Optimal Population Coding, Revisited

Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information

Mapping Spikes to Sensations

Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research

Perceptual Learning of Fine Contrast Discrimination Under Non-roving, Roving-Without-Flanker, and Roving-with-Flanker Conditions and its Relation to Neuronal Activity in Macaque V1

\ua9 The Author(s) 2024.Perceptual learning refers to an improvement in perceptual abilities with training. Neural signatures of visual perceptual learning have been demonstrated mostly in mid- and high-level cortical areas, while changes in early sensory cortex were often more limited. We recorded continuously from multiple neuronal clusters in area V1 while macaque monkeys learned a fine contrast categorization task. Monkeys performed the contrast discrimination task initially when a constant-contrast sample stimulus was followed by a test stimulus of variable contrast, whereby they had to indicate whether the test was of lower or higher contrast than the sample. This was followed by sessions where we employed stimulus roving; i.e. the contrast of the sample stimulus varied from trial to trial. Finally, we trained animals, under ‘stimulus roving-with-flanker’ conditions, where the test stimuli to be discriminated were flanked by ‘flanking stimuli’. Perceptual discrimination abilities improved under non-roving conditions and under roving-with-flanker conditions as training progressed. Neuronal discrimination abilities improved with training mostly under non-roving conditions, but the effect was modest and limited to the most difficult contrast. Choice probabilities, quantifying how well neural activity is correlated with choice, equally increased with training during non-roving, but not during either of the roving conditions (with and without flankers). Noise correlations changed with training in both monkeys, but the changes were not consistent between monkeys. In one monkey, noise correlations decreased with training for non-roving and both roving conditions. In the other monkey, noise correlations changed for some conditions, but lacked a systematic pattern. Thus, while perceptual learning occurred under non-roving and roving-with-flanker conditions, the changes in neural activity in V1 were overall modest and were essentially absent under the different roving conditions

A sensory integration account for time perception

The connection between stimulus perception and time perception remains unknown. The present study combines human and rat psychophysics with sensory cortical neuronal firing to construct a computational model for the percept of elapsed time embedded within sense of touch. When subjects judged the duration of a vibration applied to the fingertip (human) or whiskers (rat), increasing stimulus intensity led to increasing perceived duration. Symmetrically, increasing vibration duration led to increasing perceived intensity. We modeled spike trains from vibrissal somatosensory cortex as input to dual leaky integrators \u2013 an intensity integrator with short time constant and a duration integrator with long time constant \u2013 generating neurometric functions that replicated the actual psychophysical functions of rats. Returning to human psychophysics, we then confirmed specific predictions of the dual leaky integrator model. This study offers a framework, based on sensory coding and subsequent accumulation of sensory drive, to account for how a feeling of the passage of time accompanies the tactile sensory experience

Direction discrimination thresholds of vestibular and cerebellar nuclei neurons

To understand the roles of the vestibular system in perceptual detection and discrimination of self-motion, it is critical to account for response variability in computing the sensitivity of vestibular neurons. Here we study responses of neurons with no eye movement sensitivity in the vestibular (VN) and rostral fastigial (FN) nuclei using high frequency (2 Hz) oscillatory translational motion stimuli. The axis of translation (i.e., heading) varied slowly (1°/s) in the horizontal plane as the animal was translated back and forth. Signal detection theory was used to compute the threshold sensitivity of VN/FN neurons for discriminating small variations in heading around all possible directions of translation. Across the population, minimum heading discrimination thresholds averaged 16.6° ±1° SE for FN neurons and 15.3°±2.2° SE for VN neurons, several-fold larger than perceptual thresholds for heading discrimination. In line with previous studies and theoretical predictions, maximum discriminability was observed for directions where firing rate changed steeply as a function of heading, which occurs at headings approximately perpendicular to the maximum response direction. Forward/backward heading thresholds tended to be lower than lateral motion thresholds, and the ratio of lateral over forward heading thresholds averaged 2.2±6.1 (geometric mean ± SD) for FN neurons and 1.1±4.4 for VN neurons. Our findings suggest that substantial pooling and/or selective decoding of vestibular signals from the vestibular and deep cerebellar nuclei may be important components of further processing. Such a characterization of neural sensitivity is critical for understanding how early stages of vestibular processing limit behavioral performance

Two-photon all-optical interrogation of mouse barrel cortex during sensory discrimination

