Attractors and noise: Twin drivers of decisions and multistability

Abstract Perceptual decisions are made not only during goal-directed behavior such as choice tasks, but also occur spontaneously while multistable stimuli are being viewed. In both contexts, the formation of a perceptual decision is best captured by noisy attractor dynamics. Noise-driven attractor transitions can accommodate a wide range of timescales and a hierarchical arrangement with "nested attractors" harbors even more dynamical possibilities. The attractor framework seems particularly promising for understanding higher-level mental states that combine heterogeneous information from a distributed set of brain areas

Dissociated multi-unit activity and local field potentials: a theory inspired analysis of a motor decision task.

Local field potentials (LFP) and multi-unit activity (MUA) recorded in vivo are known to convey different information about the underlying neural activity. Here we extend and support the idea that single-electrode LFP-MUA task-related modulations can shed light on the involved large-scale, multi-modular neural dynamics. We first illustrate a theoretical scheme and associated simulation evidence, proposing that in a multi-modular neural architecture local and distributed dynamic properties can be extracted from the local spiking activity of one pool of neurons in the network. From this new perspective, the spectral features of the field potentials reflect the time structure of the ongoing fluctuations of the probed local neuronal pool on a wide frequency range. We then report results obtained recording from the dorsal premotor (PMd) cortex of monkeys performing a countermanding task, in which a reaching movement is performed, unless a visual stop signal is presented. We find that the LFP and MUA spectral components on a wide frequency band (3-2000 Hz) are very differently modulated in time for successful reaching, successful and wrong stop trials, suggesting an interplay of local and distributed components of the underlying neural activity in different periods of the trials and for different behavioural outcomes. Besides, the MUA spectral power is shown to possess a time-dependent structure, which we suggest could help in understanding the successive involvement of different local neuronal populations. Finally, we compare signals recorded from PMd and dorso-lateral prefrontal (PFCd) cortex in the same experiment, and speculate that the comparative time-dependent spectral analysis of LFP and MUA can help reveal patterns of functional connectivity in the brain

Population dynamics of interacting spiking neurons

A dynamical equation is derived for the spike emission rate nu(t) of a homogeneous network of integrate-and-fire (IF) neurons in a mean-field theoretical framework, where the activity of the single cell depends both on the mean afferent current (the "field") and on its fluctuations. Finite-size effects are taken into account, by a stochastic extension of the dynamical equation for the nu; their effect on the collective activity is studied in detail. Conditions for the local stability of the collective activity are shown to be naturally and simply expressed in terms of (the slope of) the single neuron, static, current-to-rate transfer function. In the framework of the local analysis, we studied the spectral properties of the time-dependent collective activity of the finite network in an asynchronous state; finite-size fluctuations act as an ongoing self-stimulation, which probes the spectral structure of the system on a wide frequency range. The power spectrum of nu exhibits modes ranging from very high frequency (depending on spike transmission delays), which are responsible for instability, to oscillations at a few Hz, direct expression of the diffusion process describing the population dynamics. The latter "diffusion" slow modes do not contribute to the stability conditions. Their characteristic times govern the transient response of the network; these reaction times also exhibit a simple dependence on the slope of the neuron transfer function. We speculate on the possible relevance of our results for the change in the characteristic response time of a neural population during the learning process which shapes the synaptic couplings, thereby affecting the slope of the transfer function. There is remarkable agreement of the theoretical predictions with simulations of a network of IF neurons with a constant leakage term for the membrane potential

Entomological knowledge in Madagascar by GBIF datasets: estimates on the coverage and possible biases (Insecta)

