The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs). Among these, the directional selective cells (DSRGC) are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs) framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion) and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system.
Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for more than 85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.
neural coding; synaptic transmission; retinal ganglion cell; information theory; receptive field center; macaque
Visual, auditory, somatosensory, and olfactory stimuli generate temporally precise patterns of action potentials (spikes). It is unclear, however, how the pattern and variability of synaptic input elicited by physiological stimuli governs the precision of spike generation. We determined how synaptic conductances evoked by light stimuli that activate the rod bipolar pathway control spike generation in three identified types of mouse retinal ganglion cells (RGCs). The relative amplitude, timing, and impact of excitatory and inhibitory input differed dramatically in On and Off RGCs. In each RGC type, however, the precision of light-evoked spikes was similar to that of spikes generated by somatic injection of measured light-evoked synaptic conductances that varied from trial to trial but less than that of spikes evoked by invariant synaptic conductances. Thus the rod bipolar pathway modulates different RGCs via unique combinations of synaptic input, and RGC temporal variability reflects variability in the input this circuit provides.
The interspike interval (ISI) preceding a retinal spike has a strong influence on whether retinal spikes will drive postsynaptic responses in the lateral geniculate nucleus (LGN). This ISI-based filtering of retinal spikes could, in principle, be used as a mechanism for processing visual information en route from retina to cortex; however, this form of processing has not been previously explored. Using a white noise stimulus and reverse correlation analysis, we compared the receptive fields associated with retinal spikes over a range of ISIs (0–120 ms). Results showed that, although the location and sign of retinal ganglion cell receptive fields are invariant to ISI, the size and amplitude of receptive fields vary with ISI. These results support the notion that ISI-based filtering of retinal spikes can serve as a mechanism for shaping receptive fields.
This study examines the rules governing the transfer of spikes between the retina and LGN with the goal of determining whether the most informative retinal spikes preferentially drive LGN responses and what role spike timing plays in the process. By recording from monosynaptically-connected pairs of retinal ganglion cells and LGN neurons in vivo in the cat, we show that relayed spikes are more likely than non-relayed spikes to be evoked by stimuli that match the recorded cells’ receptive fields and that an interspike interval (ISI)-based mechanism contributes to the process. Relayed spikes are also more reliable in their timing and number where they often achieve the theoretical limit of minimum variance. As a result, relayed spikes carry more visual information per spike. Based on these results, we conclude that retinogeniculate processing increases sparseness in the neural code by selectively relaying the highest fidelity spikes to the visual cortex.
coding; retina; LGN; retinal ganglion cell; receptive field; cat
Barlow studied summation in ganglion cell receptive fields and observed a fine discrimination of spatial information from which he inferred that retinal interneurons use analog signals to process images. Subsequent intracellular recordings confirmed that the interneurons of the outer retina, including photoreceptors, horizontal cells, and bipolar cells, respond to light with slow, graded changes in membrane potential. Analog processing may enable interneurons to discriminate fine gradations in light intensity and spatiotemporal pattern, but at the expense of the speed, temporal precision, and threshold discrimination that are characteristic of all-or-nothing Na+ spikes. We show that one type of mammalian On bipolar cell, the ground squirrel cb5b, has a large tetrodotoxin (TTX)-sensitive Na+ current. When recorded from in the perforated patch configuration, cb5b cells can signal the onset of a light step with 1–3 all-or-nothing action potentials that attain a peak amplitude of −10 to −20 mV (peak width at half-height equals 2 – 3 ms). When exposed to a continuous, temporally fluctuating stimulus, cb5b generate both graded and spiking responses. cb5b cells spike with millisecond precision, selecting for stimulus sequences in which transitions to light are preceded by a period of darkness. The axon terminals of cb5b bipolar cells co-stratify with the dendrites of amacrine and ganglion cells that encode light onset with a short latency burst of spikes. The results support the idea that a spiking On bipolar cell is part of a dedicated retinal pathway for rapidly and reliably signaling dark to light transitions.
Retina; Vision; Action Potential; Retinal bipolar cell; Sodium channel; Retinal ganglion cell
Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.
