232 research outputs found
A new approach to the spatio-temporal pattern identification in neuronal multi-electrode registrations
A lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and onerous spatial pattern recognition task. 
Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. 
This geometrical transformation is completely invertible and allows to employ very fast processing algorithms. 

Synaptic Transmission: An Information-Theoretic Perspective
Here we analyze synaptic transmission from an information-theoretic
perspective. We derive closed-form expressions for the lower-bounds on the
capacity of a simple model of a cortical synapse under two explicit coding
paradigms. Under the ``signal estimation'' paradigm, we assume the signal to be
encoded in the mean firing rate of a Poisson neuron. The performance of an
optimal linear estimator of the signal then provides a lower bound on the
capacity for signal estimation. Under the ``signal detection'' paradigm, the
presence or absence of the signal has to be detected. Performance of the
optimal spike detector allows us to compute a lower bound on the capacity for
signal detection. We find that single synapses (for empirically measured
parameter values) transmit information poorly but significant improvement can
be achieved with a small amount of redundancy.Comment: 7 pages, 4 figures, NIPS97 proceedings: neuroscience. Originally
submitted to the neuro-sys archive which was never publicly announced (was
9809002
Comment on Ryder's SINBAD Neurosemantics: Is Teleofunction Isomorphism the Way to Understand Representations?
The merit of the SINBAD model is to provide an explicit mechanism showing how the cortex may come to develop detectors responding to correlated properties and therefore corresponding to the sources of these correlations. Here I argue that, contrary to the article, SINBAD neurosemantics does not need to rely on teleofunctions to solve the problem of misrepresentation. A number of difficulties for the teleofunction theories of content are reviewed and an alternative theory based on categorization performance and statistical relations is argued to provide a better account and to come closer to the practice in neuroscience and to powerful intuitions on swampkinds and on broad/narrow content
Evolving spiking neural networks for temporal pattern recognition in the presence of noise
Creative Commons - Attribution-NonCommercial-NoDerivs 3.0 United StatesNervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noiseFinal Published versio
- …