scholarly journals Synaptic and intrinsic homeostasis cooperate to optimize single neuron response properties and tune integrator circuits

2016 ◽  
Vol 116 (5) ◽  
pp. 2004-2022 ◽  
Author(s):  
Jonathan Cannon ◽  
Paul Miller
Keyword(s):  
Firing Rate ◽  
Single Neuron ◽  
Neuronal Firing ◽  
Model Parameters ◽  
Normal Brain ◽  
Synaptic Homeostasis ◽  
Neuron Response ◽  
Normal Brain Function ◽  
Long Time ◽  
Mean And Variance

Homeostatic processes that provide negative feedback to regulate neuronal firing rate are essential for normal brain function, and observations suggest that multiple such processes may operate simultaneously in the same network. We pose two questions: why might a diversity of homeostatic pathways be necessary, and how can they operate in concert without opposing and undermining each other? To address these questions, we perform a computational and analytical study of cell-intrinsic homeostasis and synaptic homeostasis in single-neuron and recurrent circuit models. We demonstrate analytically and in simulation that when two such mechanisms are controlled on a long time scale by firing rate via simple and general feedback rules, they can robustly operate in tandem to tune the mean and variance of single neuron's firing rate to desired goals. This property allows the system to recover desired behavior after chronic changes in input statistics. We illustrate the power of this homeostatic tuning scheme by using it to regain high mutual information between neuronal input and output after major changes in input statistics. We then show that such dual homeostasis can be applied to tune the behavior of a neural integrator, a system that is notoriously sensitive to variation in parameters. These results are robust to variation in goals and model parameters. We argue that a set of homeostatic processes that appear to redundantly regulate mean firing rate may work together to control firing rate mean and variance and thus maintain performance in a parameter-sensitive task such as integration.

scholarly journals Resolving rates of mutation in the brain using single-neuron genomics

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Gilad D Evrony ◽  
Eunjung Lee ◽  
Peter J Park ◽  
Christopher A Walsh
Keyword(s):  
Single Cell ◽  
Somatic Mutation ◽  
Brain Function ◽  
Single Neuron ◽  
Bioinformatic Analysis ◽  
Normal Brain ◽  
Neuronal Function ◽  
Human Neurons ◽  
Normal Brain Function ◽  
The Brain

Whether somatic mutations contribute functional diversity to brain cells is a long-standing question. Single-neuron genomics enables direct measurement of somatic mutation rates in human brain and promises to answer this question. A recent study (<xref ref-type="bibr" rid="bib65">Upton et al., 2015</xref>) reported high rates of somatic LINE-1 element (L1) retrotransposition in the hippocampus and cerebral cortex that would have major implications for normal brain function, and suggested that these events preferentially impact genes important for neuronal function. We identify aspects of the single-cell sequencing approach, bioinformatic analysis, and validation methods that led to thousands of artifacts being interpreted as somatic mutation events. Our reanalysis supports a mutation frequency of approximately 0.2 events per cell, which is about fifty-fold lower than reported, confirming that L1 elements mobilize in some human neurons but indicating that L1 mosaicism is not ubiquitous. Through consideration of the challenges identified, we provide a foundation and framework for designing single-cell genomics studies.

scholarly journals Phase-tuned neuronal firing encodes human contextual representations for navigational goals

2017 ◽  
Author(s):  
Andrew J Watrous ◽  
Jonathan Miller ◽  
Salman E Qasim ◽  
Itzhak Fried ◽  
Joshua Jacobs
Keyword(s):  
Firing Rate ◽  
Medial Temporal Lobe ◽  
Single Neuron ◽  
Detection Algorithm ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Oscillation Detection ◽  
Intracranial Recordings

AbstractWe previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

Multiple Sites of Action Potential Initiation Increase Neuronal Firing Rate

2001 ◽  
Vol 86 (3) ◽  
pp. 1226-1236 ◽  
Author(s):  
Stephen A. Baccus ◽  
Christie L. Sahley ◽  
Kenneth J. Muller
Keyword(s):  
Firing Rate ◽  
Sensory Input ◽  
Single Neuron ◽  
Neuronal Firing ◽  
Initiation Increase ◽  
Mechanosensory Neurons ◽  
Neuronal Firing Rate ◽  
Action Potential Initiation ◽  
Sites Of Action ◽  
Potential Initiation

Sensory input to an individual interneuron or motoneuron typically evokes activity at a single site, the initial segment, so that firing rate reflects the balance of excitation and inhibition there. In a network of cells that are electrically coupled, a sensory input produced by appropriate, localized stimulation can cause impulses to be initiated in several places. An example in the leech is the chain of S cells, which are critical for sensitization of reflex responses to mechanosensory stimulation. S cells, one per segment, form an electrically coupled chain extending the entire length of the CNS. Each S cell receives input from mechanosensory neurons in that segment. Because impulses can arise in any S cell and can reliably propagate throughout the chain, all the S cells behave like a single neuron with multiple initiation sites. In the present experiments, well-defined stimuli applied to a small area of skin evoked mechanosensory action potentials that propagated centrally to several segments, producing S cell impulses in those segments. Following pressure to the skin, impulses arose first in the S cell of the same segment as the stimulus, followed by impulses in S cells in other segments. Often four or five separate initiation sites were observed. This timing of impulse initiation played an important role in increasing the frequency of firing. Impulses arising at different sites did not usually collide but added to the total firing rate of the chain. A computational model is presented to illustrate how mechanosensory neurons distribute the effects of a single sensory stimulus into spatially and temporally separated synaptic input. The model predicts that changes in impulse propagation in mechanosensory neurons can alter S cell frequency of firing by changing the number of initiation sites.

