arXiv: Neurons and Cognition
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Connectomics and network neuroscience offer quantitative scientific frameworks for modeling and analyzing networks of structurally and functionally interacting neurons, neuronal populations, and macroscopic brain areas. This shift in perspective and emphasis on distributed brain function has provided fundamental insight into the role played by the brain's network architecture in cognition, disease, development, and aging. In this chapter, we review the core concepts of human connectomics at the ...
#1Kevin L. McKeeH-Index: 4
#2Ian CrandellH-Index: 3
Last. Randall C. O'ReillyH-Index: 63
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The Bayesian brain hypothesis postulates that the brain accurately operates on statistical distributions according to Bayes' theorem. The random failure of presynaptic vesicles to release neurotransmitters may allow the brain to sample from posterior distributions of network parameters, interpreted as epistemic uncertainty. It has not been shown previously how random failures might allow networks to sample from observed distributions, also known as aleatoric or residual uncertainty. Sampling fro...
#1Nan XuH-Index: 2
#2Theodore J. LaGrowH-Index: 2
Last. Shella D. KeilholzH-Index: 27
view all 14 authors...
Resting-state functional magnetic resonant imaging (rs-fMRI), which measures spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal, is increasingly utilized for the investigation of normal and pathological brain activity. Rodent models play a key role in studies that examine the neuronal and neurophysiological processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to h...
#1Eelke Spaak (Radboud University Nijmegen)H-Index: 12
The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figure out the hidden causes of its inputs, has become very influential in cognitive (neuro)science over recent decades. Here I present a relatively straightforward generalization of this idea: the primary computational task that the brain is faced with is to track the probabilistic structure of observations themselves, without recourse to hidden states. Taking this starting point seriously turns out ...
#1Elia Turner (Technion – Israel Institute of Technology)H-Index: 3
#2Kabir Dabholkar (Technion – Israel Institute of Technology)H-Index: 1
Last. Omri Barak (Technion – Israel Institute of Technology)H-Index: 20
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Recurrent Neural Networks (RNNs) were recently successfully used to model the way neural activity drives task-related behavior in animals, operating under the implicit assumption that the obtained solutions are universal. Observations in both neuroscience and machine learning challenge this assumption. Animals can approach a given task with a variety of strategies, and training machine learning algorithms introduces the phenomenon of underspecification. These observations imply that every task i...
We investigate spike-timing dependent plasticity (STPD) in the case of a synapse connecting two neural cells. We develop a theoretical analysis of several STDP rules using Markovian theory. In this context there are two different timescales, fast neural activity and slower synaptic weight updates. Exploiting this timescale separation, we derive the long-time limits of a single synaptic weight subject to STDP. We show that the pairing model of presynaptic and postsynaptic spikes controls the syna...
Undoubtedly, textural property of an image is one of the most important features in object recognition task in both human and computer vision applications. Here, we investigated the neural signatures of four well-known statistical texture features including contrast, homogeneity, energy, and correlation computed from the gray level co-occurrence matrix (GLCM) of the images viewed by the participants in the process of magnetoencephalography (MEG) data collection. To trace these features in the hu...
Analytical expressions for scaling of brain wave spectra derived from the general nonlinear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent nonlinear brain wave dynamics reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches...
#1Joel Dapello (Harvard University)H-Index: 5
#2Jenelle Feather (MIT: Massachusetts Institute of Technology)H-Index: 6
Last. SueYeon Chung (Columbia University)H-Index: 8
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Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems. Recent work has proposed adding biologically-inspired components to visual neural networks as a way to improve their adversarial robustness. One surprisingly effective component for reducing adversarial vulnerability is response stochasticity, like that exhibited by biological neurons. Here, using recently developed geometrica...
#1Pedro A. M. MedianoH-Index: 16
#2Fernando RosasH-Index: 29
Last. Daniel BorH-Index: 22
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Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here we summarise, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quanti...
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