8,481 research outputs found
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks
A long-term goal of AI is to produce agents that can learn a diversity of
skills throughout their lifetimes and continuously improve those skills via
experience. A longstanding obstacle towards that goal is catastrophic
forgetting, which is when learning new information erases previously learned
information. Catastrophic forgetting occurs in artificial neural networks
(ANNs), which have fueled most recent advances in AI. A recent paper proposed
that catastrophic forgetting in ANNs can be reduced by promoting modularity,
which can limit forgetting by isolating task information to specific clusters
of nodes and connections (functional modules). While the prior work did show
that modular ANNs suffered less from catastrophic forgetting, it was not able
to produce ANNs that possessed task-specific functional modules, thereby
leaving the main theory regarding modularity and forgetting untested. We
introduce diffusion-based neuromodulation, which simulates the release of
diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up
or down regulate) learning in a spatial region. On the simple diagnostic
problem from the prior work, diffusion-based neuromodulation 1) induces
task-specific learning in groups of nodes and connections (task-specific
localized learning), which 2) produces functional modules for each subtask, and
3) yields higher performance by eliminating catastrophic forgetting. Overall,
our results suggest that diffusion-based neuromodulation promotes task-specific
localized learning and functional modularity, which can help solve the
challenging, but important problem of catastrophic forgetting
Toward a dynamical systems analysis of neuromodulation
This work presents some first steps toward a
more thorough understanding of the control systems
employed in evolutionary robotics. In order
to choose an appropriate architecture or to construct
an effective novel control system we need
insights into what makes control systems successful,
robust, evolvable, etc. Here we present analysis
intended to shed light on this type of question
as it applies to a novel class of artificial neural
networks that include a neuromodulatory mechanism:
GasNets.
We begin by instantiating a particular GasNet
subcircuit responsible for tuneable pattern generation
and thought to underpin the attractive
property of âtemporal adaptivityâ. Rather than
work within the GasNet formalism, we develop an
extension of the well-known FitzHugh-Nagumo
equations. The continuous nature of our model
allows us to conduct a thorough dynamical systems
analysis and to draw parallels between this
subcircuit and beating/bursting phenomena reported
in the neuroscience literature.
We then proceed to explore the effects of different
types of parameter modulation on the system
dynamics. We conclude that while there are
key differences between the gain modulation used
in the GasNet and alternative schemes (including
threshold modulation of more traditional synaptic
input), both approaches are able to produce
tuneable pattern generation. While it appears, at
least in this study, that the GasNetâs gain modulation
may not be crucial to pattern generation ,
we go on to suggest some possible advantages it
could confer
Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance
Neuromodulatory receptors in presynaptic position have the ability to
suppress synaptic transmission for seconds to minutes when fully engaged. This
effectively alters the synaptic strength of a connection. Much work on
neuromodulation has rested on the assumption that these effects are uniform at
every neuron. However, there is considerable evidence to suggest that
presynaptic regulation may be in effect synapse-specific. This would define a
second "weight modulation" matrix, which reflects presynaptic receptor efficacy
at a given site. Here we explore functional consequences of this hypothesis. By
analyzing and comparing the weight matrices of networks trained on different
aspects of a task, we identify the potential for a low complexity "modulation
matrix", which allows to switch between differently trained subtasks while
retaining general performance characteristics for the task. This means that a
given network can adapt itself to different task demands by regulating its
release of neuromodulators. Specifically, we suggest that (a) a network can
provide optimized responses for related classification tasks without the need
to train entirely separate networks and (b) a network can blend a "memory mode"
which aims at reproducing memorized patterns and a "novelty mode" which aims to
facilitate classification of new patterns. We relate this work to the known
effects of neuromodulators on brain-state dependent processing.Comment: 6 pages, 13 figure
Ageing, plasticity, and cognitive reserve in connectionist networks
Neurocomputational modeling has suggested that a range of
mechanisms can lead to reductions in functional plasticity
across development (Thomas & Johnson, 2006). In this paper,
we consider whether ageing might also produce a reduction in
