in line with previous studies recording from ventral striatum ( leung and yim , 1993 ; kasanetz et al . , 2002 ; berke et al . , 2004 ;
berke ( 2009 ) , van der meer and redish ( 2009b ) , and kalenscher et al .
however , they noted distinct power bands , with one centered at around 50  hz (  gamma-50  ) , and another , broader band from 70100 hz (  gamma-80  ) .
gamma oscillations in both bands tended to occur in  bursts  of 150250  ms ( figure 1a ) ; this pattern is consistent with power modulation by the phase of slower rhythms such as theta / alpha ( in dorsal striatum , tort et al . , 2008 ; hippocampus , colgin et al . , 2009 ; human ventral striatum , cohen et al . , 2009 ) .
oscillations in the two bands had a tendency to alternate spontaneously , rather than co - occur ( figure 1a ) , suggesting a degree of mutually exclusive coordination between the two .
berke ( 2009 ) showed further that switching between gamma-50 and gamma-80 was influenced by systemic application of dopaminergic drugs , shifting away from gamma-50 toward gamma-80 ( figure 2c ) .
as ventral striatum receives a strong dopaminergic projection from the midbrain ( mogenson et al .
, 1980 ; swanson , 1982 ) and synaptic and intrinsic membrane properties of ventral striatal neurons are modulated by dopamine ( da ) receptor activity ( bracci et al . , 2003 ; kreitzer and malenka , 2008 ) it is possible that this effect is mediated by processes in ventral striatum .
switching between ventral striatal gamma-50 and gamma-80 is also present in anesthetized animals ( sharott et al .
, 2009 ) and appears similar to gamma - band switching found in a number of other areas ( hippocampal ca1 , colgin et al . , 2009 ;
piriform cortex , kay et al . , 2009 ) , although the effects of da in those cases is not known . in any case
, the structure of ventral striatal gamma activity , with its distinct gamma-50 and gamma-80 power bands , provides a basis for investigating their behavioral and neural correlates separately .
hot colors indicate high power ; figure from van der meer and redish ( 2009b ) .
( b ) simultaneous recording from ventral striatum and piriform cortex ( pir ) shows that lfp gamma oscillations can be nearly identical in both .
figure adapted from berke ( 2005 , 2009 ; vmstr is ventromedial striatum , vlstr ventrolateral ) .
( c ) simultaneously recorded local field potential ( lfp ) traces from sites within ventral striatum suggest that gamma power can show heterogeneity between different recording sites .
( a ) gamma-50 ( left ) and gamma-80 power ( right ) were distributed differently over a track where rats ran for food reward ( provided at reward sites f1 and f2 ) .
plot shows average across four subjects ; because rats ran a lap - based task , data were linearized for display ( see figure 4 for task details ) .
( b ) example session in which gamma power differentiated between distinct reward sites ( with different appetitive rewards ) on a triangular track in kalenscher et al .
this particular lfp trace had almost no gamma power at the left reward site , but strong power at the bottom site .
( c ) systemic application of the dopaminergic agonist apomorphine ( at time 0 ; black arrow ) caused a switch from gamma-50 to gamma-80 .
example traces ( 1 ) and ( 2 ) were taken from the times indicated in the spectrogram , before and after drug administration respectively .
it is important to establish whether the recorded lfp oscillations are related to neural activity at the recording site , and not simply volume - conducted from nearby but distinct structures without any relationship to ventral striatal processing .
for instance , theta - band oscillations ( 610 hz ) generated in the hippocampus can be detected several millimeters from their generation site , with power decreasing only about 20% per millimeter depending on the direction of conduction ( sirota et al . , 2008 ) .
thus , berke ( 2009 ) , van der meer and redish ( 2009b ) , and kalenscher et al .
( 2010 ) asked whether the firing of single neurons in ventral striatum , recorded simultaneously with lfp oscillations , was related to gamma phase and power . indeed , both putative fast - spiking interneurons ( fsis ; about 90% , kalenscher et al .
