Temporal code-driven stimulation: definition and application to electric fish signaling

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permissionClosed-loop activity-dependent stimulation is a powerful methodology to assess information processing in biological systems. In this context, the development of novel protocols, their implementation in bioinformatics toolboxes and their application to different description levels open up a wide range of possibilities in the study of biological systems. We developed a methodology for studying biological signals representing them as temporal sequences of binary events. A specific sequence of these events (code) is chosen to deliver a predefined stimulation in a closed-loop manner. The response to this code-driven stimulation can be used to characterize the system. This methodology was implemented in a real time toolbox and tested in the context of electric fish signaling. We show that while there are codes that evoke a response that cannot be distinguished from a control recording without stimulation, other codes evoke a characteristic distinct response. We also compare the code-driven response to open-loop stimulation. The discussed experiments validate the proposed methodology and the software toolbox.This work was funded by Spanish projects of Ministerio de Economia y Competitividad/FEDER TIN-2010-19607, TIN2014-54580-R, TIN-2012-30883, DPI2015 65833-P (http://www.mineco.gob.es/), ONRG grant N62909-14-1-N279, Brazilian Agency of Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (http://www.cnpq.br/) and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (www.fapesp.br). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Online video tracking for activity-dependent stimulation in neuroethology

Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011. Stockholm, Sweden. 23-28 July 2011This work was supported by grants MICINN BFU2009-08473 and TIN 2010- 19607, Spanish-Brazilian Cooperation PHB2007-0008 and Brazilian agencies FAPESP, CNPq and CAPES

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

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)

Cessation of firing.

<p>Cessation of firing.</p

Data analyzes: binarization, entropy and post stimulus calculations.

<p><b>A –</b> Binarization and set of patterns for entropy calculations. The time axis is discretized into small bins. The presence of an EOD in each bin is represented by 1, whereas an absence is represented by 0. Timestamp sequences are analyzed in bit strings obtained in 40<i>W = </i>{<i>w₁</i>, <i>w₂</i>, <i>w₃</i>,…, <i>w_N</i>}. From the set of probabilities of each word <i>w_i</i> we computed the entropy <i>H</i>(W). <b>B –</b> Post stimulus time (PST) is defined as the time intervals (in red) <i>ti</i> between stimulus pulses (in blue) and the fish response ones (in green).</p

Cessation of firing for 48 h non stimulated fish.

<p>Cessation of firing for 48 h non stimulated fish.</p

Experiments with stimulation.

<p>The protocol was a sequence of 5 sessions (30 min each) as follows: first control recording (without stimulation)(black), stimulation with a real distribution of IPIs (green), second control (blue), stimulation with random distribution of IPIs (yellow), and a final control (red). <b>A-</b> Histograms of the response IPIs for each session: first, second and third controls in black, blue and red respectively. Response to real stimuli in green and to random one in yellow. In the first 30 min, fish discharged mostly 20.5 ms IPIs. In response to real IPI stimuli, the IPI distribution was bimodal with 2 equally probable values: 17 ms and 19.5 ms. In the second control session the IPI distribution changed presenting a single sharp peak in 21 ms. The distribution in response to a random stimuli was broader than for the control session with a single peak in 20 ms. The fish reacted to the third control session firing mostly longer IPIs with a peak in 21.5 ms. <b>B –</b> Sliding window histogram of IPIs <i>versus</i> time. High (low) probabilities are shown in red (blue). The same colorcode (red/yellow/blue) associated with high/intermediate/low probability explained in Fig. 5 is used here. <b>C –</b> Inferred movement, and <b>D –</b> entropy <i>versus</i> time. Fish reacted to both stimuli by decreasing the IPIs values (increasing the EODs frequency) and increasing its variability from 19–22 ms to 14.5–21 ms. During random stimuli (yellow bar) fish presented a slight relaxation with the peak of IPIs tending to higher values over time from 20 ms to 21.5 ms as well as in the third control session from 21 ms to 23 ms. The fish was restlessly moving throughout the experiment with no simple relation to the stimulation sessions. The entropy clearly increased (decreased) when both stimuli were turned on (off). <b>E –</b> Same as A but 2 weeks later. For the first 30<b> </b>min, the IPI distribution presented a single sharp peak in 20 ms (black). For the rest of the sessions the IPI distribution were bimodal differing in the peak values. The fish fired shorter IPIs (17.5 ms) in response to a real IPI stimuli session (green). The IPI distributions in response to the second control (blue) and random stimuli (yellow) were very similar with peaks around 21 ms and 24.5 ms. For the last control session fish fired longer IPIs compared to the previous sessions with peaks in 21.5 ms and 25 ms. <b>F-H </b><b>D-F –</b> same as <b>A-C B-D</b> but for an experiment performed 2 weeks later with the same individual. The same qualitative behavior was found with the remarkable exception that during the stimulation with a random distribution fish presented several epochs characterized by: very high IPI values around 24.5 ms, absence of movement, and very low entropy values, probable sleep-like state that was expected only during control sessions.</p

Automatic Realistic Real Time Stimulation/Recording in Weakly Electric Fish: Long Time Behavior Characterization in Freely Swimming Fish and Stimuli Discrimination

<div><p>Weakly electric fish are unique model systems in neuroethology, that allow experimentalists to non-invasively, access, central nervous system generated spatio-temporal electric patterns of pulses with roles in at least 2 complex and incompletely understood abilities: electrocommunication and electrolocation. Pulse-type electric fish alter their inter pulse intervals (IPIs) according to different behavioral contexts as aggression, hiding and mating. Nevertheless, only a few behavioral studies comparing the influence of different stimuli IPIs in the fish electric response have been conducted. We developed an apparatus that allows real time automatic realistic stimulation and simultaneous recording of electric pulses in freely moving <i>Gymnotus carapo</i> for several days. We detected and recorded pulse timestamps independently of the fish’s position for days. A stimulus fish was mimicked by a dipole electrode that reproduced the voltage time series of real conspecific according to previously recorded timestamp sequences. We characterized fish behavior and the eletrocommunication in 2 conditions: stimulated by IPIs pre-recorded from other fish and random IPI ones. All stimuli pulses had the exact <i>Gymontus carapo</i> waveform. All fish presented a surprisingly long transient exploratory behavior (more than 8 h) when exposed to a new environment in the absence of electrical stimuli. Further, we also show that fish are able to discriminate between real and random stimuli distributions by changing several characteristics of their IPI distribution.</p></div

Dual n-back training improves functional connectivity of the right inferior frontal gyrus at rest

Several studies have shown that the benefits of working memory (WM) training can be attributed to functional and structural neural changes in the underlying neural substrate. In the current study, we investigated whether the functional connectivity of the brain at rest in the default mode network (DMN) changes with WM training. We varied the complexity of the training intervention so, that half of the participants attended dual n-back training whereas the other half attended single n-back training. This way we could assess the effects of different training task parameters on possible connectivity changes. After 16 training sessions, the dual n-back training group showed improved performance accompanied by increased functional connectivity of the ventral DMN in the right inferior frontal gyrus, which correlated with improvements in WM. We also observed decreased functional connectivity in the left superior parietal cortex in this group. The single n-back training group did not show significant training-related changes. These results show that a demanding short-term WM training intervention can alter the default state of the brain.Peer Reviewe