scholarly journals A Mechanism for Synaptic Copy Between Neural Circuits

2019 ◽  
Vol 31 (10) ◽  
pp. 1964-1984
Author(s):  
Yuxiu Shao ◽  
Binxu Wang ◽  
Andrew T. Sornborger ◽  
Louis Tao
Keyword(s):  
Information Transfer ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Transfer Mechanism ◽  
Neural Systems ◽  
Memory Transfer ◽  
Significant Evidence ◽  
Cortical Oscillations ◽  
Neural Populations

Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome and spikes to be propagated downstream in a circuit. Thus, an observation of oscillations in neural circuits would be an indication that repeated synchronous spiking may be enabling information transfer. However, for memory transfer, in which synaptic weights must be being transferred from one neural circuit (region) to another, what is the mechanism? Here, we present a synaptic transfer mechanism whose structure provides some understanding of the phenomena that have been implicated in memory transfer, including nested oscillations at various frequencies. The circuit is based on the principle of pulse-gated, graded information transfer between neural populations.

scholarly journals A mechanism for synaptic copy between neural circuits

2018 ◽  
Author(s):  
Yuxiu Shao ◽  
Binxu Wang ◽  
Andrew T. Sornborger ◽  
Louis Tao
Keyword(s):  
Memory Consolidation ◽  
Information Transfer ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Neural Systems ◽  
Long Term Memory ◽  
Cortical Oscillations ◽  
The Brain ◽  
Putative Mechanism

The brain has a central, short-term learning module, the hippocampus, which transfers what it has learned to long-term memory in cortex during non-REM sleep. The putative mechanism responsible for this type of memory consolidation invokes hierarchically nested hippocampal ripples (100-250 Hz), thalamo-cortical spindles (7-15 Hz), and cortical slow oscillations (< 1 Hz) to enable transfer. Suppression of, for instance, thalamic spindles has been shown to impair hippocampus-dependent memory consolidation. Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome, and spikes to be propagated downstream in a circuit. Thus, an observation of oscillations in neural circuits would be an indication that repeated synchronous spiking is enabling information transfer. However, for memory transfer, in which synaptic weights must be being transferred from one neural circuit (region) to another, what is the mechanism? Here, we present a synaptic transfer mechanism whose structure provides some understanding of the phenomena that have been implicated in memory transfer, including the nested oscillations at various frequencies. The circuit is based on the principle of pulse-gated, graded information transfer between neural populations.PACS numbers: 87.18.Sn,87.19.lj,87.19.lm,87.19.lq

scholarly journals Contemporary approaches to neural circuit manipulation and mapping: focus on reward and addiction

2015 ◽  
Vol 370 (1677) ◽  
pp. 20140210 ◽  
Author(s):  
Benjamin T. Saunders ◽  
Jocelyn M. Richard ◽  
Patricia H. Janak
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Neural Systems ◽  
Adaptive Behaviour ◽  
Psychological Processes ◽  
Major Goal ◽  
Recent Advances ◽  
Great Insight ◽  
Midbrain Dopamine ◽  
Insight Into

Tying complex psychological processes to precisely defined neural circuits is a major goal of systems and behavioural neuroscience. This is critical for understanding adaptive behaviour, and also how neural systems are altered in states of psychopathology, such as addiction. Efforts to relate psychological processes relevant to addiction to activity within defined neural circuits have been complicated by neural heterogeneity. Recent advances in technology allow for manipulation and mapping of genetically and anatomically defined neurons, which when used in concert with sophisticated behavioural models, have the potential to provide great insight into neural circuit bases of behaviour. Here we discuss contemporary approaches for understanding reward and addiction, with a focus on midbrain dopamine and cortico-striato-pallidal circuits.

scholarly journals Oscillatory integration windows in neurons

2016 ◽  
Author(s):  
Nitin Gupta ◽  
Swikriti Saran Singh ◽  
Mark Stopfer
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Field Potential ◽  
Coincidence Detection ◽  
Uniform Structure ◽  
Detection Mechanism ◽  
Oscillation Frequencies ◽  
Local Field Potential Lfp ◽  
Oscillatory Cycle

AbstractOscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly tested this idea in the locust olfactory system. We found that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we found that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrated that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.

scholarly journals Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

2019 ◽  
Author(s):  
Samantha Hughes ◽  
Tansu Celikel
Keyword(s):  
Motor Neurons ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Sensory Information ◽  
Circuit Complexity ◽  
Inhibitory Circuits ◽  
Invertebrate Species ◽  
Gating Mechanism ◽  
Motor Actions ◽  
Sensorimotor Representations

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.