plasticity. Marchmanâs (1993) model of damage and recovery
in past tense formation was extended to incorporate the two
main proposals for implementing effects of ageing: altered
neuromodulation and connection loss. Simulations showed
that ageing did reduce plasticity (as assessed by the systemâs ability to recover from damage) but that effects were modulated by (a) the mechanism used to implement ageing, (b) problem type, and (c) pre-existing levels of cognitive reserve
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Transcranial Focused Ultrasound to the Right Prefrontal Cortex Improves Mood and Alters Functional Connectivity in Humans
Transcranial focused ultrasound (tFUS) is an emerging method for non-invasive neuromodulation akin to transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). tFUS offers several advantages over electromagnetic methods including high spatial resolution and the ability to reach deep brain targets. Here we describe two experiments assessing whether tFUS could modulate mood in healthy human volunteers by targeting the right inferior frontal gyrus (rIFG), an area implicated in mood and emotional regulation. In a randomized, placebo-controlled, double-blind study, participants received 30 s of 500 kHz tFUS or a placebo control. Visual Analog Mood Scales (VAMS) assessed mood four times within an hour (baseline and three times after tFUS). Participants who received tFUS reported an overall increase in Global Affect (GA), an aggregate score from the VAMS scale, indicating a positive shift in mood. Experiment 2 examined resting-state functional (FC) connectivity using functional magnetic resonance imaging (fMRI) following 2 min of 500 kHz tFUS at the rIFG. As in Experiment 1, tFUS enhanced self-reported mood states and also decreased FC in resting state networks related to emotion and mood regulation. These results suggest that tFUS can be used to modulate mood and emotional regulation networks in the prefrontal cortex
Dopaminergic Regulation of Neuronal Circuits in Prefrontal Cortex
Neuromodulators, like dopamine, have considerable influence on the\ud
processing capabilities of neural networks. \ud
This has for instance been shown in the working memory functions\ud
of prefrontal cortex, which may be regulated by altering the\ud
dopamine level. Experimental work provides evidence on the biochemical\ud
and electrophysiological actions of dopamine receptors, but there are few \ud
theories concerning their significance for computational properties \ud
(ServanPrintzCohen90,Hasselmo94).\ud
We point to experimental data on neuromodulatory regulation of \ud
temporal properties of excitatory neurons and depolarization of inhibitory \ud
neurons, and suggest computational models employing these effects.\ud
Changes in membrane potential may be modelled by the firing threshold,\ud
and temporal properties by a parameterization of neuronal responsiveness \ud
according to the preceding spike interval.\ud
We apply these concepts to two examples using spiking neural networks.\ud
In the first case, there is a change in the input synchronization of\ud
neuronal groups, which leads to\ud
changes in the formation of synchronized neuronal ensembles.\ud
In the second case, the threshold\ud
of interneurons influences lateral inhibition, and the switch from a \ud
winner-take-all network to a parallel feedforward mode of processing.\ud
Both concepts are interesting for the modeling of cognitive functions and may\ud
have explanatory power for behavioral changes associated with dopamine \ud
regulation
State-Dependent and -Independent Effects of Dialyzing Excitatory Neuromodulator Receptor Antagonists into the Ventral Respiratory Column
Unilateral dialysis of the broad-spectrum muscarinic receptor antagonist atropine (50 mM) into the ventral respiratory column [(VRC) including the pre-BĂśtzinger complex region] of awake goats increased pulmonary ventilation (VĚi) and breathing frequency (f), conceivably due to local compensatory increases in serotonin (5-HT) and substance P (SP) measured in effluent mock cerebral spinal fluid (mCSF). In contrast, unilateral dialysis of a triple cocktail of antagonists to muscarinic (atropine; 5 mM), neurokinin-1, and 5-HT receptors does not alter VĚi or f, but increases local SP. Herein, we tested hypotheses that 1) local compensatory 5-HT and SP responses to 50 mM atropine dialyzed into the VRC of goats will not differ between anesthetized and awake states; and 2) bilateral dialysis of the triple cocktail of antagonists into the VRC of awake goats will not alter VĚi or f, but will increase local excitatory neuromodulators. Through microtubules implanted into the VRC of goats, probes were inserted to dialyze mCSF alone (time control), 50 mM atropine, or the triple cocktail of antagonists. We found 1) equivalent increases in local 5-HT and SP with 50 mM atropine dialysis during wakefulness compared with isoflurane anesthesia, but VĚi and f only increased while awake; and 2) dialyses of the triple cocktail of antagonists increased VĚi, f, 5-HT, and SP
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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