Spike timing precision is a fundamental aspect of neuronal information processing in the brain. Here we examined the temporal precision of input–output operation of dentate granule cells (DGCs) in an animal model of temporal lobe epilepsy (TLE). In TLE, mossy fibers sprout and establish recurrent synapses on DGCs that generate aberrant slow kainate receptor–mediated excitatory postsynaptic potentials (EPSPKA) not observed in controls. We report that, in contrast to time-locked spikes generated by EPSPAMPA in control DGCs, aberrant EPSPKA are associated with long-lasting plateaus and jittered spikes during single-spike mode firing. This is mediated by a selective voltage-dependent amplification of EPSPKA through persistent sodium current (INaP) activation. In control DGCs, a current injection of a waveform mimicking the slow shape of EPSPKA activates INaP and generates jittered spikes. Conversely in epileptic rats, blockade of EPSPKA or INaP restores the temporal precision of EPSP–spike coupling. Importantly, EPSPKA not only decrease spike timing precision at recurrent mossy fiber synapses but also at perforant path synapses during synaptic integration through INaP activation. We conclude that a selective interplay between aberrant EPSPKA and INaP severely alters the temporal precision of EPSP–spike coupling in DGCs of chronic epileptic rats.
dentate granule cells; INaP; kainate receptors; mossy fiber sprouting; spike timing; temporal lobe epilepsy
While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes. This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations but contribute only a fraction of their spikes to temporally precise spike configurations. This finding provides direct evidence for the hypothesized relation that precise spike synchrony constitutes a major temporally and spatially organized component of the LFP.
motor cortex; oscillation; population signals; synchrony
The manner in which information is encoded in neural signals is a major issue in Neuroscience. A common distinction is between rate codes, where information in neural responses is encoded as the number of spikes within a specified time frame (encoding window), and temporal codes, where the position of spikes within the encoding window carries some or all of the information about the stimulus. One test for the existence of a temporal code in neural responses is to add artificial time jitter to each spike in the response, and then assess whether or not information in the response has been degraded. If so, temporal encoding might be inferred, on the assumption that the jitter is small enough to alter the position, but not the number, of spikes within the encoding window. Here, the effects of artificial jitter on various spike train and information metrics were derived analytically, and this theory was validated using data from afferent neurons of the turtle vestibular and paddlefish electrosensory systems, and from model neurons. We demonstrate that the jitter procedure will degrade information content even when coding is known to be entirely by rate. For this and additional reasons, we conclude that the jitter procedure by itself is not sufficient to establish the presence of a temporal code.
Thalamic relay cells fire action potentials that transmit information from retina to cortex. The amount of information that spike trains encode is usually estimated from the precision of spike timing with respect to the stimulus. Sensory input, however, is only one factor that influences neural activity. For example, intrinsic dynamics, such as oscillations of networks of neurons, also modulate firing pattern. Here, we asked if retinal oscillations might help to convey information to neurons downstream. Specifically, we made whole-cell recordings from relay cells to reveal retinal inputs (EPSPs) and thalamic outputs (spikes) and then analyzed these events with information theory. Our results show that thalamic spike trains operate as two multiplexed channels. One channel, which occupies a low frequency band (<30 Hz), is encoded by average firing rate with respect to the stimulus and carries information about local changes in the visual field over time. The other operates in the gamma frequency band (40–80 Hz) and is encoded by spike timing relative to retinal oscillations. At times, the second channel conveyed even more information than the first. Because retinal oscillations involve extensive networks of ganglion cells, it is likely that the second channel transmits information about global features of the visual scene.
LGN; retina; visual coding; natural stimuli; oscillations
Here we review evidence that loss of photoreceptors due to degenerative retinal disease causes an increase in the rate of spontaneous ganglion spike discharge. Information about persistent spike activity is important since it is expected to add noise to the communication between the eye and the brain and thus impact the design and effective use of retinal prosthetics for restoring visual function in patients blinded by disease. Patch-clamp recordings from identified types of ON and OFF retinal ganglion cells in the adult (36–210 d old) rd1 mouse show that the ongoing oscillatory spike activity in both cell types is driven by strong rhythmic synaptic input from presynaptic neurons that is blocked by CNQX. The recurrent synaptic activity may arise in a negative feedback loop between a bipolar cell and an amacrine cell that exhibits resonant behavior and oscillations in membrane potential when the normal balance between excitation and inhibition is disrupted by the absence of photoreceptor input.
