The mammalian brain is able to recognize natural sounds in the presence of acoustic uncertainties such as background noise. A prevailing theory of neural coding suggest that neural systems are optimized for natural en...
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The mammalian brain is able to recognize natural sounds in the presence of acoustic uncertainties such as background noise. A prevailing theory of neural coding suggest that neural systems are optimized for natural environment signals and sensory inputs that are biologically relevant. The optimal coding hypothesis thus suggests that neural populations should encode sensory information so as to maximize efficient utilization of environmental inputs. In the first part of my thesis, I will explore the origins of scale invariance phenomena which has been previously described for natural sounds and has been observed in a variety of natural sensory signals including natural scenes. In the second part, I will explore the ability of the brain to utilize high-level statistical regularities in natural sounds to perform sound identification tasks. Using a catalog of natural sounds, texture synthesis procedures to manipulate sounds statistics from various sound categories, and neural recordings from the auditory midbrain of awake rabbits, I will show that neural population response statistics can be used to identify discrete sound categories. In the last part of the thesis, I will explore the role of hierarchical organization in the auditory pathway for sound recognition and optimal coding in the presence of challenging background noise. Using neural responses from auditory nerve, midbrain, and auditory cortex, I developed optimal computational neural network model for word recognition in presence of speech babble noise. I demonstrate that the optimal computational strategy for word recognition in noise predicts various transformations performed by the ascending auditory pathway, including a sequential loss of temporal and spectral resolution, increasing sparseness and selectivity.
The human capacity to scale the intensity of brief mechanical indentations of the hairy skin was measured by the method of subjective magnitude estimation. The afferent discharges evoked by nearly identical stimuli de...
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Researchers studying neural coding have speculated that populations of neurons would more effectively represent the stimulus if the neurons "cooperated:" by interacting through lateral connections, the neuro...
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Researchers studying neural coding have speculated that populations of neurons would more effectively represent the stimulus if the neurons "cooperated:" by interacting through lateral connections, the neurons would process and represent information better than if they functioned independently. We apply our new theory of information processing to determine the fidelity limits of simple population structures to encode stimulus features. We focus on noncooperative populations, which have no lateral connections. We show that they always exhibit positively correlated responses and that as population size increases, they perfectly represent the information conveyed by their inputs regardless of the individual neuron's coding scheme. Cooperative populations, which do have lateral connections, can, depending on the nature of the connections, perform better or worse than their noncooperative counterparts. We further show that common notions of synergy fail to capture the level of cooperation and to reflect the information processing properties of populations.
Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. neural coding transformations c...
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Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding Output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing;in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to g
The output of the cat retina is conveyed to the brain by the axons of similar to170,000 retinal ganglion cells that together constitute an optic nerve. Ganglion cells come in a number of varieties with the result that...
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ISBN:
(纸本)0780376129
The output of the cat retina is conveyed to the brain by the axons of similar to170,000 retinal ganglion cells that together constitute an optic nerve. Ganglion cells come in a number of varieties with the result that the message conveyed along the nerve to the brain is partitioned into a number of components. In this paper some key discoveries about neural coding in the cat retina are reported and used to illustrate how efficient the retina is at encoding visual information.
Debates on gustatory neural coding have been dominated by a small number of fundamental issues since the inception of the field in 1941. Three of these are discussed in this review: (1) are there basic tastes?(2) are ...
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Debates on gustatory neural coding have been dominated by a small number of fundamental issues since the inception of the field in 1941. Three of these are discussed in this review: (1) are there basic tastes?(2) are there gustatory neuron types?(3) is the code for a taste read simultaneously across all participating neurons (across-fiber patterning), or is it confined to a selective channel composed of cells of one type (labeled-line or channeling)?No conclusions are drawn regarding (1), primarily because a universal definition of‘basic tastes’is lacking. It is concluded that gustatory neuron types are likely to exist after reviewing the issue from multiple perspectives and discovering recurring indications of neuron types from several. A firm conclusion, however, also awaits a widely accepted definition of what constitutes a neuron type. Issue (3) cannot yet be resolved for lack of definitive data, specifically whether the discharges of inhibited, unresponsive, or weakly responsive cells add to (signal) or detract from (noise) the neural code for a tast
Developing animals must begin to interact with the world before their neural development is complete. This means they must build neural codes appropriate for turning sensory inputs into motor outputs adaptively as the...
