Structured chaos shapes spike-response noise entropy in balanced neural networks

Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. A striking feature of these networks is that they are chaotic. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for the entropy of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complimented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos," as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.Comment: 9 pages, 5 figure

Statistical Evaluation of Waveform Collapse Reveals Scale-Free Properties of Neuronal Avalanches

Neural avalanches are a prominent form of brain activity characterized by network-wide bursts whose statistics follow a power-law distribution with a slope near 3/2. Recent work suggests that avalanches of different durations can be rescaled and thus collapsed together. This collapse mirrors work in statistical physics where it is proposed to form a signature of systems evolving in a critical state. However, no rigorous statistical test has been proposed to examine the degree to which neuronal avalanches collapse together. Here, we describe a statistical test based on functional data analysis, where raw avalanches are first smoothed with a Fourier basis, then rescaled using a time-warping function. Finally, an F ratio test combined with a bootstrap permutation is employed to determine if avalanches collapse together in a statistically reliable fashion. To illustrate this approach, we recorded avalanches from cortical cultures on multielectrode arrays as in previous work. Analyses show that avalanches of various durations can be collapsed together in a statistically robust fashion. However, a principal components analysis revealed that the offset of avalanches resulted in marked variance in the time-warping function, thus arguing for limitations to the strict fractal nature of avalanche dynamics. We compared these results with those obtained from cultures treated with an AMPA/NMDA receptor antagonist (APV/DNQX), which yield a power-law of avalanche durations with a slope greater than 3/2. When collapsed together, these avalanches showed marked misalignments both at onset and offset time-points. In sum, the proposed statistical evaluation suggests the presence of scale-free avalanche waveforms and constitutes an avenue for examining critical dynamics in neuronal systems

Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination

Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be "room" for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10's of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure

Caractérisation des infrastructures à l'aide des diagraphies d'imagerie optique et acoustique

L'entretien et le suivi des barrages constituent un besoin grandissant au Québec dû à l'âge moyen des installations. Bien qu'il existe des méthodes d'inspections incluant l'inspection visuelle, le carottage et des essais en laboratoire afin de déterminer l'état général et certains paramètres de la résistance au glissement tels l'angle de friction et la résistance à la compression, il subsiste encore certains manques.L'orientation et l'ouverture des plans de faiblesses sont, entre autre, deux paramètres importants dans la résistance au glissement qui ne peuvent être déterminés avec exactitude à l'aide des méthodes conventionnelles. Ce projet de recherche propose une méthode afin de pallier aux lacunes des techniques actuelles ainsi que de compléter l'étude de stabilité des ouvrages. Cette méthode consiste en l'emploi des diagraphies d'imagerie qui permettent d'obtenir une image numérique de la paroi du trou de forage à l'aide de caméras optique et acoustique. Un programme expérimental comprenant diverses séries de tests en laboratoire et l'auscultation de trois structures réelles a été mis sur place. Les essais en laboratoire ont, entre autre, permis de déterminer que ces méthodes présentent une détectabilité pouvant être moins d'un millimètre avec une erreur moyenne sur la mesure d'environ 2mm. Ces paramètres sont cependant grandement influencés par l'état des épontes surtout pour la sonde acoustique.L'auscultation de structures réelles a permis d'établir une méthode de traitement des données comprenant la correction de l'influence des zones magnétiques locales afin d'obtenir une orientation (direction et pendage) des plans de faiblesses. Les essais en laboratoire et les tests sur le terrain ont également permis de déterminer que ces méthodes constituent un bon moyen de valider, en corrélation avec les carottes de forage, la cohésion et la nature (mécanique ou naturel) des discontinuités. De plus, elles peuvent fournir des informations essentielles lors d'une faible récupération des échantillons

Addition of Red Clover or Birdsfoot Trefoil in Alfalfa-based Mixtures to Improve the Forage Energy to Protein Balance

