FOCUSED REVIEW published: 15 May 2010 doi: 10.3389/neuro.01.010.2010

Exploring the function of neural oscillations in early sensory systems

Kilian Koepsell1*, Xin Wang 2, Judith A. Hirsch 2 and Friedrich T. Sommer1*

1 Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA 2 Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA

Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems Edited by: S. Murray Sherman, as we ask how neural oscillations arise and how they might encode information about the University of Chicago, USA stimulus. We will highlight recent work in the early visual pathway that shows how oscillations Reviewed by: can multiplex different types of signals to increase the amount of information that spike trains Michael J. Friedlander, Baylor College of Medicine, USA encode and transmit. Last, we will describe oscillation-based models of visual processing and Jose-Manuel Alonso, Sociedad Espanola explore how they might guide further research. de Neurociencia, Spain; University of Connecticut, USA; State University Keywords: LGN, retina, visual coding, oscillations, multiplexing of New York, USA Naoum P. Issa, University of Chicago, USA Neural oscillations in early However, the autocorrelograms are subject to *Correspondence: sensory systems confounds caused by the refractory period and Oscillatory neural activity has been observed spectral peaks often fail to reveal weak rhythms. A in the early stages of virtually every sensory new method, the oscillation score (Muresan et al., system of animals, including insects, frogs 2008) reduces these problems. It combines analy- and primates. For example, periodic activ- ses in the time and frequency domains to indicate ity has been recorded from olfactory organs the strength of oscillations as one dimensionless Kilian Koepsell is Principal Investigator (Bressler and Freeman, 1980; Laurent, 2002) number. Also, oscillations shared by local groups at the Redwood Center for Theoretical as well as somatosensory (Ahissar and Vaadia, of cells can be detected in population responses, Neuroscience and at the Helen Wills 1990; Ahissar et al., 2000), visual (Laufer and such as the local field potential (LFP) or in pat- Neuroscience Institute at University Verzeano, 1967; Neuenschwander and Singer, terns of synaptic input. of California, Berkeley. He is working on functional models of information 1996; Arai et al., 2004) and auditory pathways processing in biological and artificial (Langner, 1992; Eguiluz et al., 2000; Roberts and Sources of oscillations neural networks and on statistical models Rutherford, 2008). The mechanisms that generate oscillations are of natural sensory stimuli and neural specific to sensory modality and brain region. activity. Kilian obtained his Ph.D. Detecting neural oscillations Oscillations can be inherited from the temporal in Physics at Hamburg University, Germany. He received postdoctoral The strength of neural oscillations can be structure of the stimulus, as in the auditory system training at the Max-Planck Institute assessed in the time and frequency domains. In (Figure 2A); originate from periodic movements for Gravitational Physics, Potsdam, the time domain, oscillations in spike trains are of mechanical sensors, as in the rodent’s whisker at King’s College, London, at the often characterized by means of periodic peaks system (Figure 2B); be generated or amplified by Redwood Neuroscience Institute, Menlo Park, and at University of California, in the autocorrelogram (Figures 1A,B); in the electrical resonances of individual cells, as in audi- Berkeley. frequency domain, they are defined on the basis tory hair cells (Figure 2C); or arise from intrinsic [email protected] of peaks in the power spectrum (Figures 1C,D). activity in recurrent neural networks, as in the

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Figure 1 | Detecting neural oscillations. (A,B) Oscillations in autocorrelations in two example recordings (spike trains recorded from LGN in cat) with oscillation score 10 and 29, respectively. (C,D) Oscillations in spectral power (same spike trains as used for panel A).

olfactory system (Figure 2D). At times, these spike trains forms the basis for the dual quality of mechanisms operate cooperatively to generate pitch perception (Licklider, 1951; Langner, 1992; oscillations. Patterson,2000).

