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Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extract the most slowly varying features from a quickly varying signal. It has been successfully applied to the unsupervised learning of translation-, rotation-, ...
The tactile somatosensory pathway from whisker to cortex in rodents provides a well-defined system for exploring the link between molecular mechanisms, synaptic circuits, and behavior. The primary somatosensory cortex has an exquisite somatotopic map where ...
The neural cell adhesion molecule (NCAM) plays a pivotal role in neural development, regeneration, synaptic plasticity, and memory processes. P2 is a 12-amino-acid peptide derived from the second immunoglobulin-like (Ig) module of NCAM mediating cis-homoph ...
Neocortical pyramidal cells (PCs) project to various cortical and subcortical targets. In layer V, the population of thick tufted PCs (TTCs) projects to subcortical targets such as the tectum, brainstem, and spinal cord. Another population of layer V PCs p ...
The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The ...
The unique properties of single-wall carbon nanotubes (SWNTs) and the application of nanotechnology to the nervous system may have a tremendous impact in the future developments of microsystems for neural prosthetics as well as immediate benefits for basic ...
Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...
Viral vectors injected into the mouse brain offer the possibility for localized genetic modifications in a highly controlled manner. Lentivector injection into mouse neocortex transduces cells within a diameter of approximately 200mum, which closely matche ...
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or severa ...
We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algo ...