According to the ‘redundancy reduction’ hypothesis, a visual neuron removes correlations from an image to reduce redundancy in the spike train, thus increasing the efficiency of information coding. However, all elaborations of this general hypothesis have treated spatial and temporal correlations separately. To investigate how a retinal ganglion cell responds to combined spatial and temporal correlations, we selected those cells with center–surround receptive field and presented a stimulus with strong spatiotemporal correlations: we presented a random sequence of intensities (of white noise) to the receptive field center and then activated the surround with the same sequence. We found that, for most cells, activating the surround reduced temporal redundancy in the spike train. Although the surround often reduced the information rate of the spike train it always increased the amount of information per spike. However, when the surround was modulated by a different white-noise sequence than the center, eliminating spatial–temporal correlations, the surround no longer reduced redundancy or increased information per spike. The proposed mechanism for redundancy reduction is based on the temporal properties of the center and surround: the surround signal is delayed behind the center signal and subtracted from it; this implements a differentiator which removes low frequencies from the stimulus, thus reducing redundancy in the spike train. These results extend the redundancy reduction hypothesis by indicating that the spatial organization of the receptive field into center and surround can reduce temporal redundancy within the spike train of a ganglion cell.
center-surround; guinea pig; information; redundancy reduction; retina
Phase-locked spikes in various types of neurons encode temporal information. To quantify the degree of phase-locking, the metric called vector strength (VS) has been most widely used. Since VS is derived from spike timing information, error in measurement of spike occurrence should result in errors in VS calculation. In electrophysiological experiments, the timing of an action potential is detected with finite temporal precision, which is determined by the sampling frequency. In order to evaluate the effects of the sampling frequency on the measurement of VS, we derive theoretical upper and lower bounds of VS from spikes collected with finite sampling rates. We next estimate errors in VS assuming random sampling effects, and show that our theoretical calculation agrees with data from electrophysiological recordings in vivo. Our results provide a practical guide for choosing the appropriate sampling frequency in measuring VS.
vector strength; phase-locking; auditory brainstem; sound localization; temporal coding; circular statistics
The neural code that represents the world is transformed at each stage of a sensory pathway. These transformations enable downstream neurons to recode information they receive from earlier stages. Using the retinothalamic synapse as a model system, we developed a theoretical framework to identify stimulus features that are inherited, gained, or lost across stages. Specifically, we observed that thalamic spikes encode novel, emergent, temporal features not conveyed by single retinal spikes. Furthermore, we found that thalamic spikes are not only more informative than retinal ones, as expected, but also more independent. Next, we asked how thalamic spikes gain sensitivity to the emergent features. Explicitly, we found that the emergent features are encoded by retinal spike pairs and then recoded into independent thalamic spikes. Finally, we built a model of synaptic transmission that reproduced our observations. Thus, our results established a link between synaptic mechanisms and the recoding of sensory information.
Computational analyses have revealed that precisely timed spikes emitted by somatosensory cortical neuronal populations encode basic stimulus features in the rat's whisker sensory system. Efficient spike time based decoding schemes both for the spatial location of a stimulus and for the kinetic features of complex whisker movements have been defined. To date, these decoding schemes have been based upon spike times referenced to an external temporal frame – the time of the stimulus itself. Such schemes are limited by the requirement of precise knowledge of the stimulus time signal, and it is not clear whether stimulus times are known to rats making sensory judgments. Here, we first review studies of the information obtained from spike timing referenced to the stimulus time. Then we explore new methods for extracting spike train information independently of any external temporal reference frame. These proposed methods are based on the detection of stimulus-dependent differences in the firing time within a neuronal population. We apply them to a data set using single-whisker stimulation in anesthetized rats and find that stimulus site can be decoded based on the millisecond-range relative differences in spike times even without knowledge of stimulus time. If spike counts alone are measured over tens or hundreds of milliseconds rather than milliseconds, such decoders are much less effective. These results suggest that decoding schemes based on millisecond-precise spike times are likely to subserve robust and information-rich transmission of information in the somatosensory system.
information theory; somatosensation; neural coding; decoding; spike patterns; population coding
Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca2+. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners.