Artificial Intelligence Based on Biological Neurons

2020 ◽  
pp. 25-53
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Next Generation ◽  
New Approach ◽  
Home Based

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home-based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.

The Neural Basis of Escape Behavior in Vertebrates

2020 ◽  
Vol 43 (1) ◽  
pp. 417-439 ◽  
Author(s):  
Tiago Branco ◽  
Peter Redgrave
Keyword(s):  
Neural Circuits ◽  
Escape Behavior ◽  
Building Blocks ◽  
Normative Theory ◽  
Neural Systems ◽  
Adaptive Behaviors ◽  
Neural Basis ◽  
Sensory Stimuli ◽  
Evolutionarily Conserved ◽  
Escape Behaviors

Escape is one of the most studied animal behaviors, and there is a rich normative theory that links threat properties to evasive actions and their timing. The behavioral principles of escape are evolutionarily conserved and rely on elementary computational steps such as classifying sensory stimuli and executing appropriate movements. These are common building blocks of general adaptive behaviors. Here we consider the computational challenges required for escape behaviors to be implemented, discuss possible algorithmic solutions, and review some of the underlying neural circuits and mechanisms. We outline shared neural principles that can be implemented by evolutionarily ancient neural systems to generate escape behavior, to which cortical encephalization has been added to allow for increased sophistication and flexibility in responding to threat.

scholarly journals A novel device for real-time measurement and manipulation of licking behavior in head-fixed mice

2018 ◽  
Vol 120 (6) ◽  
pp. 2975-2987 ◽  
Author(s):  
Brice Williams ◽  
Anderson Speed ◽  
Bilal Haider
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Closed Loop ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Visual Behavior ◽  
Sensory Stimuli ◽  
Neural Recordings ◽  
Control Signals ◽  
Sensory Motor

The mouse has become an influential model system for investigating the mammalian nervous system. Technologies in mice enable recording and manipulation of neural circuits during tasks where they respond to sensory stimuli by licking for liquid rewards. Precise monitoring of licking during these tasks provides an accessible metric of sensory-motor processing, particularly when combined with simultaneous neural recordings. There are several challenges in designing and implementing lick detectors during head-fixed neurophysiological experiments in mice. First, mice are small, and licking behaviors are easily perturbed or biased by large sensors. Second, neural recordings during licking are highly sensitive to electrical contact artifacts. Third, submillisecond lick detection latencies are required to generate control signals that manipulate neural activity at appropriate time scales. Here we designed, characterized, and implemented a contactless dual-port device that precisely measures directional licking in head-fixed mice performing visual behavior. We first determined the optimal characteristics of our detector through design iteration and then quantified device performance under ideal conditions. We then tested performance during head-fixed mouse behavior with simultaneous neural recordings in vivo. We finally demonstrate our device’s ability to detect directional licks and generate appropriate control signals in real time to rapidly suppress licking behavior via closed-loop inhibition of neural activity. Our dual-port detector is cost effective and easily replicable, and it should enable a wide variety of applications probing the neural circuit basis of sensory perception, motor action, and learning in normal and transgenic mouse models. NEW & NOTEWORTHY Mice readily learn tasks in which they respond to sensory cues by licking for liquid rewards; tasks that involve multiple licking responses allow study of neural circuits underlying decision making and sensory-motor integration. Here we design, characterize, and implement a novel dual-port lick detector that precisely measures directional licking in head-fixed mice performing visual behavior, enabling simultaneous neural recording and closed-loop manipulation of licking.

scholarly journals The dialectic of Hebb and homeostasis

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160258 ◽  
Author(s):  
Gina G. Turrigiano
Keyword(s):  
Recent Progress ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Homeostatic Plasticity ◽  
Homeostatic Control ◽  
Spatial And Temporal Scales ◽  
Network Function ◽  
Hebbian Plasticity ◽  
Circuit Function ◽  
Hebbian Mechanisms

It has become widely accepted that homeostatic and Hebbian plasticity mechanisms work hand in glove to refine neural circuit function. Nonetheless, our understanding of how these fundamentally distinct forms of plasticity compliment (and under some circumstances interfere with) each other remains rudimentary. Here, I describe some of the recent progress of the field, as well as some of the deep puzzles that remain. These include unravelling the spatial and temporal scales of different homeostatic and Hebbian mechanisms, determining which aspects of network function are under homeostatic control, and understanding when and how homeostatic and Hebbian mechanisms must be segregated within neural circuits to prevent interference. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

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