The neocortex supports a rich repertoire of cognitive and behavioural functions, yet the rules, or neural ‘codes’, that determine how patterns of cortical activity drive perceptual processes remain enigmatic. Experimental neuroscientists study these codes through measuring and manipulating neuronal activity in awake behaving subjects, which allows links to be identified between patterns of neural activity and ongoing behaviour functions. In this thesis, I detail the application of novel optical techniques for simultaneously recording and manipulating neurons with cellular resolution to examine how tactile signals are processed in sparse neuronal ensembles in mouse somatosensory ‘barrel’ cortex. To do this, I designed a whisker-based perceptual decision-making task for head-fixed mice, that allows precise control over sensory input and interpretable readout of perceptual choice. Through several complementary experimental approaches, I show that task performance is exquisitely coupled to barrel cortical activity. Using two- photon calcium imaging to simultaneously record from populations of barrel cortex neurons, I demonstrate that different subpopulations of neurons in layer 2/3 (L2/3) show selectivity for contralateral and ipsilateral whisker input during behaviour. To directly test whether these stimulus-tuned groups of neurons differentially impact perceptual decision-making I performed patterned photostimulation experiments to selectively activate these functionally defined sets of neurons and assessed the resulting impact on behaviour and the local cortical network in layer 2/3. In contrast with the expected results, stimulation of sensory-coding neurons appeared to have little perceptual impact on task performance. However, activation of non- stimulus coding neurons did drive decision biases. These results challenge the conventional view that strongly sensory responsive neurons carry more perceptual weight than non-responsive sensory neurons during perceptual decision-making. Furthermore, patterned photostimulation revealed and imposed potent surround suppression in L2/3, which points to strong lateral inhibition playing a dominant role in shaping spatiotemporally sparse activity patterns. These results showcase the utility of combined patterned photostimulation methods and population calcium imaging for revealing and testing neural circuit function during sensorimotor behaviour and provide new perspectives on sensory coding in barrel cortex

Perception of tactile vibrations and a putative neuronal code

We devised a delayed comparison task, appropriate for human and rats, in which subjects discriminate between pairs of vibration delivered either to their whiskers, in rats, or fingertips, in humans, with a delay inserted between the two stimuli. Stimuli were composed of a random time series of velocity values (\u201cnoise\u201d) taken from a Gaussian distribution with 0 mean and standard deviation referred to as \u3c31 for the first stimulus and \u3c32 for the second stimulus. The subject must select a response depending on the two vibrations\u2019 relative standard deviations, \u3c31>\u3c32 or \u3c31<\u3c32. In the standard condition, the base and comparison stimuli both had duration of 400 ms and they were separated by a 800 ms pause. In this condition, humans had better performance than did rats on average, yet the best rats were better than the worst humans. To learn how signals are integrated over time, we varied the duration of the second stimulus. In rats, the performance was progressively improved when the comparison stimulus duration increased from 200 to 400 and then to 600 ms. In humans, the effect of comparison stimulus duration was different: an increase in duration did not improve their performance but biased their choice. Stimuli of longer duration were perceived as having a larger value of \u3c3. We employed a novel psychophysical reverse correlation method to find out which kinematic features of the stochastic stimulus influenced the choices of the subjects. This analysis revealed that rats rely principally on features related to velocity and speed values normalized by stimulus duration \u2013 that is, the rate of velocity and speed features per unit time. In contrast, while human subjects used velocity- and speed-related features, they tended to be influenced by the summated values of those features over time. The summation strategy in humans versus the rate strategy in rats accounts for both (i) the lack of improvement in humans for greater stimulus durations and (ii) the bias by which they judged longer stimuli as having a greater value of \u3c3. Next, we focused on the capacity of rats to accomplish a task of parametric working memory, a capacity until now not found in rodents. For delays between the base and comparison stimuli of up to 6-10 seconds, humans and rats showed similar performance. However when the difference in \u3c3 was small, the rats\u2019 performance began to decay over long inter-stimulus delays more markedly than did the humans\u2019 performance. The next chapter reports the analyses of the activity of barrel cortex neurons during the vibration comparison task. 35% of sampled neuron clusters showed a significant change in firing rate as \u3c3 varied, and the change was positive in every case \u2013 the slope of firing rate versus \u3c3 was positive. We used methods related to signal detection theory to estimate the behavioral performance that could be supported by single neuron clusters and found that the resulting \u201cneurometric\u201d curve was much less steep performance than the psychometric curve (the performance of the whole rat). This led to the notion that stimuli are encoded by larger populations. A general linear model (GLM) that combined multiple simultaneously recorded 2 clusters performed much better than single clusters and began to approach animal performance. We conclude that a potential code for the stimulus is the variation in firing rate according to \u3c3, distributed across large populations.In conclusion, this thesis characterizes the perceptual capacities of humans and rats in a novel working memory task. Both humans and rats can extract the statistical structure of a \u201cnoisy\u201d tactile vibration, but seem to integrate signals by different operations. A major finding is that rats are endowed with a capacity to hold stimulus parameters in working memory with a proficiency that, until now, could be ascribed only to primates. The statistical properties of the stimulus appear to be encoded by a distributed population

Fast Coding of Orientation in Primary Visual Cortex

Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple ‘race to threshold’ readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made