9 research outputs found
Analysing the information contributions and anatomical arrangement of neurons in population codes
Population coding—the transmission of information by the combined activity of
many neurons—is a feature of many neural systems. Identifying the role played
by individual neurons within a population code is vital for the understanding of
neural codes. In this thesis I examine which stimuli are best encoded by a given
neuron within a population and how this depends on the informational measure
used, on commonly-measured neuronal properties, and on the population size and
the spacing between stimuli. I also show how correlative measures of topography
can be used to test for significant topography in the anatomical arrangement of
arbitrary neuronal properties.
The neurons involved in a population code are generally clustered together in
one region of the brain, and moreover their response selectivity is often reflected
in their anatomical arrangement within that region. Although such topographic
maps are an often-encountered feature in the brains of many species, there
are no standard, objective procedures for quantifying topography. Topography
in neural maps is typically identified and described subjectively, but in cases
where the scale of the map is close to the resolution limit of the measurement
technique, identifying the presence of a topographic map can be a challenging
subjective task. In such cases, an objective statistical test for detecting topography
would be advantageous. To address these issues, I assess seven measures by
quantifying topography in simulated neural maps, and show that all but one of
these are effective at detecting statistically significant topography even in weakly
topographic maps.
The precision of the neural code is commonly investigated using two different
families of statistical measures: (i) Shannon mutual information and derived
quantities when investigating very small populations of neurons and (ii) Fisher
information when studying large populations. The Fisher information always
predicts that neurons convey most information about stimuli coinciding with the
steepest regions of the tuning curve, but it is known that information theoretic
measures can give very different predictions. Using a Monte Carlo approach
to compute a stimulus-specific decomposition of the mutual information (the
stimulus-specific information, or SSI) for populations up to hundreds of neurons
in size, I address the following questions: (i) Under what conditions can
Fisher information accurately predict the information transmitted by a neuron
within a population code? (ii) What are the effects of level of trial-to-trial
variability (noise), correlations in the noise, and population size on the best-encoded
stimulus? (iii) How does the type of task in a behavioural experiment (i.e.
fine and coarse discrimination, classification) affect the best-encoded stimulus? I
show that, for both unimodal and monotonic tuning curves, the shape of the SSI
is dependent upon trial-to-trial variability, population size and stimulus spacing,
in addition to the shape of the tuning curve. It is therefore important to take these
factors into account when assessing which stimuli a neuron is informative about;
just knowing the tuning curve may not be sufficient
Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations
The efficient coding hypothesis predicts that sensory neurons adjust their coding resources to optimally represent the stimulus statistics of their environment. To test this prediction in the moth olfactory system, we have developed a stimulation protocol that mimics the natural temporal structure within a turbulent pheromone plume. We report that responses of antennal olfactory receptor neurons to pheromone encounters follow the temporal fluctuations in such a way that the most frequent stimulus timescales are encoded with maximum accuracy. We also observe that the average coding precision of the neurons adjusted to the stimulus-timescale statistics at a given distance from the pheromone source is higher than if the same encoding model is applied at a shorter, non-matching, distance. Finally, the coding accuracy profile and the stimulus-timescale distribution are related in the manner predicted by the information theory for the many-to-one convergence scenario of the moth peripheral sensory system
Mutual information measure of visual perception based on noisy spiking neural networks
Note that images of low-illumination are weak aperiodic signals, while mutual information can be used as an effective measure for the shared information between the input stimulus and the output response of nonlinear systems, thus it is possible to develop novel visual perception algorithm based on the principle of aperiodic stochastic resonance within the frame of information theory. To confirm this, we reveal this phenomenon using the integrate-and-fire neural networks of neurons with noisy binary random signal as input first. And then, we propose an improved visual perception algorithm with the image mutual information as assessment index. The numerical experiences show that the target image can be picked up with more easiness by the maximal mutual information than by the minimum of natural image quality evaluation (NIQE), which is one of the most frequently used indexes. Moreover, the advantage of choosing quantile as spike threshold has also been confirmed. The improvement of this research should provide large convenience for potential applications including video tracking in environments of low illumination
Bayesian Computations in Noisy Spiking Neurons
The world is stochastic and chaotic, and organisms have access to limited information to take decisions. For this reason, brains are continuously required to deal with probability distributions, and experimental evidence confirms that they are dealing with these distributions optimally or close to optimally, according to the rules of Bayesian probability theory. Yet, a complete understanding of how these computations are implemented at the neural level is still missing. We assume that the “computational” goal of neurons is to perform Bayesian inference and to represent the state of the world efficiently. Starting from this assumption, we derive from first principles two distinct models of neural functioning, one in single neuron and one in neural populations, which explain known biophysics and molecular processes of neurons.
The models we propose suggest a new original interpretation for various neural quantities. Action potentials, which are usually considered the paramount form of communication between neurons, in our model of single neuron dynamics are reinterpreted as an internal communication channel. On the contrary, intracellular calcium concentration is interpreted as the most explicit representation of the external world inside the neuron. Specifically, we propose that calcium level represents the log-odds probability ratio of a particular hidden state in the world. Furthermore, we reinterpret synaptic vesicle release as a sampling process, which simulates the external world given all the available information. Finally, the neural population dynamics we propose interpret spontaneous neural activity as a process of sampling from the prior world statistics. This enables the system to implement a Markov Chain Monte Carlo algorithm that produces inference by sampling.
The proposed models generate various observable predictions, which match experimental results about synaptic vesicle release, short-term synaptic potentiation, ions channels open probability, intracellular calcium dynamics and propagation, spike rate adaptation and neural receptive fields