Global attractor alphabet of neural firing modes

2013 ◽  
Vol 110 (4) ◽  
pp. 907-915 ◽  
Author(s):  
Yoram Baram
Keyword(s):  
Firing Rate ◽  
Global Attractor ◽  
Single Neuron ◽  
Global Attractors ◽  
Neuronal Firing ◽  
Passive State ◽  
Rate Model ◽  
Neural Firing ◽  
Neural Interaction ◽  
Parameter Dependent

The elementary set, or alphabet, of neural firing modes is derived from the widely accepted conductance-based rectified firing-rate model. The firing dynamics of interacting neurons are shown to be governed by a multidimensional bilinear threshold discrete iteration map. The parameter-dependent global attractors of the map morph into 12 attractor types. Consistent with the dynamic modes observed in biological neuronal firing, the global attractor alphabet is highly visual and intuitive in the scalar, single-neuron case. As synapse permeability varies from high depression to high potentiation, the global attractor type varies from chaotic to multiplexed, oscillatory, fixed, and saturated. As membrane permeability decreases, the global attractor transforms from active to passive state. Under the same activation, learning and retrieval end at the same global attractor. The bilinear threshold structure of the multidimensional map associated with interacting neurons generalizes the global attractor alphabet of neuronal firing modes to multineuron systems. Selective positive or negative activation and neural interaction yield combinatorial revelation and concealment of stored neuronal global attractors.

scholarly journals Phase-tuned neuronal firing encodes human contextual representations for navigational goals

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Andrew J Watrous ◽  
Jonathan Miller ◽  
Salman E Qasim ◽  
Itzhak Fried ◽  
Joshua Jacobs
Keyword(s):  
Firing Rate ◽  
Medial Temporal Lobe ◽  
Single Neuron ◽  
Detection Algorithm ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Oscillation Detection ◽  
Intracranial Recordings

We previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a virtual navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

scholarly journals Two modes of inhibitory neuronal shutdown distinctly amplify seizures in humans

2020 ◽  
Author(s):  
Omar J. Ahmed ◽  
Tibin T. John ◽  
Shyam K. Sudhakar ◽  
Ellen K.W. Brennan ◽  
Alcides Lorenzo Gonzalez ◽  
...  
Keyword(s):  
Brain Function ◽  
Action Potentials ◽  
Computational Models ◽  
Neuronal Firing ◽  
Inhibitory Neurons ◽  
Normal Brain ◽  
Neuronal Control ◽  
Normal Brain Function ◽  
Fast Spiking

ABSTRACTInhibitory neurons are critical for normal brain function but dysregulated in disorders such as epilepsy. At least two theories exist for how inhibition may acutely decrease during a seizure: hyperpolarization of fast-spiking (FS) inhibitory neurons by other inhibitory neurons, or depolarization block (DB) of FS neurons resulting in an inability to fire action potentials. Firing rate alone is unable to disambiguate these alternatives. Here, we show that human FS neurons can stop firing due to both hyperpolarization and DB within the same seizure. However, only DB of FS cells is associated with dramatic increases in local seizure amplitude, unobstructed traveling waves, and transient increases in excitatory neuronal firing. This result is independent of seizure etiology or focus. Computational models of DB reproduce the in vivo human biophysics. These methods enable intracellular decoding using only extracellular recordings in humans and explain the otherwise ambiguous inhibitory neuronal control of human seizures.

scholarly journals New Players Tip the Scales in the Balance between Excitatory and Inhibitory Synapses

2005 ◽  
Vol 1 ◽  
pp. 1744-8069-1-12 ◽  
Author(s):  
Joshua N Levinson ◽  
Alaa El-Husseini
Keyword(s):  
Brain Function ◽  
Single Neuron ◽  
Neuronal Excitability ◽  
Synapse Formation ◽  
Inhibitory Synapses ◽  
Normal Brain ◽  
Signaling Proteins ◽  
Tight Control ◽  
Normal Brain Function

Synaptogenesis is a highly controlled process, involving a vast array of players which include cell adhesion molecules, scaffolding and signaling proteins, neurotransmitter receptors and proteins associated with the synaptic vesicle machinery. These molecules cooperate in an intricate manner on both the pre- and postsynaptic sides to orchestrate the precise assembly of neuronal contacts. This is an amazing feat considering that a single neuron receives tens of thousands of synaptic inputs but virtually no mismatch between pre- and postsynaptic components occur in vivo. One crucial aspect of synapse formation is whether a nascent synapse will develop into an excitatory or inhibitory contact. The tight control of a balance between the types of synapses formed regulates the overall neuronal excitability, and is thus critical for normal brain function and plasticity. However, little is known about how this balance is achieved. This review discusses recent findings which provide clues to how neurons may control excitatory and inhibitory synapse formation, with focus on the involvement of the neuroligin family and PSD-95 in this process.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

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