8 research outputs found
Neuron as a reward-modulated combinatorial switch and a model of learning behavior
This paper proposes a neuronal circuitry layout and synaptic plasticity
principles that allow the (pyramidal) neuron to act as a "combinatorial
switch". Namely, the neuron learns to be more prone to generate spikes given
those combinations of firing input neurons for which a previous spiking of the
neuron had been followed by a positive global reward signal. The reward signal
may be mediated by certain modulatory hormones or neurotransmitters, e.g., the
dopamine. More generally, a trial-and-error learning paradigm is suggested in
which a global reward signal triggers long-term enhancement or weakening of a
neuron's spiking response to the preceding neuronal input firing pattern. Thus,
rewards provide a feedback pathway that informs neurons whether their spiking
was beneficial or detrimental for a particular input combination. The neuron's
ability to discern specific combinations of firing input neurons is achieved
through a random or predetermined spatial distribution of input synapses on
dendrites that creates synaptic clusters that represent various permutations of
input neurons. The corresponding dendritic segments, or the enclosed individual
spines, are capable of being particularly excited, due to local sigmoidal
thresholding involving voltage-gated channel conductances, if the segment's
excitatory and absence of inhibitory inputs are temporally coincident. Such
nonlinear excitation corresponds to a particular firing combination of input
neurons, and it is posited that the excitation strength encodes the
combinatorial memory and is regulated by long-term plasticity mechanisms. It is
also suggested that the spine calcium influx that may result from the
spatiotemporal synaptic input coincidence may cause the spine head actin
filaments to undergo mechanical (muscle-like) contraction, with the ensuing
cytoskeletal deformation transmitted to the axon initial segment where it
may...Comment: Version 5: added computer code in the ancillary files sectio
An Operating Principle of the Cerebral Cortex, and a Cellular Mechanism for Attentional Trial-and-Error Pattern Learning and Useful Classification Extraction
A feature of the brains of intelligent animals is the ability to learn to
respond to an ensemble of active neuronal inputs with a behaviorally
appropriate ensemble of active neuronal outputs. Previously, a hypothesis was
proposed on how this mechanism is implemented at the cellular level within the
neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate
"guess" neuron firings, while the basal dendrites identify input patterns based
on excited synaptic clusters, with the cluster excitation strength adjusted
based on reward feedback. This simple mechanism allows neurons to learn to
classify their inputs in a surprisingly intelligent manner. Here, we revise and
extend this hypothesis. We modify synaptic plasticity rules to align with
behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area
CA1, making the framework more biophysically and behaviorally plausible. The
neurons for the guess firings are selected in a voluntary manner via feedback
connections to apical tufts in the neocortical layer 1, leading to dendritic
Ca2+ spikes with burst firing, which are postulated to be neural correlates of
attentional, aware processing. Once learned, the neuronal input classification
is executed without voluntary or conscious control, enabling hierarchical
incremental learning of classifications that is effective in our inherently
classifiable world. In addition to voluntary, we propose that pyramidal neuron
burst firing can be involuntary, also initiated via apical tuft inputs, drawing
attention towards important cues such as novelty and noxious stimuli. We
classify the excitations of neocortical pyramidal neurons into four categories
based on their excitation pathway: attentional versus automatic and
voluntary/acquired versus involuntary. Additionally, we hypothesize that
dendrites within pyramidal neuron minicolumn bundles are coupled via
depolarization...Comment: 20 pages, 13 figure
An operating principle of the cerebral cortex, and a cellular mechanism for attentional trial-and-error pattern learning and useful classification extraction
A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate “guess” neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention toward important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization cross-induction, enabling minicolumn functions such as the creation of powerful hierarchical “hyperneurons” and the internal representation of the external world. We suggest building blocks to extend the microcircuit theory to network-level processing, which, interestingly, yields variants resembling the artificial neural networks currently in use. On a more speculative note, we conjecture that principles of intelligence in universes governed by certain types of physical laws might resemble ours
Data_Sheet_1_An operating principle of the cerebral cortex, and a cellular mechanism for attentional trial-and-error pattern learning and useful classification extraction.docx
A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate “guess” neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention toward important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization cross-induction, enabling minicolumn functions such as the creation of powerful hierarchical “hyperneurons” and the internal representation of the external world. We suggest building blocks to extend the microcircuit theory to network-level processing, which, interestingly, yields variants resembling the artificial neural networks currently in use. On a more speculative note, we conjecture that principles of intelligence in universes governed by certain types of physical laws might resemble ours.</p
Study of the quasielastic ³He(e,e'p) reaction at Q² = 1.5 (GeV/c)² up to missing momenta of 1 GeV/c
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2003.Includes bibliographical references (leaves 311-315).As a part of the E89044 experiment at Hall A of Jefferson Lab, we have studied the quasielastic ³He(e,e'p) reaction in perpendicular coplanar kinematics, with energy and momentum transfer by the electron fixed at 837 MeV and 1500 MeV/c respectively, at three beam energies of 1255, 1954 and 4807 MeV. ³He(e,e'p)D and ³He(e,e'p)pn cross sections and distorted spectral functions were measured up to missing momenta of 1000 MeV/c, and, for the three-body breakup channel, up to missing energy of 30 MeV. The A[sub]TL asymmetry, R[sub]T and R[sub]TL response functions, and the combination R[sub]L + R[sub]TT V[sub]TT/V[sub]L of response functions were separated for the ³He(e,e'p)D reaction channel up to missing momenta of 550 MeV/c. In the low missing momentum regime, measured ³He(e,e'p)D cross sections agree with available calculations based on variational ground state wave functions, and disagree with calculations based on Faddeev-type ground state wave functions. missing momenta from 100 to 740 MeV/c, strong final state interaction effects, in general consistent with Glauber-type and diagrammatic calculations, are observed. On a finer detail, meson exchange currents, isobaric currents and dynamical relativistic effects might be isolated with further theoretical work, in view of remaining disagreements between available calculations and the measurements. For missing momenta from 740 to 1000 MeV/c, measured ³He(e,e'p)D cross sections are more than an order of magnitude greater than predicted by available theories. Further theoretical work is needed for understanding the nature of processes in this region.by Marat M. Rvachev.Ph.D