Synapse; Neuron; Release probability; Action potential and Neuronal encoding
Use of spike timing to encode information requires that neurons respond with high temporal precision and with high reliability. Fast fluctuating stimuli are known to result in highly reproducible spike times across trials, whereas constant stimuli result in variable spike times. Here, we have investigated how spike-time reliability depends on the time scale of fluctuations of the input stimuli in real neurons (mitral cells in the olfactory bulb and pyramidal cells in the neocortex) as well as in neuron models (integrate-and-fire and Hodgkin-Huxley) with intrinsic noise. In all cases we found that for firing frequencies in the beta/gamma range, spike reliability is maximal when the input includes fluctuations on the time scale of a few milliseconds (2-5 ms), coinciding with the time scale of fast synapses, and decreases substantially for faster and slower inputs. In addition, we show mathematically that the existence of an optimal time scale for spike-time reliability is a general feature of neurons. Finally, we comment how these findings relate to the mechanisms that cause neuronal synchronization.
Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies1–3, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses4,5. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding6. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.
The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a ‘quasi-renewal equation’ which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction.
How can information be encoded and decoded in populations of adapting neurons? A quantitative answer to this question requires a mathematical expression relating neuronal activity to the external stimulus, and, conversely, stimulus to neuronal activity. Although widely used equations and models exist for the special problem of relating external stimulus to the action potentials of a single neuron, the analogous problem of relating the external stimulus to the activity of a population has proven more difficult. There is a bothersome gap between the dynamics of single adapting neurons and the dynamics of populations. Moreover, if we ignore the single neurons and describe directly the population dynamics, we are faced with the ambiguity of the adapting neural code. The neural code of adapting populations is ambiguous because it is possible to observe a range of population activities in response to a given instantaneous input. Somehow the ambiguity is resolved by the knowledge of the population history, but how precisely? In this article we use approximation methods to provide mathematical expressions that describe the encoding and decoding of external stimuli in adapting populations. The theory presented here helps to bridge the gap between the dynamics of single neurons and that of populations.
In somatosensory cortex, stimulus amplitude is represented at a relatively coarse temporal resolution, while stimulus frequency is represented by precisely timed action potentials.
Our ability to perceive and discriminate textures relies on the transduction and processing of complex, high-frequency vibrations elicited in the fingertip as it is scanned across a surface. How naturalistic vibrations, and by extension texture, are encoded in the responses of neurons in primary somatosensory cortex (S1) is unknown. Combining single unit recordings in awake macaques and perceptual judgments obtained from human subjects, we show that vibratory amplitude is encoded in the strength of the response evoked in S1 neurons. In contrast, the frequency composition of the vibrations, up to 800 Hz, is not encoded in neuronal firing rates, but rather in the phase-locked responses of a subpopulation of neurons. Moreover, analysis of perceptual judgments suggests that spike timing not only conveys stimulus information but also shapes tactile perception. We conclude that information about the amplitude and frequency of natural vibrations is multiplexed at different time scales in S1, and encoded in the rate and temporal patterning of the response, respectively.
When we slide our fingertip across a textured surface, small, complex, and high-frequency vibrations are elicited in the skin and our nervous system extracts information about texture from these vibrations. In this study, we investigate how texture-like vibrations are processed in primary somatosensory cortex (S1). First, we show that the time-varying amplitude of skin vibrations is encoded in the time-varying response rates of a subpopulation of S1 neurons. Second, we show that this same subpopulation of S1 neurons produces responses whose timing closely matches that of the vibrations: The frequency composition of the spiking patterns matches that of the stimulus, even for complex vibrations. We demonstrate that this temporal precision is behaviorally relevant by showing that the tactile perception of vibration is better predicted from neuronal responses when spike timing is taken into consideration than when it is not. The activity of S1 neurons is thus multiplexed at different time scales: Stimulus amplitude, which changes relatively slowly, is represented at a relatively coarse temporal resolution, while stimulus frequency is represented by precisely timed action potentials.
