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)

MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity

When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms

Effect of localized input on bump solutions in a two-population neural-field model

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Neural firing rate model with a steep firing rate function

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Data from: Computing the local field potential (LFP) from integrate-and-fire network models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo

The coastal military architecture of World War II in Sardinia

After the Unification of Italy and after the First World War, the Stato Maggiore of the Royal Italian Army had to change the defensive strategies and give more attention to coastal defense, because of the changing political relations and the development of military technology. In this overall strategic framework, the isle of Sardinia was considered an "outpost of Italy", because its defensive and offensive importance in the Mediterranean Sea. During World War II coastal defense became the operational priority of the Italian Army. In Sardinia, that was crucial for its proximity to Corsica and Tunisia and as target of the Allies (af-ter 1943), were introduced substantial defense forces: army corps, brigades, mobile divisions, self-propelled, self-cannons. Therefore, the Army started the construction of strongholds of permanent fortifi-cation, particularly works made of reinforced concrete produced according to standardized modules but adapted to the context with the means, resources and techniques available. Along the Sardinian coast, hundreds bunkers were built and most of them are still existing in a state of ne-glect, sometimes in contexts of particular landscape and environmental quality. This paper presents the systematic study of these "modern ruins built in concrete", through the examination of documents found in historical military archives and through a direct investigation of some of significant works. Also it offers a cataloguing through the categories of different disciplines (history, architecture, engineering, "Art of War" and ballistics) to stimulate their valorisation and conservation, as architectural expression of a particular pe-riod of the history of the twentieth century

LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons

Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials

Simulated laminar recordings for excitatory synapses only in the upper dendritic bush

Simulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper when excitatory synapses located only in the upper dendritic bush, computed from LFPy. Each row is a different depth. Input intensity is 1.5 sp/ms

Simulated laminar recordings for input intensity 3.0 sp/ms

Simulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title