Functional roles of oscillations Somatosensory system Many studies have explored functional roles Cyclical movements of the whiskers are reflected of neural oscillations. Work in sensory systems in neural oscillations in the rodent somatosensory suggests that oscillations might improve the system (Figure 2B). The neural oscillations form chance that neural activity propagates from a reference signal such that the spatial location of one stage to the next. For example, oscillations an obstacle is encoded in the phase at the time the might synchronize convergent inputs to a single whisker registers a contact (Ahissar et al., 2000; postsynaptic cell (Usrey et al., 1998; Bruno and Szwed et al., 2003). In addition, high frequency Sakmann, 2006) or drive downstream neurons oscillations have been proposed to encode the at their preferred input frequency (Nowak et al., textures of surfaces (Brecht, 2006). Thus, work *Correspondence: 1997; Hutcheon and Yarom, 2000; Fellous et al., from the somatosensory system suggests that 2001). Further, work in the auditory, somatosen- spatial structure in the stimulus is transformed sory, olfactory and visual systems suggest that to temporal structure in the spike train. oscillations carry specific types of information about the stimulus. Olfactory system Finally, in the olfactory system, neural oscillations Friedrich T. Sommer is Associate Adjunct Auditory system are internally generated by recurrent feedback Professor at the Redwood Center Neural oscillations in the auditory system can be among neurons in sensory organs (Figure 2D) for Theoretical Neuroscience and at the Helen Wills Neuroscience Institute directly induced by the stimulus (Figure 2A) and (Bressler and Freeman, 1980). Thus, this case is at University of California, Berkeley. are often amplified by mechanical and electrical different from the two previous examples, in which His research interests include models resonances (Figures 2B,C). The periodicity of the neural oscillations represent external variables. of memory and studies of the the spikes is phase-locked to the acoustic signal, Exploring the role of intrinsic versus extrinsic computation performed by networks and can be used to increase sensitivity to different dynamics poses unique difficulties, but nonetheless of sensory neurons. Dr. Sommer holds a Habilitation degree in Computer features in the stimulus and sharpen tuning to has proven key to understanding how some forms Science and Ph.D./Diploma degrees specific frequencies (Eguiluz et al., 2000; Roberts of sensory information are analyzed (Freeman and in Physics from the universities of Ulm, and Rutherford, 2008). Information about the Viana Di Prisco, 1986; Laurent, 2002). The olfac- Düsseldorf and Tübingen. He conducted phase of the acoustic signal is also essential tory bulb of zebra fish provides a good illustra- postdoctoral research at the Massachusetts Institute of Technology for spatial localization (Gerstner et al., 1996). tion. Information about ­complementary features and the University of Tübingen. Further, psychophysical studies suggest that of odors, identity and category, is encoded in the [email protected] the representation of acoustic phase in periodic

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Figure 2 | Origin of neural oscillations. (A) Oscillatory stimulus. (B) Oscillatory sensor movements (driven or resonance). (C) Oscillation from electrical resonance of individual cells. (D) Oscillatory intrinsic network activity due to recurrent network activity.