, 2010 ; over 50% , van der meer and redish , 2009b ; about 15% , berke , 2009 ) , and msns ( about 3% ; kalenscher et al . , 2010 )
had significant phase relationships ( phase locking ) to the gamma lfp , as indicated by spike - triggered averages , phase distributions , and coherence analyses .
interestingly , many fsis cohered with either gamma-50 or gamma-80 , but not both , indicating that the different gamma bands are associated with distinct fsi populations ( figure 3 ) . while in vitro studies have shown that ventral striatal fsis show intrinsic resonance in the gamma range ( bracci et al . , 2003 ; taverna et al . , 2007 ) , those studies did not report fsi groups specifically tuned to gamma-50 or gamma-80 ; it is possible that the observed fsi subclasses result from differences in the source of their inputs , or different responses to neuromodulators .
the observation that fsis were preferentially entrained to gamma oscillations is consistent with their integral role in the generation of gamma oscillations in other brain regions , as assessed by dynamic , in vivo control using optogenetic tools ( fuchs et al . , 2007
2009 ) , though usually as part of recurrent local excitatory  inhibitory loops ( bartos et al . , 2007 ; fries et al . , 2007 )
striatal fsis can show task - modulated entrainment to gamma oscillations even without changes in firing rate ( berke , 2009 ) , part of a set of recent findings suggesting that fsi spike timing , and fsi spike rate , may be controlled by distinct aspects of their connectivity ( berke , 2008 ; gage et al . , 2010 ;
thus , not only do such spiking relationships indicate that gamma oscillations are relevant to local processing , they also provide novel insights into the organization of ventral striatal circuitry .
neurons in ventral striatum show firing relationships to gamma band local field potentials . ( a ) putative fast - spiking interneurons ( fsis ) tended to show strong relationships to either gamma-50 or gamma-80 .
shown is the average spike waveform ( left ) , spike - triggered lfp average ( middle ) and spike - field coherence ( right ) of two example putative fsis in berke ( 2009 ) .
note how the top neuron is coherent with gamma-50 but not gamma-80 , while the bottom neuron is more coherent at gamma-80 ( arrows ) .
van der meer and redish ( 2009b ) similarly examined the coherence of a population of putative fsis ( b ) finding subgroups of gamma-50 and gamma-80 coherent neurons .
plot shows the z - scored difference of the observed spike - field coherence relative to a set of interspike - interval - shuffled controls ; high z - scores ( hot colors ) indicate high coherence .
note the two distinct populations at the top ( gamma-80 ) and bottom ( gamma-50 ; arrows ) .
( c ) phase histograms for two example putative medium - sized spiny neurons ( msns ) in kalenscher et al .
( 2010 ) , showing a tendency to fire at a particular phase of the gamma cycle .
schematic representation of the tasks used ( top ) in berke ( 2009 , left ) , van der meer and redish ( 2009b , middle ) , and kalenscher et al .
( 2010 , right ) , and summary of the main findings in each ( bottom ) . in each study , rats ran to obtain appetitive rewards at reward sites ( filled circles ) .
in berke ( 2009 ) the rewarded arm was indicated by cue lights visible from the central choice point . in van der meer and redish ( 2009b )
only one side of the maze was rewarded , but the rewarded side was changed between recording sessions . in kalenscher et al .
note that , in the summary of the findings presented here ( as in the main text ) , a distinction is made between  reward delivery 
, which is assessed by comparing rewarded and unrewarded trials , and  reward sites  which may include components ( such as an anticipatory ramp ) independent of actual reward delivery .
ecog , electrocorticogram ; da , dopamine ; fsi , fast - spiking interneuron ; msn , medium - sized spiny neuron .
all three studies ( berke , 2009 ; van der meer and redish , 2009b ; kalenscher et al . ,
2010 ) found changes in gamma oscillations associated with reward sites and delivery . in one manifestation of such reward relationships ,
van der meer and redish ( 2009b ) found strikingly different distributions of gamma-50 and gamma-80 power across the running track , with gamma-50 increasing abruptly at the reward sites before trailing off gradually , whereas gamma-80 ramped up gradually to the reward sites ( figure 2a ) . zooming in on the time of reward delivery ,
gamma-80 power showed a complex pattern , ramping up gradually before falling sharply at the time of reward receipt , but then rising again before returning to baseline ( note that this refers to fast - timescale changes beyond those seen in figure 2a ; see figure 9 in van der meer and redish 2009b ) .
