21,687 research outputs found
Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease
This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer
Oscillations, metastability and phase transitions in brain and models of cognition
Neuroscience is being practiced in many different forms and at many different organizational levels of the Nervous System. Which of these levels and associated conceptual frameworks is most informative for elucidating the association of neural processes with processes of Cognition is an empirical question and subject to pragmatic validation. In this essay, I select the framework of Dynamic System Theory. Several investigators have applied in recent years tools and concepts of this theory to interpretation of observational data, and for designing neuronal models of cognitive functions. I will first trace the essentials of conceptual development and hypotheses separately for discerning observational tests and criteria for functional realism and conceptual plausibility of the alternatives they offer. I will then show that the statistical mechanics of phase transitions in brain activity, and some of its models, provides a new and possibly revealing perspective on brain events in cognition
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex
How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852
Dynamic Adaptive Computation: Tuning network states to task requirements
Neural circuits are able to perform computations under very diverse
conditions and requirements. The required computations impose clear constraints
on their fine-tuning: a rapid and maximally informative response to stimuli in
general requires decorrelated baseline neural activity. Such network dynamics
is known as asynchronous-irregular. In contrast, spatio-temporal integration of
information requires maintenance and transfer of stimulus information over
extended time periods. This can be realized at criticality, a phase transition
where correlations, sensitivity and integration time diverge. Being able to
flexibly switch, or even combine the above properties in a task-dependent
manner would present a clear functional advantage. We propose that cortex
operates in a "reverberating regime" because it is particularly favorable for
ready adaptation of computational properties to context and task. This
reverberating regime enables cortical networks to interpolate between the
asynchronous-irregular and the critical state by small changes in effective
synaptic strength or excitation-inhibition ratio. These changes directly adapt
computational properties, including sensitivity, amplification, integration
time and correlation length within the local network. We review recent
converging evidence that cortex in vivo operates in the reverberating regime,
and that various cortical areas have adapted their integration times to
processing requirements. In addition, we propose that neuromodulation enables a
fine-tuning of the network, so that local circuits can either decorrelate or
integrate, and quench or maintain their input depending on task. We argue that
this task-dependent tuning, which we call "dynamic adaptive computation",
presents a central organization principle of cortical networks and discuss
first experimental evidence.Comment: 6 pages + references, 2 figure
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