oscillating spike trains of single populations of ture in the PSTH reflects temporal changes in neurons (Friedrich et al., 2004). the features of the stimulus to which the cell responded (Figure 3A). Information in spike trains By applying Shannon’s information theory, it The discovery that the amount of force applied is possible to quantify how much information the Neural oscillations to the skin modulates the firing rate of periph- averaged spike rate (PSTH) conveys about the stim- Neural oscillations are rhythmic eral nerves provided great insight into neural ulus (MacKay and McCulloch, 1952; Grüsser et al., patterns of activity. They are present coding (Adrian and Zotterman, 1926). It led to 1962; Rieke et al., 1999; Brenner et al., 2000). The in the intracellular voltage, individual spike trains and/or in local field the realization that features of sensory stimuli, result of this analysis, the information rate I (spike potentials generated by populations like pressure, sound level or visual contrast are at t|stimulus), is usually given in bits per spike (or of synchronized cells. Oscillations encoded by instantaneous spike rate. Rate coding bits per second). In simple terms, the information are usually detected by means of sensory information is, perhaps, the most suc- rate corresponds to the mean number of yes/no of auto-correlations or spectrograms cessful paradigm for understanding how neurons questions one would have to ask in order to gather of the neural signal, e.g., Figure 1. convey information. the amount of information conveyed by each action Neural coding Neural coding refers to the process Techniques to estimate the amount of infor- potential, or each second of the spike train. by which one or more neurons encode mation transmitted by changes in spike rate information about sensory stimuli (MacKay and McCulloch, 1952; Grüsser et al., Information in neural oscillations or other quantities. Typically, studies 1962; Rieke et al., 1999; Brenner et al., 2000) are Now consider the case of an oscillating neuron of the neural code focus on discerning well established. But what if neural firing pat- in a sensory pathway. The spike rate encodes which aspects of the stimulus are represented, the means by which terns are shaped not only by stimulus-evoked the temporal structure of a stimulus and is also the information is encoded in spike changes in rate, but also by intrinsic or extrin- modulated by an oscillation that is not phase- patterns, and how much information sic oscillations? How might one separate and locked to the onset of the stimulus (Figure 3B). is conveyed, e.g., in bits per second, measure the contribution of each component Therefore, the spikes are phase-locked rather or bits per spike. to the transmission of neural information? than ­stimulus-locked. The PSTH, which reports Rate coding The next section discusses how to address this averaged stimulus-evoked changes in spike tim- Sensory information is often encoded by changes in spike rate (the mean question. ing, will not show evidence of the oscillation. number of spikes in a given time This is because the phase of the oscillation varies interval). Information in spike rate randomly with respect to the ­stimulus onset; the Peri-stimulus time histogram The influence of a stimulus on the spike rate of periodic component evident during single trials The peri-stimulus time histogram a sensory neuron is usually estimated by record- of the stimulus is lost in the average. Thus, the (PSTH) is a plot of spike rate during ing neural responses to multiple repeats of the information that the spikes carry about the oscil- the stimulus. The PSTH is usually same stimulus. Firing rate during repeats of latory signal cannot be estimated by the standard made by averaging responses to repeated presentations of the same the stimulus is then averaged to obtain a peri- methods. stimulus. stimulus time histogram (PSTH). Thus, struc-

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Figure 3 | Modeling spike trains of sensory neurons. (A) Conventional rate Sommer, 2008). (C) LGN spikes relative to oscillations detected in synaptic events coding: The spike train is modeled by a point process with an instantaneous rate originating from retinal ganglion cell spikes (Koepsell et al., 2009). (D) Phase that is modulated by the magnitude of the stimulus feature (Kuffler et al., 1957; distribution of LGN spikes. The peak indicates phase locking. The inset depicts Perkel et al., 1967). (B) Multiplicative model: The spike rate is modeled by a point how the degree of phase-locking is parametrized by the so-called concentration process that is modulated by the product of the value of the sensory feature and parameter κ of the circular Von Mises distribution. (E) The phase distribution of an independent oscillatory signal. Thus, the spike train contains information about LGN spikes relative to oscillation phase from previous trials (shift predictor) is flat, sensory stimulus and about oscillation phase (Berman, 1981; Koepsell and reflecting the fact that the oscillations are not locked to the stimulus.

