The effect of neural adaptation on population coding accuracy (original) (raw)
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The effect of neural adaptation of population coding accuracy
2011
Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accurac...
Adaptive coding of visual information in neural populations
Nature, 2008
Our perception of the environment relies on the capacity of neural networks to adapt rapidly to changes in incoming stimuli. It is increasingly being realized that the neural code is adaptive, that is, sensory neurons change their responses and selectivity in a dynamic manner to match the changes in input stimuli. Understanding how rapid exposure, or adaptation, to a stimulus of fixed structure changes information processing by cortical networks is essential for understanding the relationship between sensory coding and behaviour. Physiological investigations of adaptation have contributed greatly to our understanding of how individual sensory neurons change their responses to influence stimulus coding, yet whether and how adaptation affects information coding in neural populations is unknown. Here we examine how brief adaptation (on the timescale of visual fixation) influences the structure of interneuronal correlations and the accuracy of population coding in the macaque (Macaca mulatta) primary visual cortex (V1). We find that brief adaptation to a stimulus of fixed structure reorganizes the distribution of correlations across the entire network by selectively reducing their mean and variability. The post-adaptation changes in neuronal correlations are associated with specific, stimulus-dependent changes in the efficiency of the population code, and are consistent with changes in perceptual performance after adaptation. Our results have implications beyond the predictions of current theories of sensory coding, suggesting that brief adaptation improves the accuracy of population coding to optimize neuronal performance during natural viewing.
The role of adaptation in neural coding
Current Opinion in Neurobiology, 2019
The concept of 'neural coding' supposes that neural firing patterns in some sense represent some external correlate, whether sensory, motor, or structural knowledge about the world. While the implied existence of a one-to-one mapping between external referents and neural firing has been useful, the prevalence of adaptation challenges this. Adaptation provides neural responses with dynamics on timescales that range from milliseconds up to many seconds. These timescales are highly relevant for sensory experience in the natural world, in which local statistical properties of inputs change continuously, and are additionally altered by active sensing. Adaptation has a number of consequences for coding: it creates short-term history dependence; it engenders complex feature selectivity that is time-varying; and it can serve to enhance information representation in dynamic environments. Considering how to best incorporate adaptation into neural models exposes a fundamental dichotomy in approaches to the description of neural systems: ones that take an explicitly 'coding' perspective versus ones that describe the system's dynamics. Here we discuss the pros and cons of different approaches to the modeling of adaptive dynamics.
Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy
Cerebral Cortex, 2016
Information processing in the cerebral cortex depends not only on the nature of incoming stimuli, but also on the state of neuronal networks at the time of stimulation. That is, the same stimulus will be processed differently depending on the neuronal context in which it is received. A major factor that could influence neuronal context is the background, or ongoing neuronal activity before stimulation. In visual cortex, ongoing activity is known to play a critical role in the development of local circuits, yet whether it influences the coding of visual features in adult cortex is unclear. Here, we investigate whether and how the information encoded by individual neurons and populations in primary visual cortex (V1) depends on the ongoing activity before stimulus presentation. We report that when individual neurons are in a "low" prestimulus state, they have a higher capacity to discriminate stimulus features, such as orientation, despite their reduction in evoked responses. By measuring the distribution of prestimulus activity across a population of neurons, we found that network discrimination accuracy is improved in the low prestimulus state. Thus, the distribution of ongoing activity states across the network creates an "internal context" that dynamically filters incoming stimuli to modulate the accuracy of sensory coding. The modulation of stimulus coding by ongoing activity state is consistent with recurrent network models in which ongoing activity dynamically controls the balanced background excitation and inhibition to individual neurons.
PLoS Biology, 2007
Neuronal responses to ongoing stimulation in many systems change over time, or ''adapt.'' Despite the ubiquity of adaptation, its effects on the stimulus information carried by neurons are often unknown. Here we examine how adaptation affects sensory coding in barrel cortex. We used spike-triggered covariance analysis of single-neuron responses to continuous, rapidly varying vibrissa motion stimuli, recorded in anesthetized rats. Changes in stimulus statistics induced spike rate adaptation over hundreds of milliseconds. Vibrissa motion encoding changed with adaptation as follows. In every neuron that showed rate adaptation, the input-output tuning function scaled with the changes in stimulus distribution, allowing the neurons to maintain the quantity of information conveyed about stimulus features. A single neuron that did not show rate adaptation also lacked input-output rescaling and did not maintain information across changes in stimulus statistics. Therefore, in barrel cortex, rate adaptation occurs on a slow timescale relative to the features driving spikes and is associated with gain rescaling matched to the stimulus distribution. Our results suggest that adaptation enhances tactile representations in primary somatosensory cortex, where they could directly influence perceptual decisions. Citation: Maravall M, Petersen RS, Fairhall AL, Arabzadeh E, Diamond ME (2007) Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex. PLoS Biol 5(2): e19.
Frontiers in Computational Neuroscience, 2014
The primary visual cortex is an excellent model system for investigating how neuronal populations encode information, because of well-documented relationships between stimulus characteristics and neuronal activation patterns. We used two-photon calcium imaging data to relate the performance of different methods for studying population coding (population vectors, template matching, and Bayesian decoding algorithms) to their underlying assumptions. We show that the variability of neuronal responses may hamper the decoding of population activity, and that a normalization to correct for this variability may be of critical importance for correct decoding of population activity. Second, by comparing noise correlations and stimulus tuning we find that these properties have dissociated anatomical correlates, even though noise correlations have been previously hypothesized to reflect common synaptic input. We hypothesize that noise correlations arise from large non-specific increases in spiking activity acting on many weak synapses simultaneously, while neuronal stimulus response properties are dependent on more reliable connections. Finally, this paper provides practical guidelines for further research on population coding and shows that population coding cannot be approximated by a simple summation of inputs, but is heavily influenced by factors such as input reliability and noise correlation structure.
A stimulus-dependent spike threshold is an optimal neural coder
Frontiers in computational neuroscience, 2015
A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an interna...
Adaptive coding efficiency in recurrent cortical circuits via gain control
arXiv (Cornell University), 2023
Sensory systems across all modalities and species exhibit adaptation to continuously changing input statistics. Individual neurons have been shown to modulate their response gains so as to maximize information transmission in different stimulus contexts. Experimental measurements have revealed additional, nuanced sensory adaptation effects including changes in response maxima and minima, tuning curve repulsion from the adapter stimulus, and stimulus-driven response decorrelation. Existing explanations of these phenomena rely on changes in interneuronal synaptic efficacy, which, while more flexible, are unlikely to operate as rapidly or reversibly as single neuron gain modulations. Using published V1 population adaptation data, we show that propagation of single neuron gain changes in a recurrent network is sufficient to capture the entire set of observed adaptation effects. We propose a novel adaptive efficient coding objective with which single neuron gains are modulated, maximizing the fidelity of the stimulus representation while minimizing overall activity in the network. From this objective, we analytically derive a set of gains that optimize the trade-off between preserving information about the stimulus and conserving metabolic resources. Our model generalizes well-established concepts of single neuron adaptive gain control to recurrent populations, and parsimoniously explains experimental adaptation data. Preprint. Under review.