scholarly journals Embedding optimization reveals long-lasting history dependence in neural spiking activity

2020 ◽  
Author(s):  
Lucas Rudelt ◽  
Daniel González Marx ◽  
Michael Wibral ◽  
Viola Priesemann
Keyword(s):  
Information Processing ◽  
Optimization Approach ◽  
History Dependence ◽  
Hierarchical Processing ◽  
Reliable Estimation ◽  
Neural Spiking ◽  
Properties Of Information ◽  
The Brain ◽  
Recording Techniques ◽  
Spiking Activity

AbstractInformation processing can leave distinct footprints on the statistical history dependence in single neuron spiking. Statistical history dependence can be quantified using information theory, but its estimation from experimental recordings is only possible for a reduced representation of past spiking, a so called past embedding. Here, we present a novel embedding-optimization approach that optimizes temporal binning of past spiking to capture most history dependence, while a reliable estimation is ensured by regularization. The approach does not only quantify non-linear and higher-order dependencies, but also provides an estimate of the temporal depth that history dependence reaches into the past. We benchmarked the approach on simulated spike recordings of a leaky integrate-and-fire neuron with long lasting spike-frequency-adaptation, where it accurately estimated history dependence over hundreds of milliseconds. In a diversity of extra-cellular spike recordings, including highly parallel recordings using a Neuropixel probe, we found some neurons with surprisingly strong history dependence, which could last up to seconds. Both aspects, the magnitude and the temporal depth of history dependence, showed interesting differences between recorded systems, which points at systematic differences in information processing between these systems. We provide practical guidelines in this paper and a toolbox for Python3 at https://github.com/Priesemann-Group/hdestimator for readers interested in applying the method to their data.Author summaryEven with exciting advances in recording techniques of neural spiking activity, experiments only provide a comparably short glimpse into the activity of only a tiny subset of all neurons. How can we learn from these experiments about the organization of information processing in the brain? To that end, we exploit that different properties of information processing leave distinct footprints on the firing statistics of individual spiking neurons. In our work, we focus on a particular statistical footprint: How much does a single neuron’s spiking depend on its own preceding activity, which we call history dependence. By quantifying history dependence in neural spike recordings, one can, in turn, infer some of the properties of information processing. Because recording lengths are limited in practice, a direct estimation of history dependence from experiments is challenging. The embedding optimization approach that we present in this paper aims at extracting a maximum of history dependence within the limits set by a reliable estimation. The approach is highly adaptive and thereby enables a meaningful comparison of history dependence between neurons with vastly different spiking statistics, which we exemplify on a diversity of spike recordings. In conjunction with recent, highly parallel spike recording techniques, the approach could yield valuable insights on how hierarchical processing is organized in the brain.

scholarly journals Embedding optimization reveals long-lasting history dependence in neural spiking activity

2021 ◽  
Vol 17 (6) ◽  
pp. e1008927
Author(s):  
Lucas Rudelt ◽  
Daniel González Marx ◽  
Michael Wibral ◽  
Viola Priesemann
Keyword(s):  
Information Processing ◽  
Mutual Information ◽  
Temporal Integration ◽  
Optimization Approach ◽  
Temporal Dependence ◽  
History Dependence ◽  
Exponential Stretching ◽  
Neural Spiking ◽  
Time Lagged

Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long—potentially redundant—past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so-called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information-theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox.

scholarly journals Functional determinants of enhanced and depressed inter-areal information flow in NREM sleep between neuronal ensembles in rat cortex and hippocampus

2018 ◽  
Author(s):  
Umberto Olcese ◽  
Jeroen J. Bos ◽  
Martin Vinck ◽  
Cyriel M.A. Pennartz
Keyword(s):  
Information Processing ◽  
Time Scales ◽  
Information Flow ◽  
Rem Sleep ◽  
Transfer Entropy ◽  
Behavioral Task ◽  
Related Information ◽  
Long Time ◽  
The Brain ◽  
Spiking Activity

AbstractCompared to wakefulness, neuronal activity during non-REM sleep is characterized by a decreased ability to integrate information, but also by the re-emergence of task-related information patterns. To investigate the mechanisms underlying these seemingly opposing phenomena, we measured directed information flow by computing transfer entropy between neuronal spiking activity in three cortical regions and the hippocampus of rats across brain states. State-dependent information flow resulted to be jointly determined by the anatomical distance between neurons and by their functional specialization. We distinguished two regimes, operating at short and long time scales, respectively. From wakefulness to non-REM sleep, transfer entropy at short time scales increased for inter-areal connections between neurons showing behavioral task correlates. Conversely, transfer entropy at long time scales became stronger between non-task modulated neurons and weaker between task- modulated neurons. These results may explain how, during non-REM sleep, a global inter-areal disconnection is compatible with highly specific task-related information transfer.Author SummaryThe brain remains active during deep sleep, yet we still do not know which rules govern information processing between neurons across wakefulness and sleep. Here we provide a first study of how information flow at the level of spiking activity varies as a function of brain state, temporal scale, brain area and behavioral task correlates of single neurons. We found that inter-areal communication at millisecond time scales is enhanced during sleep compared to wakefulness between neurons that code for task information. Conversely, non-modulated neurons showed more prominent communication at longer time scales. These results indicate that multiple, functionally determined communicative architectures coexist in the brain, and provide a novel framework to understand information processing and its consequences during sleep.