The low ratio of sugars (S) to crude proteins (CP) in alfalfa (AL, Medicago sativa L.) leads to inefficient use of nitrogen by ruminants. The objective was to determine if adding red clover (RC, Trifolium pratense L.) or birdsfoot trefoil (BT, Lotus corniculatus L.) with or without a grass species to AL improved the forage S/CP ratio. Treatments were 100% AL (control) or AL-based mixtures with RC or BT in three proportions (75, 50, or 25% of seeded legumes) with either no grass or with timothy (Phleum pratense L.) or tall fescue (Schedonorus arundinaceus Schreb. Dumort.), resulting in 21 treatments assigned to a randomized complete block design with four replications at three sites in Canada (Agassiz, BC; St-Anne-de-Bellevue, QC; StAugustin-de-Desmaures, QC). Species contribution and nutritive attributes measured at each harvest were weighted for yield as a proportion of the seasonal yield and expressed yearly for the first two post-seeding years. Regression analyses showed that forage S concentration increased, CP concentration tended to decrease, and the S/CP ratio increased from 0.3 to 0.5 (y = 0.002 x + 0.3; P = 0.003, R2 = 0.53) with the addition of up to 92% RC or up to 66% BT to AL-based mixtures. The addition of up to 61% TI or 55% TF did not impact the S/CP ratio of AL-based mixtures. Further studies are needed to determine if the improved forage S/CP ratio following the addition of RC or BT to AL-based mixtures leads to an improved N-use efficiency in ruminants

Transcranial magnetic stimulation measures in the elderly: reliability, smallest detectable change and the potential influence of lifestyle habits

Background: Transcranial magnetic stimulation (TMS) is a non-invasive technique that can be used to evaluate cortical function and corticospinal pathway in normal and pathological aging. Yet, the metrologic properties of TMS-related measurements is still limited in the aging population. Objectives: The aim of this cross-sectional study was to document the reliability and smallest detectable change of TMS measurements among community-dwelling seniors. A secondary objective was to test if TMS measurements differ between elders based on lifestyle, medical and socio-demographic factors. Methods: Motor evoked potentials (MEPs) elicited by single-pulse TMS were recorded in the first dorsal interosseous (FDI) in 26 elderly individuals (mean age = 70 3.8 years). Resting motor threshold (rMT), MEP amplitudes and contralateral silent period (cSP) were measured on two separate occasions (1-week interval), and the standard error of the measurement (SEMeas), intraclass correlation coefficient (ICC), and smallest detectable change in an individual (SDCindv) were calculated. Lifestyle, medical and socio-demographic factors were collected using questionnaires. TMS-related outcomes were compared using independent sample t-test based on the presence of chronic health diseases, chronic medication intake, obesity, history of smoking, physical activity levels, gender, and level of education. Results: rMT and cSP measures were the most reliable outcomes, with the lowest SEMeas and highest ICCs, whereas MEP amplitude-related measures were less reliable. SDCindv levels were generally high, even for rMT (7.29 %MSO) and cSP (43.16– 50.84 ms) measures. Although not systematically significant, results pointed toward a higher corticospinal excitability in elderly individuals who were regularly active, who had no chronic medical conditions and who did not take any medication. Conclusion: Even though SDCindv levels were relatively high, these results show that rMT and cSP are the most reliable outcomes to investigate age-related changes in the corticomotor system and suggest that the influence of factors such as lifestyle habits and medications on TMS measures should be investigated further

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)

How does non-random spontaneous activity contribute to brain development?

Highly non-random forms of spontaneous activity are proposed to play an instrumental role in the early development of the visual system. However, both the fundamental properties of spontaneous activity required to drive map formation, as well as the exact role of this information remain largely unknown. Here, a realistic computational model of spontaneous retinal waves is employed to demonstrate that both the amplitude and frequency of waves may play determining roles in retinocollicular map formation. Furthermore, results obtained with different learning rules show that spike precision in the order of milliseconds may be instrumental to neural development: a rule based on precise spike interactions (spike-timing-dependent plasticity) reduced the density of aberrant projections to the SC to a markedly greater extent than a rule based on interactions at much broader time-scale (correlation-based plasticity). Taken together, these results argue for an important role of spontaneous yet highly non-random activity, along with temporally precise learning rules, in the formation of neural circuits.Peer reviewed: YesNRC publication: Ye

Knowledge selection, mapping and transfer in artificial neural networks

Knowledge-based Cascade-correlation is a neural network algorithm that combines inductive learning and knowledge transfer (Shultz & Rivest, 2001). In the present thesis, this algorithm is tested on several real-world and artificial problems, and extended in several ways. The first extension consists in the incorporation of the Knowledge-based Artificial Neural Network (KBANN; Shavlik, 1994) technique for generating rule-based (RBCC) networks. The second extension consists of the adaptation of the Optimal Brain Damage (OBD; LeCun, Denker, & Solla, 1990) pruning technique to remove superfluous connection weights. Finally, the third extension consists in a new objective function based on information theory for controlling the distribution of knowledge attributed to subnetworks. A simulation of lexical ambiguity resolution is proposed. In this study, the use of RBCC networks is motivated from a cognitive and neurophysiological perspective