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Developing animals must begin to interact with the world before their neural development is complete. This means they must build neural codes appropriate for turning sensory inputs into motor outputs adaptively as their neural hardware matures. We review some recent progress in the understanding of the relationship between neural coding and neural circuit development. We focus particularly on neural coding in the context of topographic maps and spontaneous activity, as well as receptive field and circuit development, drawing on examples from both mammalian visual cortex and fish optic tectum. Overall we suggest that neural coding strategies during development may be highly dynamic.
A ten-layer feed forward network was constructed in the presence of an exogenous alternating magnetic field. Results indicate that for rate coding, the firing rate is increased in the presence of an exogenous alternat...
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A ten-layer feed forward network was constructed in the presence of an exogenous alternating magnetic field. Results indicate that for rate coding, the firing rate is increased in the presence of an exogenous alternating magnetic field and particularly with increasing enhancement of the alternating magnetic field amplitude. For temporal coding, in the presence of alternating magnetic field, the interspike intervals of the spiking sequence are decreased and the distribution of interspike intervals tends to be uniform.
In this paper, we propose to generalize the notion of depth in temporal point process observations. The new depth is defined as a weighted product of two probability terms: (1) the number of events in each process, an...
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In this paper, we propose to generalize the notion of depth in temporal point process observations. The new depth is defined as a weighted product of two probability terms: (1) the number of events in each process, and (2) the center-outward ranking on the event times conditioned on the number of events. In this study, we adopt the Poisson distribution for the first term and the Mahalanobis depth for the second term. We propose an efficient boot-strapping approach to estimate parameters in the defined depth. In the case of Poisson process, the observed events are order statistics where the parameters can be estimated robustly with respect to sample size. We demonstrate the use of the new depth by ranking realizations from a Poisson process. We also test the new method in classification problems using simulations as well as real neural spike train data. It is found that the new framework provides more accurate and robust classifications as compared to commonly used likelihood methods.
Understanding how the brain processes sensory input to generate behavior remains an important problem in neuroscience. Towards this end, it is useful to compare results obtained across multiple species to gain underst...
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Understanding how the brain processes sensory input to generate behavior remains an important problem in neuroscience. Towards this end, it is useful to compare results obtained across multiple species to gain understanding as to the general principles of neural coding. Here we investigated hindbrain pyramidal cell activity in the weakly electric fish Apteronotus albifrons. We found strong heterogeneities when looking at baseline activity. Additionally, ON- and OFF-type cells responded to increases and decreases of sinusoidal and noise stimuli, respectively. While both cell types displayed band-pass tuning, OFF-type cells were more broadly tuned than their ON- type counterparts. The observed heterogeneities in baseline activity as well as the greater broadband tuning of OFF-type cells were both similar to those previously reported in other weakly electric fish species, suggesting that they constitute general features of sensory processing. However, we found that peak tuning occurred at frequencies similar to 15 Hz in A. albifrons, which is much lower than values reported in the closely related species Apteronotus leptorhynchus and the more distantly related species Eigenmannia virescens. In response to stimuli with time-varying amplitude (i.e., envelope), ON- and OFF-type cells displayed similar high-pass tuning curves characteristic of fractional differentiation and possibly indicate optimized coding. These tuning curves were qualitatively similar to those of pyramidal cells in the closely related species A. leptorhynchus. In conclusion, comparison between our and previous results reveals general and species-specific neural coding strategies. We hypothesize that differences in coding strategies, when observed, result from different stimulus distributions in the natural/social environment.
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