Although Madagascar is one of the world's most important biodiversity hotspots, the knowledge of its faunistic diversity is still incomplete, notwithstanding many field campaigns were organized since the 17th century until nowadays, leading to a huge number of vertebrate and invertebrate records. In this contribution, taking into consideration the geographic distribution by a GBIF dataset including 286,764 records referred to nine insect orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera, Neuroptera, Odonata, Orthoptera, Trichoptera), we tried to supply some observations on the spatial distribution and to point out some possible biases in the entomological knowledge of Madagascar. Hymenoptera, Coleoptera and Diptera were the most represented orders in the dataset, respectively. Some orders show many "coupled" sampling, with peaks of shared sampled localities between Diptera with Hymenoptera (98.07%) and Hemiptera with Coleoptera (64.21%). Considering the geographic location and the extension of the vegetation macrogroups in Madagascar, the entomological data result unevenly distributed. Current Protected Areas' (PAs) network covers about the 70% of the total of the collecting localities for the nine insect orders considered, even though some, such as Trichoptera, Odonata, and Neuroptera seem significantly less protected than others. However, the possible new PAs planned for Madagascar could greatly increase in the future the protection level for all 9 insect orders analyzed, especially for Neuroptera, Odonata and Lepidoptera. A percentage of 82.3% of the whole sampling localities falls inside the PAs themselves or within 1000 m from their borders. A similar pattern is observed for the road network: the 62.9% of the localities fall at least at 1000 m from a road, with no sampling localities observed further than 10 km from a road; statistically significant clusters were observed in evaluating these biases, coinciding with major towns or PAs

Attentional processes during P3-based Brain Computer Interface task in amyotrophic lateral sclerosis patients

To be available for a wide range of end-users a brain-computer interface (BCI) should be flexible and adaptable to end-users’ cognitive strengths and weaknesses. People’s cognitive abilities change according to the disease they are affected by, and people suffering from the same disease could have different cognitive capacities. We aimed at investigating how the amyotrophic lateral sclerosis (ALS) disease, and two different cognitive attentional aspects [1] influenced the usage of a P3-based BC

Dimensional reduction in networks of non- Markovian spiking neurons: Equivalence of synaptic filtering and heterogeneous propagation delays

Understanding the collective behavior of the intricate web of neurons composing a brain is one of the most challenging and complex tasks of modern neuroscience. Part of this complexity resides in the distributed nature of the interactions between the network components: for instance, the neurons transmit their messages (through spikes) with delays, which are due to different axonal lengths (i.e., communication distances) and/or noninstantaneous synaptic transmission. In developing effective network models, both of these aspects have to be taken into account. In addition, a satisfactory description level must be chosen as a compromise between simplicity and faithfulness in reproducing the system behavior. Here we propose a method to derive an effective theoretical description - validated through network simulations at microscopic level - of the neuron population dynamics in many different working conditions and parameter settings, valid for any synaptic time scale. In doing this we assume relatively small instantaneous fluctuations of the input synaptic current. As a by-product of this theoretical derivation, we prove analytically that a network with non-instantaneous synaptic transmission with fixed spike delivery delay is equivalent to a network characterized by a suited distribution of spike delays and instantaneous synaptic transmission, the latter being easier to treat

Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table

Clinical and psychological outcome after surgery for lumbar spinal stenosis: A prospective observational study with analysis of prognostic factors

Background The identification of psychological risk factors is important for the selection of patients before spinal surgery. Moreover, the effect of surgical decompression in lumbar spinal stenosis (LSS) on psychological outcome is not previously well analyzed. Aim of paper to investigate clinical and psychological outcome after surgery for LSS and the effect of depressive symptoms and anxiety on the clinical outcome. Materials and methods A total of 25 patients with symptomatic LSS underwent decompressive surgery with or without spinal stabilization were prospectively enrolled in this observational surgery. The Symptom Checklist-90-Revised (SCL-90-R) was used to assess global psychological distress with a summary score termed Global Severity Index (GSI) and single psychological disorders including depression (DEP) and anxiety (ANX). The clinical outcome of surgery was evaluated with the Oswestry Disability Index (ODI) and visual analogue scale (VAS) pain assessment. Results Compared with baseline, there was a statistically significant improvement in VAS, ODI and GSI after surgery (p<0.05) in all patients. Univariate analysis revealed that patients with high GSI and anxiety and depression scores had significantly higher ODI and VAS scores in the follow-up with a bad outcome. Conclusions Surgery for spinal stenosis was effective to treat pain and disability. In this prospective study baseline global psychological distress, depression and anxiety were associated with poorer clinical outcome