Homeostatic plasticity studied using in vivo hippocampal activity-blockade: synaptic scaling, intrinsic plasticity and age-dependence
J Echegoyen, A Neu, KD Graber, I Soltesz - PloS one, 2007 - journals.plos.org
Homeostatic plasticity is thought to be important in preventing neuronal circuits from
becoming hyper-or hypoactive. However, there is little information concerning homeostatic …
becoming hyper-or hypoactive. However, there is little information concerning homeostatic …
Parameter space structure of continuous-time recurrent neural networks
RD Beer - Neural computation, 2006 - ieeexplore.ieee.org
A fundamental challenge for any general theory of neural circuits is how to characterize the
structure of the space of all possible circuits over a given model neuron. As a first step in this …
structure of the space of all possible circuits over a given model neuron. As a first step in this …
On the role of sensory feedbacks in Rowat–Selverston CPG to improve robot legged locomotion
E Amrollah, P Henaff - Frontiers in neurorobotics, 2010 - frontiersin.org
This paper presents the use of Rowat and Selverston-type of central pattern generator
(CPG) to control locomotion. It focuses on the role of afferent exteroceptive and …
(CPG) to control locomotion. It focuses on the role of afferent exteroceptive and …
Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness
Synaptic plasticity plays a critical role in the expression power of brain neural networks.
Among diverse plasticity rules, synaptic scaling presents indispensable effects on …
Among diverse plasticity rules, synaptic scaling presents indispensable effects on …
[LLIBRE][B] Unconventional information processing systems, novel hardware: A tour d'horizon
This report provides a wide-angle survey on computational paradigms which have a
possible bearing on the development of unconventional computational substrates and …
possible bearing on the development of unconventional computational substrates and …
Need is all you need: Homeostatic neural networks adapt to concept shift
In living organisms, homeostasis is the natural regulation of internal states aimed at
maintaining conditions compatible with life. Typical artificial systems are not equipped with …
maintaining conditions compatible with life. Typical artificial systems are not equipped with …
Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture
This paper describes experimental results regarding the real time implementation of
continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation …
continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation …
Towards modelling social habits: an organismically inspired evolutionary robotics approach
There has been a revival of the notion of habit in the embodied and situated cognitive
sciences. A habit can be understood as 'a self-sustaining pattern of sensorimotor …
sciences. A habit can be understood as 'a self-sustaining pattern of sensorimotor …
Monostable controllers for adaptive behaviour
Recent artificial neural networks for machine learning have exploited transient dynamics
around globally stable attractors, inspired by the properties of cortical microcolumns. Here …
around globally stable attractors, inspired by the properties of cortical microcolumns. Here …
Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons
H Toutounji, F Pasemann - Frontiers in neurorobotics, 2014 - frontiersin.org
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 …
specialized and highly recurrent neural networks. Learning and memory in these systems is …