16,196 research outputs found
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of âstrongâ artificial intelligence in robotics are brought forward
A roadmap to integrate astrocytes into Systems Neuroscience.
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease
Birth of a Learning Law
Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-92-J-1309
A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons
We present a biophysical approach for the coupling of neural network activity
as resulting from proper dipole currents of cortical pyramidal neurons to the
electric field in extracellular fluid. Starting from a reduced threecompartment
model of a single pyramidal neuron, we derive an observation model for
dendritic dipole currents in extracellular space and thereby for the dendritic
field potential that contributes to the local field potential of a neural
population. This work aligns and satisfies the widespread dipole assumption
that is motivated by the "open-field" configuration of the dendritic field
potential around cortical pyramidal cells. Our reduced three-compartment scheme
allows to derive networks of leaky integrate-and-fire models, which facilitates
comparison with existing neural network and observation models. In particular,
by means of numerical simulations we compare our approach with an ad hoc model
by Mazzoni et al. [Mazzoni, A., S. Panzeri, N. K. Logothetis, and N. Brunel
(2008). Encoding of naturalistic stimuli by local field potential spectra in
networks of excitatory and inhibitory neurons. PLoS Computational Biology 4
(12), e1000239], and conclude that our biophysically motivated approach yields
substantial improvement.Comment: 31 pages, 4 figure
Tracing the Biological Roots of Knowledge
The essay is a critical review of three possible approaches in the theory of knowledge while tracing the biological roots of knowledge: empiricist, rationalist and developmentalist approaches.
Piaget's genetic epistemology, a developmentalist approach, is one of the first comprehensive
treatments on the question of tracing biological roots of knowledge. This developmental approach is
currently opposed, without questioning the biological roots of knowledge, by the more popular
rationalist approach, championed by Chomsky. Developmental approaches are generally coherent
with cybernetic models, of which the theory of autopoiesis proposed by Maturana and Varela made
a significant theoretical move in proposing an intimate connection between metabolism and
knowledge. Modular architecture is currently considered more or less an undisputable model for
both biology as well as cognitive science. By suggesting that modulation of modules is possible by
motor coordination, a proposal is made to account for higher forms of conscious cognition within
the four distinguishable layers of the human mind. Towards the end, the problem of life and
cognition is discussed in the context of the evolution of complex cognitive systems, suggesting the
unique access of phylogeny during the ontogeny of human beings as a very special case, and how
the problem cannot be dealt with independent of the evolution of coding systems in nature
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