RESUMO
A majority of hearing defects are due to malfunction of the outer hair cells (OHCs), those cells within the mammalian hearing sensor (the cochlea) that provide an active amplification of the incoming signal. Malformation of the hearing sensor, ototoxic drugs, acoustical trauma, infections, or the effect of aging affect often a whole frequency interval, which leads to a substantial loss of speech intelligibility. Using an energy-based biophysical model of the passive cochlea, we obtain an explicit description of the dependence of the tonotopic map on the biophysical parameters of the cochlea. Our findings indicate the possibility that by suitable local modifications of the biophysical parameters by microsurgery, even very salient gaps of the tonotopic map could be bridged.
Assuntos
Vias Auditivas/fisiologia , Cóclea/fisiologia , Transtornos da Audição/fisiopatologia , Modelos Biológicos , Estimulação Acústica , Animais , Vias Auditivas/fisiopatologia , Percepção Auditiva/fisiologia , Limiar Auditivo/fisiologia , Cóclea/fisiopatologia , Transferência de Energia/fisiologia , Humanos , Transdução de SinaisRESUMO
Conventional approaches to detect patterns in neuronal firing are template based. As the pattern length increases, the number of trial patterns to be tested leads to strongly divergent computational costs. To remedy this problem, we propose a different statistical approach, based on the correlation integral. Applications of our method to model and neuronal data demonstrate its reliability, even in the presence of noise. Additionally, our investigation provides interesting insights into the nature of correlation-integral anomalies.
Assuntos
Potenciais de Ação/fisiologia , Inteligência Artificial , Modelos Neurológicos , Modelos Estatísticos , Neurônios/fisiologia , Reconhecimento Automatizado de Padrão , Transmissão Sináptica/fisiologia , Animais , Encéfalo/fisiologia , Gatos , Simulação por Computador , Armazenamento e Recuperação da Informação/métodos , Estatística como Assunto , Processos EstocásticosRESUMO
The recognition rate of holographic neural synapses, performing a pattern recognition task, is significantly higher when applied to natural, rather than artificial, images. This shortcoming of artificial images can be largely compensated for, if noise is added to the input pattern. The effect is the result of a trade-off between optimal representation of the stimulus (for which noise is favorable) and keeping as much as possible of the stimulus-specific information (for which noise is detrimental). The observed mechanism may play a prominent role for simple biological sensors.
Assuntos
Biofísica/métodos , Neurônios/fisiologia , Processos Estocásticos , Percepção Visual , Potenciais de Ação , Animais , Humanos , Modelos Biológicos , Modelos Neurológicos , Modelos Estatísticos , Modelos Teóricos , Condução Nervosa , Reconhecimento Psicológico , Limiar Sensorial , Transmissão Sináptica , Visão OcularRESUMO
We identify generic sources of complex and irregular spiking in biological neural networks. For the network description, we operate on a mathematically exact mesoscopic approach. Starting from experimental data, we determine exact properties of noise-driven, binary neuron interaction and extrapolate from there to properties of more complex types of interaction. Our approach fills a gap between approaches that start from detailed biophysically motivated simulations but fail to make mathematically exact global predictions, and approaches that are able to make exact statements but only on levels of description that are remote from biology. As a consequence of the approach, a novel coding scheme emerges, shedding new light on local information processing in biological neural networks.