Event-Related Potentials in Psychiatry: Approaches to Research and Clinical Applications

1983 ◽  
Vol 17 (4) ◽  
pp. 307-318 ◽  
Author(s):  
H. G. Stampfer
Keyword(s):  
Information Processing ◽  
Psychiatric Disorders ◽  
Rapid Development ◽  
Event Related Potentials ◽  
Clinical Applications ◽  
Direct Access ◽  
Practical Application ◽  
Clinical Methods ◽  
Related Potentials ◽  
The Brain

This article suggests that the potential usefulness of event-related potentials in psychiatry has not been fully explored because of the limitations of various approaches to research adopted to date, and because the field is still undergoing rapid development. Newer approaches to data acquisition and methods of analysis, combined with closer co-operation between medical and physical scientists, will help to establish the practical application of these signals in psychiatric disorders and assist our understanding of psychophysiological information processing in the brain. Finally, it is suggested that psychiatrists should seek to understand these techniques and the data they generate, since they provide more direct access to measures of complex cerebral processes than current clinical methods.

Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback

2005 ◽  
Vol 17 (10) ◽  
pp. 2139-2175 ◽  
Author(s):  
Naoki Masuda ◽  
Brent Doiron ◽  
André Longtin ◽  
Kazuyuki Aihara
Keyword(s):  
Information Processing ◽  
Stimulus Frequency ◽  
Spiking Neurons ◽  
Feedforward Networks ◽  
Dynamical Characteristics ◽  
Neural Activities ◽  
Inherent Frequency ◽  
Types Of Information ◽  
The Brain

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

scholarly journals Management of Mathematics Anxiety through Behaviour Modification, Super brain Yoga and JPMR in Ninth Standard Student

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Dr. Rajesh Ganesan ◽  
Pankaj Singh
Keyword(s):  
Information Processing ◽  
Mathematics Anxiety ◽  
Math Anxiety ◽  
Rating Scale ◽  
Emotional Reactions ◽  
Progressive Muscle Relaxation ◽  
Hemispheric Dominance ◽  
Human Information Processing ◽  
Behaviour Modification ◽  
The Brain

Mathematics Anxiety is an irrational fear of Mathematics. Mathematics Anxiety is defined as “the presence of a syndrome of emotional reactions to arithmetic and mathematics” (Dreger & Aiken, 1957, p.344). It creates a feeling of tension, apprehension, or fear that interferes with performance in Mathematics and also results in ‘Mathematics-Avoidance’. Further, ‘Mathematics-Avoidance’ leads to less competency, exposure and practice of Mathematics, leaving students more anxious and mathematically, unprepared to achieve. Math anxiety is a learned response that inhibits cognitive performance in the math classroom. It is widespread among students from elementary age through college. Students suffering from math anxiety have difficulty performing calculations and maintaining a positive outlook on mathematics. Math anxiety is the result of a cycle of math avoidance that begins with negative experiences regarding mathematics. These students avoid Mathematic courses and tend to feel negative towards Mathematics and this also affects student’s overall confidence level. However, Behaviour Modification techniques have proven instruments that can reduce various types of anxieties and Super Brain Yoga for improving integration of the brain. This is a case study of a student of IX standard, Kendriya Vidalaya, Who was referred by his Mathematics teacher and parent complaining that the student becomes anxious whenever he encounters Mathematic problems. After taking Math autobiography it was revealed that the anxiety began due to harsh handling by father while teaching Mathematics. Students score in recent Mathematic exam was noted very low i.e 12/40. His Mathematics Anxiety was assessed by using Suri, Monroe and Koc’s (2012) short Mathematics Anxiety Rating Scale. Student’s hemispheric dominance of the brain was measured by using Taggart and Torrance’s Human Information Processing Survey (1984). This student was treated with Behaviour Modification techniques and Super Brain Yoga for six weeks. Interventions used are: (i) Reduction of Rate of Breathing (Ganesan, 2012). (ii) Jacobson Progressive Muscle Relaxation (Jacobson, 1938) (iii) Laughter Technique (Ganesan, 2008b). (iv) Develpoment of Alternate Emotional Responses to the Threatening Stimulus (Ganesan, 2008a). (v) Super Brain Yoga (Sui, 2005). The anxiety level and performance in Mathematics exam was reassessed after six weeks. Results showed that Mathematics Anxiety was significantly reduced (60 to 20, 40%) and he performed better in the Mathematics exam (12/40 to 24/40, 30%). After reassessing student on Human Information Processing Survey by Taggart and Torrance (1984), it was found that student’s dominant information processing mode was ‘Integrated’ and this shows that Behaviour Modification techniques and Super Brain Yoga are efficient in treating Mathematics Anxiety.

scholarly journals Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

2020 ◽  
Author(s):  
Soma Nonaka ◽  
Kei Majima ◽  
Shuntaro C. Aoki ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
High Performance ◽  
Deep Neural Networks ◽  
Recognition Performance ◽  
Brain Activity ◽  
Spatial Integration ◽  
Hierarchical Processing ◽  
Visual Areas ◽  
The Brain

SummaryAchievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on the decoding of individual DNN unit activations from human brain activity. We find that BH scores for 29 pretrained DNNs with varying architectures are negatively correlated with image recognition performance, indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that relatively simple feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method provides new ways for designing DNNs and understanding the brain in consideration of their representational homology.

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