Gamma band plasticity in sensory cortex is a signature of the strongest memory rather than memory of the training stimulus
Introduction
A major advance in broadening our understanding of the neural substrates of learning and memory has involved a shift in emphasis from linear stimulus–response circuits to neural networks. Following Hebb’s insights (Hebb, 1949), it is now generally accepted that coordinated neuronal activity forms during learning to represent and store relevant information, serve cognition and ultimately behavioral action. Gamma frequency oscillations (∼30–120 Hz) are thought to reflect the synchronous activity of neurons both within and across cortical fields (Buzsáki & Wang, 2012). The timescale of gamma oscillations is appropriate for synaptic integration (Salinas and Sejnowski, 2000, Volgushev et al., 1998) and spike timing dependent plasticity (STDP) (Bi and Poo, 1998, Isaac et al., 2009, Wespatat et al., 2004). Particularly relevant to the domain of learning and memory, increased gamma activity has been linked to processes such as attention (Börgers, Epstein, & Kopell, 2008) and short-term memory (Lutzenberger et al., 2002, Pesaran et al., 2002). Moreover, the level of gamma activity at the time of encoding can predict the degree of later recall (Fell and Axmacher, 2011, Osipova et al., 2006, Sederberg et al., 2006, Sederberg et al., 2007). An increase in cortical gamma power also develops during simple associative auditory classical conditioning in humans (Miltner, Braun, Arnold, Witte, & Taub, 1999), underscoring its ubiquity and the potential applicability of appropriate animal models to mechanisms of human learning.
Animal models of associative learning have identified candidate neural substrates for the representation and storage of signal stimuli in the cerebral cortex. For example when a tone is paired with a reinforcer, receptive fields (RF) in the primary auditory cortex (A1) shift to emphasize the frequency of the conditioned stimulus (CS) (Bakin and Weinberger, 1990, Edeline and Weinberger, 1993, Gao and Suga, 2000, Kisley and Gerstein, 2001). Such representational plasticity has the main attributes of associative memory: associativity, specificity, consolidation and long-term retention (reviewed in Weinberger, 2007). Gamma activity may play a critical role in the development of cortically based associative learning. For example, an increase in gamma power within A1 predicts both specific CS-directed cortical plasticity and also behaviorally validated learning 24 h later, but only during initial acquisition, not during maintenance of the memory (Headley and Weinberger, 2011, Weinberger et al., 2006).
Heretofore, it has been assumed tacitly that enhanced gamma activity induced by a signal stimulus during learning reflects the increased salience or behavioral relevance of that stimulus, e.g., the CS in simple associative learning. However, there is an alternative possibility. It is well known that even when perceptual, acquisition and storage processes are functioning optimally, the content of the resultant memory can differ from the actual experience. Subjects trained identically do not all acquire the exact same content (Bieszczad and Weinberger, 2010b, Bieszczad and Weinberger, 2010a, Bieszczad and Weinberger, 2012, Ohl et al., 2001, Polley et al., 2006). Therefore, the greatest increase in gamma activity may actually reflect the stimulus that has gained the greatest strength through learning, rather than the training stimulus. The relative strength of memory for different stimuli along a sensory dimension cannot be determined during training, but rather depends on obtaining post-training stimulus generalization gradients (Bouton, 2007, Mostofsky, 1965). Discrimination learning (i.e., reinforced CS+ with non-reinforced CS−) is a well-documented example. The peak of the post-training generalization gradient is generally not at the CS+ but is displaced to a stimulus value that is farther away from the CS− (Purtle, 1973). Such “peak shift” has been thought to reflect the summation of an excitatory neural gradient centered on the CS+ and an inhibitory neural gradient centered on the CS− (Spence, 1937).
Recently, we found such shifted generalization peaks in simple associative conditioning, due to pre-training exposure to various tones that induced an inhibitory neural gradient in primary auditory cortex (A1) (Miasnikov & Weinberger, 2012). This disjunction between the training frequency and the peak of the generalization gradient provides a unique opportunity to determine whether enhanced gamma activity during learning is tied to the CS frequency or to the peak of the generalization gradient, i.e., to the tone that is most strongly represented in memory. If increased gamma activity reflects increased neural synchrony that is part of the substrate of auditory frequency memory, then the greatest increase in gamma should be tightly linked to the strongest memory, regardless of the training frequency. We report here the analysis of changes in gamma activity that had been recorded during the previous study. The same changes in gamma were analyzed two ways: based on the CS training frequency and based on the peak of the generalization gradient.
Section snippets
Materials and methods
As the present analysis concerns EEG activity obtained in our previous study (Miasnikov & Weinberger, 2012), the materials and methods are mainly the same and will be summarized briefly. All procedures were performed in accordance with the University of California, Irvine, Animal Research Committee and the NIH Animal Welfare guidelines. During training and testing, subjects were continuously monitored by video cameras.
Behavior
Pairing tone and NBstm produced some significant changes from the pre-training (Day 2) respiratory responses. Fig. 2A presents group pre- and post-training tone frequency generalization gradients (FGGs). Prior to training, responses were not uniform but were greatest at 8.22 kHz for the group. Regardless, as the tones used both pre- and post-training were identical, it is the difference between the pre- and post-FGGs that reveals the effects of training.
The post-training FGG did not differ from
Resume of gamma findings
This report focuses on the relationship between associatively induced gamma band activity in sensory cortex and behaviorally validated memory. The latter was implanted by pairing a tone with stimulation of the nucleus basalis. Previously, increased gamma activity had been identified as a neural signature of association, e.g., when a sensory stimulus develops the ability to predict a reinforcer (Gruber et al., 2001, Headley and Weinberger, 2011, McLin et al., 2003, Miltner et al., 1999). Such a
Acknowledgements
We are grateful to Steven Clifford (CED) for specialized software development, Gabriel K. Hui for assistance with the manuscript and Jacquie Weinberger for citation research. This study was funded by NIDCD/NIH DC-02938 to NMW.
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