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Title: Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex}
Article:
Abstract:
The notion that neurons with higher selectivity carry more information about external sensory inputs is widely accepted in neuroscience. High-selectivity neurons respond to a narrow range of sensory inputs, and thus would be considered highly informative by rejecting a large proportion of possible inputs. In auditory cortex, neuronal responses are less selective immediately after the onset of a sound and then become highly selective in the following sustained response epoch. These 2 temporal response epochs have thus been interpreted to encode first the presence and then the content of a sound input. Contrary to predictions from that prevailing theory, however, we found that the neural population conveys similar information about sound input across the 2 epochs in spite of the neuronal selectivity differences. The amount of information encoded turns out to be almost completely dependent upon the total number of population spikes in the read-out window for this system. Moreover, inhomogeneous Poisson spiking behavior is sufficient to account for this property. These results imply a novel principle of sensory encoding that is potentially shared widely among multiple sensory systems.
Introduction:
Our current understanding of sensory information processing in the brain has been built heavily upon the observations of neurons that respond only to particular sensory inputs. One classical and common conception is that neurons with higher selectivity, i. e., responding to a narrower range of sensory input parameters, provide more information about sensory input[1–4]. In auditory cortex, selectivity of single neurons increases as spiking evolves following the onset of a stationary sound stimulus. Specifically, during the first tens to hundreds of milliseconds following stimulus onset, neurons tend to respond to a large range of sounds. Average spiking rates are high during this transient epoch and settle to lower sustained values during the later epoch. Differential adaptation leads to many fewer neurons active during the sustained response epoch, resulting in a sparser representation and, correspondingly, a neuron population of higher selectivity[5–8]. This feature of neuronal adaptation following stimulus onset is shared among multiple sensory systems[9–11]. Based on this selectivity difference, the current theory regarding neuronal responses in auditory cortex posits that the relatively unselective( i. e., dense) onset responses predominately encode the presence of sounds whereas the more selective( i. e., sparse) sustained responses predominately encode the identity of sounds[5, 12, 13]. The approach of interpreting informativeness based on neuronal response selectivity, although intuitively appealing, contains potential caveats upon closer consideration. For example, due to the limited size of individual neuronal receptive fields, stimulus information must be encoded collectively by multiple neurons[14, 15]. Therefore, a larger population of highly selective neurons would be required to encode an entire stimulus space compared to less selective neurons. Current theory predicts that the sustained response epoch of auditory cortical responses should carry more information on average than the onset response epoch because stimulus identification is a less constrained task than detection. Here, we tested this prediction by investigating the sound encoding dynamics of a population of 171 neurons in marmoset primary auditory cortex( A1). Our results contradicted current theory and implied a novel sensory encoding principle potentially applicable to other sensory systems.
Discussion:
We studied the population encoding properties of marmoset A1 neurons in response to a tonal stimulus set. We found that an equivalent amount of information about the tonal frequency of the stimuli was contained in intervals having the same spike count across both the onset and sustained response epochs. The Poisson nature of spike trains helps to explain this constant spike count encoding property. Higher information transfer rates could support more rapid information processing, while lower information transfer rates could reduce energy requirements through a lower spiking rate. Our constant spike count hypothesis postulates that simultaneous system constraints of( 1) the fundamental biophysical properties of how spikes are generated, manifested as Poisson-like spiking,( 2) the behavioral need for rapid stimulus identification at onset and less rapid readout as a stimulus continues unchanged, and( 3) the metabolic need for efficient stimulus encoding throughout the remainder of the stimulus interval, have collectively given rise through evolutionary processes to the spiking rate dynamics we observe. An advantage of this strategy is that it is simultaneously both fast and efficient. A less obvious advantage of such a sensory system is that encoding accuracy could be modulated for any decoder by altering the total number of spikes produced across the population for any stimulus. Fig 6D demonstrates that when simulating purely Poisson-spiking neurons responding to our 6 stimuli at a fixed observation window length, higher average discharge rates lead to higher decoding accuracy. A sensory system with excess rate capacity( by having many normally low-spiking neurons, for example) could therefore speed up decoding simply by adjusting all the neurons in the population to higher discharge rates, or possibly by recruiting more neurons to generate more spikes. The latter is exactly what happens during stimulus transitions, giving