We developed a new technique to estimate Conventional methods of estimating infor- the amount of information that oscillating spike mation in spike rates do not account for infor- trains transmit (Koepsell and Sommer, 2008). It mation encoded by oscillation phase, since uses an oscillatory reference signal that can be they depend solely on the PSTH (which only extracted from various sources, for example, reports changes in spike timing with respect to the simultaneously recorded LFP or presynaptic the stimulus). Our technique, the multicondi- inputs. The reference signal is used to assign each tional direct method, measures both the stimu- spike in the train at time t a phase φ(t) (Figure 3C). lus and oscillation based information (Koepsell If there is a fixed relationship between the phase and Sommer, 2008). It uses a two-dimensional of the oscillation and the generation of spikes, response histogram whose bins contain the the histogram of spike phases has a certain struc- average response for a given latency between ture. Specifically, the histogram has a single peak if the onset of the stimulus and a particular phase spikes are locked to a particular phase of the oscil- of the oscillation. The method yields estimates lation (Figure 3D). Similarly, the shift-­predictor of I (spike at t|stimulus,φ(t)), the information of the histogram is flat Figure( 3E) if the phase contained in a single spike about the stimulus Stimulus versus phase locking Changes in spike rate that reliably of oscillation varies relative to the onset of the and the oscillation. occur with a fixed latency following stimulus. Because the multiconditional direct method the stimulus are “stimulus-locked.” We illustrate this technique by analyzing an relies on a higher dimensional response histo- If changes in firing are stimulus-locked, oscillating spike train (Figures 3B–E) that car- gram than conventional PSTH-based methods, then the PSTH represents sensory ries information about two messages: the stimulus it requires substantial amounts of data. Such information encoded by spike rate. When neurons are more likely to fire feature that the spike rate encodes and the phase of large datasets are difficult to acquire in the during one phase of an ongoing the oscillation (Figures 3C,D). Further, changes laboratory. Thus, we developed an alternative (reference) oscillation than during in firing rate evoked by the stimulus versus those method to analyze small datasets, the phase others, then their behavior is induced by the oscillation occupy separate bands de-jittering method (Koepsell et al., 2009). This “phase-locked.” Phase-locking is visible in histograms of spike counts of the power spectrum of the spike train, so the method, in essence, uses the reference signal to as a function of the oscillation phase two messages do not interfere with each other. align, or de-jitter, the oscillation phase across (Figure 3). different repeats of the stimulus. This is done

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Information multiplexing by shifting spikes in time, but with displace- system (Friedrich et al., 2004) and has also been This term refers to the case of a single ments so small that the structure of the spike proposed in theoretical work for other sensory signal, such as spike train, that carries train at low frequencies is unaltered. After de- systems (Masquelier et al., 2009; Nadasdy, 2009; different messages, each encoded in separate information channels. jittering, the oscillation is retained in the PSTH Panzeri et al., 2010). If different channels are displaced and therefore it is possible to use conventional in the frequency or time domains, PSTH-based methods to estimate the informa- Multiplexing in the visual system they will not interfere with each other. tion. Comparisons of the full multiconditional Ongoing oscillations, those generated by the Frequency division multiplexing method and the de-­jittering method applied to a internal dynamics of the system, have been found This is a subtype of “information surrogate dataset, show that both yield compara- at all stages of visual processing, from the retina to multiplexing” in which the separate channels occupy different frequency ble results (Koepsell and Sommer, 2008). the cortex (Munk and Neuenschwander, 2000). In bands in the spectrum of the signal, the cortex, oscillations reflect visual information such as a spike train. Information can Multiplexing with periodic in several different ways. Information about the be encoded in the amplitude, phase or spike trains stimulus is conveyed by the synchrony between frequency modulation of a oscillatory In the previous section we explained how informa- two oscillating spike trains (Eckhorn et al., 1988; reference signal. tion multiplexing can occur in a single neuron, that Gray and Singer, 1989; Samonds et al., 2006), in Spike phase coding In this scheme, information is encoded is, the spike train conveys information about two the relative phase between spikes and oscillations by the timing of spikes relative to the different messages. We found that the two signals in the LFP (Montemurro et al., 2008; Kayser et phase of a reference oscillation. occupy separate frequency bands – a scheme called al., 2009) and in the oscillations themselves The phase of the oscillation, at which a frequency division multiplexing. Spike trains that (Kayser and Konig, 2004; Berens et al., 2008; given spike is fired, is the “spike-phase.” encode dual messages in this form are common in Mazzoni et al., 2008). In addition to endogenous Time division multiplexing sensory pathways. Usually, information encoded by rhythms, the cortex also seems to inherit oscil- This is a subtype of “information multiplexing” in which the separate spike rate occupies the lower part of the frequency lations that emerge at earlier stages of the vis- channels occupy alternating time spectrum. This position reflects the temporal struc- ual system (Neuenschwander and Singer, 1996; windows, or temporal segments, ture in the stimulus; the spectral power of natural Castelo-Branco et al., 1998). The oscillations are of the signal. The time windows signals decays with increasing temporal frequency observed in the absence (Doty et al., 1964; Heiss can be defined with respect to the (Ruderman and Bialek, 1994). By contrast, infor- and Bornschein, 1966) and the presence of anes- reference oscillation. mation encoded by the oscillations often resides in thetics [barbiturates (Heiss and Bornschein, 1965;