interestingly , the initial ramping effect was independent of whether reward was actually received or not , while the post - arrival increase was only seen when reward was actually delivered . in apparent contrast , berke ( 2009 ) found that gamma-50 power was virtually abolished following reward receipt , while gamma-80 power showed a transient increase .
thus , both berke ( 2009 ) and van der meer and redish ( 2009b ) found an increase in gamma-80 specific to reward delivery , but in van der meer and redish ( 2009b ) gamma-80 additionally ramped up to the reward sites , reminiscent of firing patterns found in ventral striatal neurons , which have been widely shown to anticipate reward delivery ( schultz et al .
, 1992 ; lavoie and mizumori , 1994 ; miyazaki et al . , 1998 ;
, 2008 ; van der meer and redish , 2009a ) . in further contrast to berke ( 2009 ) ,
van der meer and redish ( 2009b ) reported that gamma-50 power , like gamma-80 , showed a multiphasic relationship to reward delivery , increasing abruptly immediately following reward receipt , before decreasing and then rebounding , persisting well past initiation of the following lap .
the more immediate post - arrival increase in gamma-50 power was dependent on actual reward delivery , while the later one was not .
( 2010 ) similarly found that gamma-50 power tended to be higher on rewarded ( compared to unrewarded ) trials , and in line with van der meer and redish ( 2009b ) , that gamma-80 power was increased prior to reward delivery . in these analyses , it should be noted that behavioral differences ( such as grooming , consumption , frustration , initiation of movement to the next reward site ) could be different between rewarded and non - rewarded trials .
however , in van der meer and redish ( 2009b ) the early components of the gamma-50 and gamma-80 differentiated between rewarded and non - rewarded trials even though movement speeds were similar .
( 2010 ) showed that especially gamma-50 ( and to a lesser degree gamma-80 power ) differentiated between visits to different reward sites with distinct appetitive rewards ( figure 2b ) .
since behavioral differences such as grooming and consumption are not site - specific and should hence influence gamma power at all reward locations equally , differential effects of site on gamma power suggest that gamma power modulation is at least in part related to reward - specific processing . thus , while the three studies differ in the precise pattern of gamma-50 and gamma-80 power changes relative to reward , taken together they support the notion that ventral striatal gamma oscillations are affected by reward anticipation and receipt . beyond relationships to the reward sites and reward delivery , van der meer and redish ( 2009b ) noted a number of further differences in the task- and behavioral correlates of gamma-50 and gamma-80 .
gamma-50 and gamma-80 differed in their development as animals learned to choose the correct maze arm , with gamma-80 power being highest early in a session , while gamma-50 power was almost absent early and developed later ( within the first 5  min or 10 laps , even when rats were already very familiar with the environment ) .
gamma-80 power was increased specifically at the maze 's choice point early in sessions , similar to the time course and location of both forward representations (  sweeps  ) in the hippocampus , and covert activation of ventral striatal reward - responsive neurons ( johnson and redish , 2007 ; van der meer and redish , 2009a , 2010 ) .
van der meer and redish ( 2009b ) also found , consistent with an earlier report ( masimore et al .
, 2005 ) , that gamma-50 but not gamma-80 power was transiently increased before movement initiation ( starting to run from having been previously stationary ) , even when they only considered movements away from the reward sites to exclude any potential contribution of reward delivery . in apparent contrast , kalenscher et al .