A wide variety of neurons encode temporal information via phase-locked spikes. In the avian auditory brainstem, neurons in the cochlear nucleus magnocellularis (NM) send phase-locked synaptic inputs to coincidence detector neurons in the nucleus laminaris (NL) that mediate sound localization. Previous modeling studies suggested that converging phase-locked synaptic inputs may give rise to a periodic oscillation in the membrane potential of their target neuron. Recent physiological recordings in vivo revealed that owl NL neurons changed their spike rates almost linearly with the amplitude of this oscillatory potential. The oscillatory potential was termed the sound analog potential, because of its resemblance to the waveform of the stimulus tone. The amplitude of the sound analog potential recorded in NL varied systematically with the interaural time difference (ITD), which is one of the most important cues for sound localization. In order to investigate the mechanisms underlying ITD computation in the NM-NL circuit, we provide detailed theoretical descriptions of how phase-locked inputs form oscillating membrane potentials. We derive analytical expressions that relate presynaptic, synaptic, and postsynaptic factors to the signal and noise components of the oscillation in both the synaptic conductance and the membrane potential. Numerical simulations demonstrate the validity of the theoretical formulations for the entire frequency ranges tested (1–8 kHz) and potential effects of higher harmonics on NL neurons with low best frequencies (<2 kHz).
phase-locking; sound localization; auditory brainstem; periodic signals; oscillation; owl
The visual system is highly sensitive to dynamic features in the visual scene. However, it is not known how or where this enhanced sensitivity first occurs. We investigated this phenomenon by studying interactions between excitatory and inhibitory synapses in the second synaptic layer of the mouse retina. We found that these interactions showed activity-dependent changes that enhanced signaling of dynamic stimuli. Excitatory signaling from cone bipolar cells to ganglion cells exhibited strong synaptic depression, attributable to reduced glutamate release from bipolar cells. This depression was relieved by amacrine cell inhibitory feedback that activated presynaptic GABAC receptors. We found that the balance between excitation and feedback inhibition depended on stimulus frequency; at short interstimulus intervals excitation was enhanced, attributable to reduced inhibitory feedback. This dynamic interplay may enrich visual processing by enhancing retinal responses to closely spaced temporal events, representing rapid changes in the visual environment.
depression; gain control; dendro-dendritic interactions; presynaptic inhibition; sensory processing
An influential theory of visual processing asserts that retinal center-surround receptive fields remove spatial correlations in the visual world, producing ganglion cell spike trains that are less redundant than the corresponding image pixels. For bright, high-contrast images, this decorrelation would enhance coding efficiency in optic nerve fibers of limited capacity. Here we test the central prediction of the theory and demonstrate that the spike trains of retinal ganglion cells are indeed decorrelated compared to the visual input. However, most of the decorrelation is accomplished not by the receptive fields, but by nonlinear processing in the retina. We show that a steep response threshold enhances efficient coding by noisy spike trains, and the effect of this nonlinearity is near optimal in both salamander and macaque retina. These results offer an explanation for the sparseness of retinal spike trains, and highlight the importance of treating the full nonlinear character of neural codes.
Understanding how neural and behavioral timescales interact to influence cortical activity and stimulus coding is an important issue in sensory neuroscience. In air-breathing animals, voluntary changes in respiratory frequency alter the temporal patterning olfactory input. In the olfactory bulb, these behavioral timescales are reflected in the temporal properties of mitral/tufted (M/T) cell spike trains. As the odor information contained in these spike trains is relayed from the bulb to the cortex, interactions between presynaptic spike timing and short-term synaptic plasticity dictate how stimulus features are represented in cortical spike trains. Here we demonstrate how the timescales associated with respiratory frequency, spike timing and short-term synaptic plasticity interact to shape cortical responses. Specifically, we quantified the timescales of short-term synaptic facilitation and depression at excitatory synapses between bulbar M/T cells and cortical neurons in slices of mouse olfactory cortex. We then used these results to generate simulated M/T population synaptic currents that were injected into real cortical neurons. M/T population inputs were modulated at frequencies consistent with passive respiration or active sniffing. We show how the differential recruitment of short-term plasticity at breathing versus sniffing frequencies alters cortical spike responses. For inputs at sniffing frequencies, cortical neurons linearly encoded increases in presynaptic firing rates with increased phase locked, firing rates. In contrast, at passive breathing frequencies, cortical responses saturated with changes in presynaptic rate. Our results suggest that changes in respiratory behavior can gate the transfer of stimulus information between the olfactory bulb and cortex.