a separate high-frequency band, such as the gamma Laufer and Verzeano, 1967), N2O and halothane frequency range. For example, the frequency band (Neuenschwander and Singer, 1996; Castelo- of the stimulus to which auditory neurons phase- Branco et al., 1998), propofol (Koepsell et al., lock is usually higher than the acoustic modulation 2009)]. spectrum (Langner, 1992). We recently asked how oscillations in the retina might be used by the thalamus to transmit infor- Examples for multiplexing mation downstream. In particular, we asked how of sensory information the spike trains of a single thalamic relay cell can The transmission of oscillatory signals in spike transmit two separate streams of information, one trains can be used in various ways to convey encoded by firing rate and the other in oscillations sensory information. The frequency, phase or (Koepsell et al., 2009). We used the technique of coherence of the oscillatory signal can convey whole-cell recording in vivo, which allowed us to sensory information in itself, as in the audi- detect retinothalamic synaptic potentials and the tory system (Langner, 1992). The hippocampus action potentials they evoked from single relay provides an example of spike phase coding, in cells. In other words, we were able to reconstruct which the relative phases between spikes and the spike trains of the inputs and outputs of single an oscillatory signal carry sensory information. relay cells. Often, we found that both spike trains Specifically, the location of the animal can be had an oscillatory component. To explore whether resolved by decoding the relative phase between these oscillations were able to encode informa- spikes fired by a place cell and the activity that tion, we used the phase of the oscillation of the produces the theta rhythm (OKeefe and Recce, retinal inputs to de-jitter the timing of thalamic 1993). Further, oscillatory signals can enable spikes across repeated trials of the stimulus (see time division multiplexing within individ- Figures 1 and 3C–E). The result of the realign- ual spike trains. Here, the idea is that differ- ment was dramatic, as illustrated in Figure 4A. ent phases of the oscillation define temporal Although the oscillation was not visible in the windows in which particular features of the raw PSTH, it generated a pronounced modula- stimulus are selectively transmitted. Thus, the tion in the amplitude of the PSTH made from the rates measured in these different time windows de-jittered signal (Figure 4B). encode different types of sensory signals. Such a We then estimated the information in the de- coding scheme has been found in the olfactory jittered spike train. The results showed that most

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Figure 4 | Multiplexed information in the visual system. (A) Event times under the curve corresponds to a total information rate of 12.7 bit/s; the mean aligned to stimulus onset displayed as averaged spike rate (red curve) spike rate 29 spikes/s yields a value of 0.4 bit/spike. (D) Power spectrum and rasters for spikes (red), and EPSPs (blue) for 20 trials of a movie clip; spike (top) of de-jittered spike train decomposed into signal (solid line) and noise rasters were smoothed with a Gaussian window (σ = 2 ms) before averaging. (dashed line); spectral information rate (bottom). De-jittering increased the (B) Responses corrected for variation in latency ±10 ms by using periodicity total information from 0.4 bit/spike (C) to 1.2 bit/spike (Koepsell et al., 2009). in the ongoing activity that preceded stimulus onset; conventions as in (A). The movie stimulus was presented with 30 frames/s on a monitor with (C) Top, power spectrum of thalamic spike trains decomposed into signal a high refresh rate (150 Hz). The neural response did not lock to the frame (solid line) and noise (dashed line). Bottom, spectral information rate. The area update or monitor refresh.