( 2010 ) found only very weak correlations between gamma power and movement parameters , even when the analysis was restricted to test the effect of positive acceleration on gamma power .
however , in addition to the fact that these diverging results may be attributed to differences in the analyses used , this set of observations may not in fact be incompatible , but rather result from gamma-50 power being increased before  but not at  the time of , positive acceleration accompanying movement initiation . in sum
, it appears that the full complement of task- and behavioral correlates of ventral striatal gamma - band activity , which includes not only the complex multiphasic pattern at reward sites , but also movement and other correlates , is not easily captured by a unitary explanation  whether in the case of gamma-50 or gamma-80 .
this observation raises the question to what extent these different correlates are generated by the same neural process or circuit , or whether they are generated by different ( topographically segregated ) circuits , but mixed together by volume conduction .
for instance , in the case of gamma-80 ( in van der meer and redish 2009b at least ) , is the ramping up to reward ( which is independent of reward delivery ) and the transient response to actual reward delivery , linked at the neural level within ventral striatum ? or , alternatively , are there separate circuits , one generating the ramp and another generating the transient reward response , which merely appear linked because both components appear in lfps that reflect a wide area ?
as discussed above , several lines of evidence suggest that at least part of ventral striatal gamma activity is locally generated .
these include relationships between spiking activity and gamma oscillation phase , reports of intrinsic membrane oscillations in the gamma range in striatal fsis , and the role of similar interneurons in generating gamma oscillations in other structures .
these could include synaptic inputs from afferent areas , such as hippocampus , amygdala , prefrontal cortex and thalamus ( which are known to exhibit gamma oscillations : e.g. , colgin et al .
2009 ) , synaptic currents resulting from local projections within the striatum ( both fsis and msns make local connections ) , local intrinsic membrane potentials , and multi - unit spiking activity .
these possibilities thus raise the question : how much of ventral striatal gamma is due to structure already present in its inputs , and what ( if any ) is the contribution of local ventral striatal processing ? to address this , berke ( 2009 ) recorded ventral striatal lfps simultaneously with piriform and frontal cortical ( skull screw electrocorticogram ) lfps , finding that ventral striatal gamma-50 appeared similar to piriform gamma ( figure 1b ) . while piriform gamma oscillations can include both gamma-50 and a higher frequency , 70 hz component ( kay et al . , 2009 )
, berke ( 2009 ) found that gamma-80 in ventral striatum could be coherent with frontal cortical gamma-80 .
these findings suggest that gamma-50 recorded from rat ventral striatum contains a contribution from piriform gamma ; however , because ventral striatal neuronal firing can cohere with gamma-50 ( figure 3 ) , this is unlikely to be due to volume conduction alone .
ventral striatal gamma-80 may reflect increased inputs from frontal cortical areas , again relevant to processing within ventral striatum as indicated by fsi firing relationships .
the above results suggest a possible conception of ventral striatal gamma as resulting from synaptic currents from excitatory afferents that already possess oscillatory structure : the major sources of afferent input into ventral striatum ( amygdala , hippocampal formation , prefrontal cortex , piriform cortex , thalamus ) are all known to contain gamma oscillations ( chrobak and buzski , 1998 ; lee et al .
, 2003 ; colgin et al . , 2009 ; kay et al . , 2009 ) , .
in this scenario , ventral striatal gamma oscillations simply result from synaptic currents generated by afferent inputs to fsis and msns . on the one hand
, this would be consistent with the observation that ventral striatum lacks the recurrent inhibitory 
excitatory loops thought to be responsible for de novo generation ( that is , not present in inputs ) of gamma oscillations ( bartos et al .
, 2007 ; fries et al . , 2007 ; tiesinga and sejnowski , 2009 ) and central to computational models of gamma synchronization ( e.g. , lytton and sejnowski , 1991 ; vreeswijk et al .
on the other hand , in such a scenario it is not clear how coordinated switching between gamma-50 and gamma-80 ( as in figure 1a ) can arise from distinct inputs ; these would already have to be coordinated in order to generate a consistent switching pattern .