rise to strong onset responses. Given that we have firmly established accurate A1 encoding of tone frequency during the onset response epoch given a sufficient number of spikes, a logical question would be why sustained responses exist at all. The continuing representation of an ongoing stimulus might be necessary or at least convenient from a decoding perspective to signify the durations of ongoing stimuli. Sustained responses might also facilitate the detection of temporally overlapping novel stimuli[24, 34]. Our observation that onset responses contain stimulus-specific information is consistent with behavioral results indicating that sounds of extremely short durations( as short as 3 ms) are recognizable[35–39]. The relationship between tuning curve width and information encoding has been the focus of many theoretical discussions[40–43]. Our result is consistent with theoretical analysis showing that response selectivity and the quality of sensory information encoding does not have a definitive relationship[44]. It should be noted that due to the necessary acoustic transients at the beginning and end of each sound and in spite of the ramps applied to the sounds’ amplitude envelopes, the sound stimuli contain other frequency components in addition to the tonal frequencies at these transients. The particular effects of these other sound frequencies on neural activities are not explicitly considered in the current study. The much weaker energy of these frequencies and the observation that central auditory circuits exist to improve spectral resolution in the face of broad acoustic transients[45] potentially mitigate the effects of these other frequencies in the transients. To gain more insight into this question, one could potentially vary the duration of the tone onset ramps to study the effects of spectral splatter on neural encoding. Prior studies have shown that single neurons could provide temporally precise spiking code for the encoding of slope[46] and duration[47, 48] of onset ramps. It would also be important to determine whether the sound encoding strategies proposed in this study hold for more complex sounds such as speech that varies in temporal envelope. For example, different neural codes are shown to exhibit different correlations in the decoding performance relative to behavioral performance for consonant and vowel discriminations[49]. Another caveat of the current study is that neural activities were sequentially recorded and aggregated offline to compose the neural population responses instead of simultaneously measured for the entire neural population. Although similar methods have been widely used and shown effective for studies of neural population responses[11, 23–25, 29], sequential sampling does not provide information on the noise correlations of neuronal spiking activities, which might play an important role in information encoding in neural systems[15, 42, 50]. How noise correlations evolve over time and affect information encoding across the onset and sustained response epochs is an important topic for future investigations. An investigation of neuronal response correlations in primate visual cortex has revealed that rapid adaptation to the statistical properties of the preceding stimuli altered the noise correlation profile across the neural population to the subsequent stimuli and improved the coding efficiency for stimulus discrimination[51]. It remains to be seen whether similar effects exist for the adaptation occurring during the presentation of a single stimulus. It is possible that trial-by-trial simultaneous recordings that take into account noise correlation effects would alter the current results. The relationship we have described between neural activity amplitude and neural encoding reliability is consistent with the analysis of Poisson spiking behavior using a Bayesian framework[52, 53]. Because uniformly adaptive Poisson-like population spiking behavior is sufficient to give rise to the constant spike count encoding phenomenon, sensory systems may employ this strategy generally. Neural decoders appear to be able to take on a wide variety of forms and still extract equivalent stimulus-specific information for any constant spike count. It is worth pointing out that the Poisson rate code is one of the simplest neural codes. Other forms of neural coding using higher order correlations between neurons’ spiking activities, for example, might convey more information with fewer spikes.
Neurons act together to encode information such as the nature of a sensory stimulus . The number of neurons used for individual stimuli and the nature of the encoding used are not well understood . Higher sensory areas have been observed to respond sparsely to sensory stimuli , meaning that only a relatively small number of neurons fire action potentials ( or “spikes” ) when any particular stimulus is present . Sparse spiking activity is present in primary auditory cortex but only after a sound has been playing for some period of time . Dense spiking , however , is present at stimulus onset . We found that each action potential in primary auditory cortex contributed approximately the same amount of information about a tone stimulus , which resulted in more accurate encoding from the dense onset spiking . The later sparse spiking retained stimulus information but required a longer time to read it out . This arrangement allows sensory stimuli to be identified rapidly yet represented efficiently for extended periods , while neurons still retain sensitivity to novel stimuli . Dense and sparse coding may therefore work together dynamically in order to represent complex , temporally overlapping sensory content .