relay cells that received periodic synaptic inputs One possibility we explore is the case in which transmitted a significant amount of information the oscillatory trend of the retinal cell does not con- in the gamma frequency band. For some cells, the tain information about the visual stimulus. Even amount of information in the oscillation-based in this situation, the oscillations might increase the (high frequency) channel was severalfold higher amount of information about local retinal features than that conveyed by rate-coded (low frequency) transmitted by the thalamic rate code. They would channel; compare Figure 4C with Figure 4D. do so by a process akin to amplitude modulation, in which information about the retinal feature is Potential new roles for oscillations reproduced in the frequency band of the oscilla- Gamma oscillations in retina and thalamus pro- tions. This redundant information could be read vide a novel channel that is able to convey infor- out and decoded in the cortex by various mecha- mation to the cortex. How might this channel nisms, such as coincidence detection of afferent contribute to visual function? In the following inputs or by the relative phase of the thalamic and we outline various hypotheses about the potential cortical oscillations. A specific role for the second roles for the new channel and how they might channel could be de-noising. Further, the amplitude be tested. modulation of the afferent spike train generates a signal that might enable cortical oscillations (e.g.,

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by adjusting relative phases of the two oscillations) ments. Thus, for the time being, the most prom- to route sensory information or to direct attention ising approach to studying the computational to a particular feature. (For discussion of potential roles of retinal oscillations in vision is to use roles of cortical oscillations in analyzing afferent computational models constrained by biologi- input, see Buzsaki and Draguhn, 2004; Sejnowski cal and psychophysical results. In fact, there are and Paulsen, 2006; Fries et al., 2007). many models of oscillatory neural networks that A second possibility is that retinal oscillations are able to transform spatial structure of visual are influenced by the stimulus, specifically, by input into temporal structure of neural activity. displacements of the retinal image caused by eye These models, which were originally developed movements. Thus, periodic activity in the retina to simulate cortical computations, are built with might encode spatial information in the tempo- phase-coupled oscillatory neurons (e.g., Baldi and ral domain, as in the whisker system (Ahissar and Meir, 1990; Sompolinsky et al., 1991; Sporns et al., Arieli, 2001; Rucci, 2008). This idea is supported, 1991; von der Malsburg and Buhmann, 1992; at least in part, by the strong similarity between the Schillen and Koenig, 1994; Wang and Terman, dominant frequency bands in the LFP recorded 1997; Ursino et al., 2006). It would be useful to from primary visual cortex and fixational eye develop such models to explore retinal and tha- movements [also note that oscillatory fixational lamic function. eye movements are found in species ranging from Other families of models combine psycho- turtle to humans (Greschner et al., 2002; Martinez- physical results with approaches used in com- Conde et al., 2004)]. Work that combines electro- puter vision to determine how various image physiology, measurements of eye-movements, and operations are able to reproduce visual percep- psychophysics should help to test this idea. tion and behavior. For example, recent work com- A third potential role for retinal oscillations pared how well segmentation algorithms (Pal and involves computational analysis of visual stimuli. Pal, 1993) matched the human ability to outline To explore this possibility, one must address two objects in images (Martin et al., 2004). State-of- questions at the same time. First, one must deter- the-art algorithms for edge detection and image mine which features of the stimulus are encoded segmentation approached human performance by the oscillations. To address this question, it by combining local with non-local features of the is helpful to recall that retinal oscillations are image; algorithms based solely on local contrast formed by distributed networks, and thus might were not successful. be sensitive to spatially extensive features and/or Motivated by this work, we implemented the context. Second, one must identify which par- normalized cut algorithm for the computation ticular attributes of the oscillations are used to of non-local features (Shi and Malik, 2000) in a encode information. Reasonable guesses include network of oscillating neurons (Figure 5). Our frequency, phase, relative phase, modulation of preliminary results indicate that, over time, coherence among cells, or combinations of these information about homogeneous image seg- parameters. The possible answers to these two ments is encoded by different oscillation phases questions are numerous, and this uncertainty (Figure 5B). Further, oscillatory spike trains are precludes the design of physiological experi- able to transmit this information as well as that