an alternative scenario is that the intrinsic membrane properties of fsis allow them to convert excitatory inputs without oscillatory structure into local gamma frequency inhibition . in particular , the subthreshold gamma oscillations observed in fsis in response to non - oscillatory depolarization in vitro ( bracci et al . , 2003 ;
may contribute to lfp gamma oscillations directly , but also generate rhythmic , potentially widespread inhibition of msns , even for non - oscillatory input barrages . while fsis are thought to be relatively rare in ventral striatum , and may not act in a coordinated manner ( berke , 2008 ) , they do have a high probability of being connected with surrounding msns via powerful inhibitory synapses ( kos and tepper , 1999 ; taverna et al . , 2007 ) .
further experiments may be able to decide between these two proposals for how ventral striatal gamma is generated .
for instance , nasal occlusions are known to abolish piriform gamma ( vanderwolf , 2000 ) , an effect that might be exploited to exclude contributions of piriform gamma to ventral striatum . a different
, powerful approach would be to excite or inhibit ventral striatal fsis directly , as has been done with optogenetic techniques in different brain areas ( cardin et al .
this could test whether ventral striatal fsis are causally involved in the generation of ventral striatal gamma , as well as open the door to studies of their functional significance ( tiesinga and sejnowski , 2009 ) . whatever the precise source of ventral striatal gamma  inherited from afferents or generated by local circuitry , with potentially different sources for gamma-50 and gamma-80  it is possible that even oscillations within these separate bands are themselves not unitary phenomena .
in particular , different components within the bands , such as the ramp and reward receipt response for gamma-80 , the reward site selectivity and movement initiation correlates for gamma-50 , may well arise from distinct circuits or inputs within ventral striatum .
an important point for future research would be to investigate to what extent single neurons ( especially msns , which make up the vast majority of neurons in ventral striatum ) actually participate in these different components ; are these multiple correlates also linked at the single neuron level , or do they become mixed together at the lfp level ? there are many known subdomains of ventral striatum , such as the distinction between core / shell , patch / matrix , and the variety of interacting input gradients ( for cortical and hippocampal formation inputs ) and spots ( in the case of amygdala ) which could in principle support such a separation . in support of the idea that different ventral striatal gamma correlates may arise from spatially distinct sources , kalenscher et al .
( 2010 ) found that the gamma power time series in a substantial number of simultaneously recorded lfps were remarkably poorly correlated ( figure 1c ) .
this was the case even when all estimated recording locations were in the ventral striatum core , and poor correlations could not be attributed to differences in net gamma power , or generally low gamma power , suggesting that distinct gamma patterns stemmed from spatially and functionally distinct compartments within ventral striatum .
in contrast , berke ( 2005 , 2009 ) found that the time course of gamma oscillations appeared extremely similar across widely separated areas of striatum .
( 2005 ; see also masimore , 2008 ) gamma-50 oscillatory events tended to occur at similar times across recording sites .
a possible explanation for this divergence may lie in a subtle difference in the analysis methods used : while kalenscher et al .
( 2010 ) generated a single power time series for a relatively broad frequency band ( 30100  hz ) to correlate between recording sites , berke ( 2009 ) correlated power between recording sites for a range of narrow frequency bands ( i.e. , without  mixing  frequencies across the gamma band ) .
thus , it may be that while the time course of single ( narrow ) gamma frequencies shows relatively little regional differentiation ( berke , 2009 ) , more complex relationships across different frequencies could give rise to heterogeneity only detectable at wider frequency ranges ( kalenscher et al . , 2010 ) .
another possibility is that particular , spatially restricted micro - domains within ventral striatum exhibit different gamma oscillations .
further studies and analyses , such as those that can reveal cross - frequency correlations between sites ( masimore et al . , 2004 ) , as well as
higher - density recordings made possible by silicon recording probes , will be important in investigating these possibilities .
the anatomical location of the ventral striatum as a site of convergence with inputs from frontal cortex , hippocampus , and amygdala , modulated by da and providing outputs with motor influence inspired the classical notion of  a pathway from motivation to action 
that of a  switchboard  ( redgrave et al . , 1999 ; goto and grace , 2008 ; gruber et al . ,
these proposals imply that rather than simply being a passive recipient of incoming signals , ventral striatum contains a mechanism for switching between them .
in such a switch scenario , ventral striatal activity is not dependent on inputs alone , but instead emphasizes ( selects ) a dominant input depending on the state of the  switch   realized in some internal property of ventral striatum , which may itself be controlled by a distinct  switching  input .
such a mechanism is consistent with a popular proposal about the function of gamma oscillations in the brain : that they serve to  bind  together aspects of a common representation by functionally linking neural ensembles or brain areas when they synchronize at the same ( gamma ) frequency ( fries et al .
