https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory The book is divided into three parts Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics Part III analyzes the role of plasticity in development and learning An appendix covers the mathematical methods used, and exercises are available on the book's Web site

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources — including Na+ and Ca2+ spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations — can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.

/pdf/the-origin-of-extracellular-fields-and-currents-eeg-ecog-lfp-2e79zygxj0.pdf

The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes

A paradox that exists in auditory and electrosensory neural systems is that they encode behaviorally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower. The importance of temporal coding in neural information processing is not clear yet. A central question is whether neuronal firing can be more precise than the time constants of the neuronal processes involved. Here we address this problem using the auditory system of the barn owl as an example. We present a modelling study based on computer simulations of a neuron in the laminar nucleus. Three observations explain the paradox. First, spiking of an 'integrate-and-fire' neuron driven by excitatory postsynaptic potentials with a width at half-maximum height of 250 micros, has an accuracy of 25 micros if the presynaptic signals arrive coherently. Second, the necessary degree of coherence in the signal arrival times can be attained during ontogenetic development by virtue of an unsupervised hebbian learning rule. Learning selects connections with matching delays from a broad distribution of axons with random delays. Third, the learning rule also selects the correct delays from two independent groups of inputs, for example, from the left and right ear.

/pdf/a-neuronal-learning-rule-for-sub-millisecond-temporal-coding-57369ghp02.pdf

A neuronal learning rule for sub-millisecond temporal coding

A correlation-based ~‘‘Hebbian’’ ! learning rule at a spike level with millisecond resolution is formulated, mathematically analyzed, and compared with learning in a firing-rate description. The relative timing of presynaptic and postsynaptic spikes influences synaptic weights via an asymmetric ‘‘learning window.’’ A differential equation for the learning dynamics is derived under the assumption that the time scales of learning and neuronal spike dynamics can be separated. The differential equation is solved for a Poissonian neuron model with stochastic spike arrival. It is shown that correlations between input and output spikes tend to stabilize structure formation. With an appropriate choice of parameters, learning leads to an intrinsic normalization of the average weight and the output firing rate. Noise generates diffusion-like spreading of synaptic weights. @S1063-651X~99!02804-4#

/pdf/hebbian-learning-and-spiking-neurons-185vew6wal.pdf

Hebbian learning and spiking neurons

Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase–phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and rapid eye movement sleep. Hippocampal theta waves were asymmetric, and estimation of the spatial position of the animal was improved by identifying the waveform-based phase of spiking, compared to traditional methods used for phase estimation. Using the waveform-based theta phase, three distinct gamma bands were identified: slow gamma S (gamma S ; 30–50 Hz), midfrequency gamma M (gamma M ; 50–90 Hz), and fast gamma F (gamma F ; 90–150 Hz or epsilon band). The amplitude of each sub-band was modulated by the theta phase. In addition, we found reliable phase–phase coupling between theta and both gamma S and gamma M but not gamma F oscillators. We suggest that cross-frequency phase coupling can support multiple time-scale control of neuronal spikes within and across structures.

https://www.jneurosci.org/content/jneuro/32/2/423.full.pdf

Cross-Frequency Phase–Phase Coupling between Theta and Gamma Oscillations in the Hippocampus

Tinnitus, the perception of a sound in the absence of acoustic stimulation, is often associated with hearing loss. Animal studies indicate that hearing loss through cochlear damage can lead to behavioral signs of tinnitus that are correlated with pathologically increased spontaneous firing rates, or hyperactivity, of neurons in the auditory pathway. Mechanisms that lead to the development of this hyperactivity, however, have remained unclear. We address this question by using a computational model of auditory nerve fibers and downstream auditory neurons. The key idea is that mean firing rates of these neurons are stabilized through a homeostatic plasticity mechanism. This homeostatic compensation can give rise to hyperactivity in the model neurons if the healthy ratio between mean and spontaneous firing rate of the auditory nerve is decreased, for example through a loss of outer hair cells or damage to hair cell stereocilia. Homeostasis can also amplify non-auditory inputs, which then contribute to hyperactivity. Our computational model predicts how appropriate additional acoustic stimulation can reverse the development of such hyperactivity, which could provide a new basis for treatment strategies.

/pdf/development-of-tinnitus-related-neuronal-hyperactivity-utgf4726u1.pdf

Development of tinnitus-related neuronal hyperactivity through homeostatic plasticity after hearing loss: a computational model.

https://itb.biologie.hu-berlin.de/~kempter/Publications/2006/HearRes/koenig06.pdf

Richard Kempter

Papers

A neuronal learning rule for sub-millisecond temporal coding

Hebbian learning and spiking neurons

Cross-Frequency Phase–Phase Coupling between Theta and Gamma Oscillations in the Hippocampus

Development of tinnitus-related neuronal hyperactivity through homeostatic plasticity after hearing loss: a computational model.

Course of hearing loss and occurrence of tinnitus