Figure 5 | Visual processing using coupled oscillators. with oscillation frequency ω, coupling function f(φj − φi) and local coupling

(A) Example image from the Berkeley segmentation database. constants cij that depend on similarity of pixel intensities. (C) Information (B) Phase representation in network of coupled phase oscillators φ(x) represented by neurons spiking at a specific oscillation phase, a few cycles evolving according to the dynamic equation after the stimulus onset.

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about local contrast. In addition, synchronized spikes a novel channel that conveys information from the in the ­oscillating network provide information about retina, via the thalamus, to the cortex. As well, we sur- edges (Figure 5C). These results suggest that features veyed current techniques that are used to quantify the like edge continuation, orientation, and border owner- amount of information that oscillatory spike trains ship – known to be represented by cortical firing rates encode. Further, we summarized potential functions – might already be available in the temporal structure of oscillation-based channels in the periphery that are of retinal activity. being actively explored by the community. Unlike the case for mammalian vision, in which the The work we have reviewed also bears on cortex function of oscillations remain subjects of debate, the (Eckhorn et al., 1988; Gray and Singer, 1989; Young behavioral role of gamma oscillations has been clearly et al., 1992; de Oliveira et al., 1997; Thiele and Stoner, established in the frog. Specifically, looming stimuli 2003), where oscillations are generated by two sources: designed to simulate shadows (cast by predators) sensory afferents and intracortical networks. That is, evoke synchronous oscillatory discharges in neural we not only discussed the types of sensory information “dimming detectors”. By contrast, small dark spots that that oscillations carry downstream, but also described mimic prey fail to induce such activity (Ishikane et al., theoretical frameworks that can be applied to diverse 1999). The consequence of the synchronous oscilla- cortical regions. tions among retinal dimming detectors is important for an animal’s survival – it triggers escape behavior Acknowledgments (Arai et al., 2004). Further strengthening the link We are grateful to Tony Bell, Bruno Olshausen and the between synchronous retinal activity and behavior, other members of the former Redwood Neuroscience pharmacological suppression of gamma oscillations Institute for helpful discussions. We thank Tim abolishes escape responses, but spares the slower mod- Blanche, Charles Cadieu, Jack Culpepper, and Christian ulation of spike rate evoked by small objects (Ishikane Wehrhahn for valuable comments on the manuscript. et al., 2005). Thus, in the frog, information about dif- Vishal Vaingankar, Yichun Wei, Qingbo Wang, Daniel ferent types of visual signals seems to be multiplexed Rathbun, and W. Martin Usrey contributed to the in different frequency bands of neural spike trains. experiments we described. This work was supported by NIH grant EY09593 (Judith A. Hirsch), NSF grant Summary IIS-0713657 (Friedrich T. Sommer), NSF grant IIS- This review focused on research that explores the func- 0917342 (Kilian Koepsell), The Swartz Foundation tional role of neural oscillations in the early stages of (Kilian Koepsell), and the Redwood Neuroscience sensory pathways. We described neural oscillations that Institute (Friedrich T. Sommer, Kilian Koepsell). The carry information about diverse sensory modalities, data analysis, simulations and figures were produced including olfaction, vision, audition and somatosensa- using IPython (Pérez and Granger, 2007), NumPy/ tion. In particular, we discussed gamma oscillations in SciPy (Oliphant, 2007), and Matplotlib (Barrett the early visual system, and showed how these form et al., 2005).

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