51,742 research outputs found
Functional connectivity modules in recurrent neural networks: function, origin and dynamics
Understanding the ubiquitous phenomenon of neural synchronization across
species and organizational levels is crucial for decoding brain function.
Despite its prevalence, the specific functional role, origin, and dynamical
implication of modular structures in correlation-based networks remains
ambiguous. Using recurrent neural networks trained on systems neuroscience
tasks, this study investigates these important characteristics of modularity in
correlation networks. We demonstrate that modules are functionally coherent
units that contribute to specialized information processing. We show that
modules form spontaneously from asymmetries in the sign and weight of
projections from the input layer to the recurrent layer. Moreover, we show that
modules define connections with similar roles in governing system behavior and
dynamics. Collectively, our findings clarify the function, formation, and
operational significance of functional connectivity modules, offering insights
into cortical function and laying the groundwork for further studies on brain
function, development, and dynamics
Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons
The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot. © 2014 Toutounji and Pasemann
Intrinsic adaptation in autonomous recurrent neural networks
A massively recurrent neural network responds on one side to input stimuli
and is autonomously active, on the other side, in the absence of sensory
inputs. Stimuli and information processing depends crucially on the qualia of
the autonomous-state dynamics of the ongoing neural activity. This default
neural activity may be dynamically structured in time and space, showing
regular, synchronized, bursting or chaotic activity patterns.
We study the influence of non-synaptic plasticity on the default dynamical
state of recurrent neural networks. The non-synaptic adaption considered acts
on intrinsic neural parameters, such as the threshold and the gain, and is
driven by the optimization of the information entropy. We observe, in the
presence of the intrinsic adaptation processes, three distinct and globally
attracting dynamical regimes, a regular synchronized, an overall chaotic and an
intermittent bursting regime. The intermittent bursting regime is characterized
by intervals of regular flows, which are quite insensitive to external stimuli,
interseeded by chaotic bursts which respond sensitively to input signals. We
discuss these finding in the context of self-organized information processing
and critical brain dynamics.Comment: 24 pages, 8 figure
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