, 2007 ; uhlhaas et al . , 2009 ) . applied to ventral striatum , this suggests a possible implementation of the switchboard idea in that during ventral striatal gamma-50 , areas exhibiting 50 hz activity such as the piriform cortex or amygdala are functionally linked to ventral striatum and in control of its influence on behavior , while during gamma-80 , frontal cortical areas control ventral striatal output . while it is currently unknown to what extent ventral striatal gamma oscillations
are locally generated ( as discussed above ) , the ability of ventral striatal neurons to dynamically phase - lock to gamma oscillations in a task - dependent manner ( berke , 2009 ) provides one possible switching mechanism .
the observation that dopaminergic drugs induce gamma-80 ( berke , 2009 ) suggests that switching can also occur on a more coordinated scale , consistent with in vitro findings on the effects of da agonists on local inhibitory mechanisms within ventral striatum ( bracci et al .
switch whose only function is to pass on the selected input verbatim ; in particular , it does not exclude the possibility of further computation taking place .
for instance , ventral striatal neurons encode predicted outcomes ( setlow et al . , 2003 ; roitman et al . , 2005 ) , specific action values ( ito and doya , 2009 ; roesch et al . ,
2009 ) , and entrain to hippocampal activity patterns ( berke et al . , 2004 , 2009 ;
, 2009 ) . when these findings are taken together with the effects of ventral striatum lesions on behaviors dependent on associative learning ( pennartz et al . , 1994 ; schoenbaum and setlow , 2003 ; humphries and prescott , 2010 ) they suggest ventral striatum is not simply a ( switching ) waystation but contains sufficient intrinsic circuitry for operations such as the association of spatial information ( where can a reward be found ) with information about a rewards motivating properties ( how good is the reward ; lansink et al . ,
2009 ) or output selection by recurrent inhibition amongst msns ( czubayko and plenz , 2002 ; gurney et al . , 2004 ) . from this perspective , future work might address the relationship between ventral striatal processing during gamma-50 and gamma-80 : is ventral striatum performing the same , or different , operations in those two states ?
more generally , the results reviewed here are part of a broad recognition that basal ganglia operations involve specific patterns of synchrony and rhythmicity , rather than firing rates changes alone ( e.g. , bevan et al . , 2002 ) .
the study of lfps can be a valuable tool for the exploration of how such dynamics contribute to information processing underlying choices , reinforcement learning , and rewards .
the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest .
 a vast literature implicates the ventral striatum in the processing of reward - related information and in mediating the impact of such information on behavior . 
 it is characterized by heterogeneity at the local circuit , connectivity , and functional levels . a tool for dissecting this complex structure that has received relatively little attention until recently is the analysis of ventral striatal local field potential oscillations , which are more prominent in the gamma band compared to the dorsal striatum . 
 here we review recent results on gamma oscillations recorded from freely moving rats . 
 ventral striatal gamma separates into distinct frequency bands ( gamma-50 and gamma-80 ) with distinct behavioral correlates , relationships to different inputs , and separate populations of phase - locked putative fast - spiking interneurons . 
 fast switching between gamma-50 and gamma-80 occurs spontaneously but is influenced by reward delivery as well as the application of dopaminergic drugs . 
 these results provide novel insights into ventral striatal processing and highlight the importance of considering fast - timescale dynamics of ventral striatal activity . 
Ventral Striatal Gamma Oscillations Consist of Distinct, Switching Rhythms
Task- and Reward-Related Modulation of Gamma-50 and Gamma-80
Origins of Ventral Striatal Gamma
Where Next? Functional Relevance of Ventral Striatal Gamma Oscillations
Conflict of Interest Statement