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub.

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models.

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require ad hoc programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework. Thanks to its inherent characteristics, the HHNB facilitates the data-driven model building workflow and its reproducibility, hence fostering a collaborative approach to brain modeling.

Age-related preference for geometric spatial cues during real-world navigation.

Ageing effects on spatial navigation are characterized mainly in terms of impaired allocentric strategies. However, an alternative hypothesis is that navigation difficulties in aged people are associated with deficits in processing and encoding spatial cues. We tested this hypothesis by studying how geometry and landmark cues control navigation in young and older adults in a real, ecological environment. Recordings of body and gaze dynamics revealed a preference for geometry-based navigation in older adults, and for landmark-based navigation in younger ones. While cue processing was associated with specific fixation patterns, advanced age manifested itself in a longer reorientation time, reflecting an unbalanced exploration-exploitation trade-off in scanning policies. Moreover, a battery of tests revealed a specific cognitive deficit in older adults with geometric preference. These results suggest that allocentric strategy deficits in ageing can result from difficulties related to landmark coding, and predict recovery of allocentric strategies in geometrically polarized environments.

Modulation of network activity and induction of homeostatic synaptic plasticity by enzymatic removal of heparan sulfates.

Heparan sulfates (HSs) are complex and highly active molecules that are required for synaptogenesis and long-term potentiation. A deficit in HSs leads to autistic phenotype in mice. Here, we investigated the long-term effect of heparinase I, which digests highly sulfated HSs, on the spontaneous bioelectrical activity of neuronal networks in developing primary hippocampal cultures. We found that chronic heparinase treatment led to a significant reduction of the mean firing rate of neurons, particularly during the period of maximal neuronal activity. Furthermore, firing pattern in heparinase-treated cultures often appeared as epileptiform bursts, with long periods of inactivity between them. These changes in network activity were accompanied by an increase in the frequency and amplitude of miniature postsynaptic excitatory currents, which could be described by a linear up-scaling of current amplitudes. Biochemically, we observed an upregulation in the expression of the glutamate receptor subunit GluA1, but not GluA2, and a strong increase in autophosphorylation of α and ß Ca(2+)/calmodulin-dependent protein kinase II (CaMKII), without changes in the levels of kinase expression. These data suggest that a deficit in HSs triggers homeostatic synaptic plasticity and drastically affects functional maturation of neural network.

Encoding/decoding of first and second order tactile afferents in a neurorobotic application.

We present a neurorobotic framework to investigate tactile information processing at the early stages of the somatosensory pathway. We focus on spatiotemporal coding of first and second order responses to Braille stimulation, which offers a suitable protocol to investigate the neural bases of fine touch discrimination. First, we model Slow Adaptive type I fingertip mechanoreceptor responses to Braille characters sensed both statically and dynamically. We employ a network of spiking neurones to transduce analogue skin deformations into primary spike trains. Then, we model second order neurones in the cuneate nucleus (CN) of the brainstem to study how mechanoreceptor responses are possibly processed prior to their transmission to downstream central areas. In the model, the connectivity layout of mechanoreceptor-to-cuneate projections produces a sparse CN code. To characterise the reliability of neurotransmission we employ an information theoretical measure accounting for the metrical properties of spiking signals. Our results show that perfect discrimination of primary and secondary responses to a set of 26 Braille characters is achieved within 100 and 500 ms of stimulus onset, in static and dynamic conditions, respectively. Furthermore, clusters of responses to different stimuli are better separable after the CN processing. This finding holds for both statically and dynamically delivered stimuli. In the presented system, when sliding the artificial fingertip over a Braille line, a speed of 40-50mm/s is optimal in terms of rapid and reliable character discrimination. This result is coherent with psychophysical observations reporting average reading speeds of 30-40±5 mm/s adopted by expert Braille readers.

Investigating neuronal activity by SPYCODE multi-channel data analyzer.

Multi-channel acquisition from neuronal networks, either in vivo or in vitro, is becoming a standard in modern neuroscience in order to infer how cell assemblies communicate. In spite of the large diffusion of micro-electrode-array-based systems, researchers usually find it difficult to manage the huge quantity of data routinely recorded during the experimental sessions. In fact, many of the available open-source toolboxes still lack two fundamental requirements for treating multi-channel recordings: (i) a rich repertoire of algorithms for extracting information both at a single channel and at the whole network level; (ii) the capability of autonomously repeating the same set of computational operations to 'multiple' recording streams (also from different experiments) and without a manual intervention. The software package we are proposing, named SPYCODE, was mainly developed to respond to the above constraints and generally to offer the scientific community a 'smart' tool for multi-channel data processing.