Repeated acquisitions and extinctions in classical conditioning of the rabbit nictitating membrane response

Kehoe, E. James

doi:10.1101/lm.169306

Cited by 17 publications

(14 citation statements)

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“…As previously found for extinction (Napier et al 1992;Kehoe 2006) Despite large changes in magnitude, the timing of the CR peak and duration appeared to change very little. To provide a picture without averaging artifacts, four temporal features of each The time course of CRs constructed by averaging the momentary NM readings for all rabbits at successive 5-msec time points after CS onset on CS-alone trials within each of the specified days in each phase.…”

supporting

confidence: 55%

“…In extinction, the magnitude measures plotted in Figure 3 indicate that most responding occurred on the first day, with only brief periods of spontaneous recovery subsequently (Napier et al 1992;Kehoe 2006). Thus, linear regressions were conducted for both the first day alone and all 6 d. The lower portion of Table 1 indicates that, across both sets of regressions, the onset latency showed a significant increase across trials (P's , 0.001).…”

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confidence: 99%

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Time course of the rabbit's conditioned nictitating membrane movements during acquisition, extinction, and reacquisition

2014

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The present experiment tested whether or not the time course of a conditioned eyeblink response, particularly its duration, would expand and contract, as the magnitude of the conditioned response (CR) changed massively during acquisition, extinction, and reacquisition. The CR duration remained largely constant throughout the experiment, while CR onset and peak time occurred slightly later during extinction. The results suggest that computational models can account for these results by using two layers of plasticity conforming to the sequence of synapses in the cerebellar pathways that mediate eyeblink conditioning.Computational models of eyeblink conditioning generally predict that, during the pairings of a conditioned stimulus (CS) with the unconditioned stimulus (US), the conditioned response (CR) should expand in magnitude and duration around the point of US delivery. Thus, during acquisition, the CR should roughly resemble the changes in the height and width of the sun's rising across the horizon, as depicted schematically in the top panel of Figure 1. Conversely, during the successive CS-alone presentations in extinction, the CR should contract like a setting sun.These predictions flow from the shared assumption that the CS onset initiates a spectrum of microstimuli, whose intensities increase and decrease at different rates (Desmond and Moore 1988;Grossberg and Schmajuk 1989;Machado 1997;Ludvig et al. 2012). At a neural level, the spectrum of microstimuli appears to start with the projection of mossy fibers into the cerebellar cortex (Buonomano and Mauk 1994;Moore and Choi 1997;Mauk et al. 2000;Lepora et al. 2010). In turn, these mossy fiber inputs are refined as temporal codes by interactions among Golgi and granule cells that are active at different times (Kalmbach et al. 2011). Experimentally, asynchronous stimulation of two populations of mossy fibers can act as a CS for acquisition of welltimed eyeblink CRs in rabbits, and post-acquisition manipulations of the relative stimulus durations can predictably alter the CR timing (Kalmbach et al. 2011). In turn, the outputs of these cells activate parallel fibers that converge near the base of Purkinje cells. As a result of pairing stimulation of these microstimulus-like pathways with stimulation of ascending US pathways, CR-like activity has, in fact, been observed in individual Purkinje cells (Jirenhed et al. 2007;Jirenhed and Hesslow 2011).According to the behavioral and neural models described above, each microstimulus gains associative strength in proportion to its level of activation during the US. Thus, the microstimulus whose maximum intensity occurs during the US will gain the greatest associative strength, although microstimuli that are weaker at the time of US will gain less associative strength. During subsequent CS presentations, the magnitude of a CR at any point in time reflects the sum of the associative strengths of the microstimuli multiplied by their level of activity at that moment.Hence, a CR's peak occurs near the time of US delivery, a...

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confidence: 55%

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confidence: 99%

Time course of the rabbit's conditioned nictitating membrane movements during acquisition, extinction, and reacquisition

2014

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“…Subsequent re-acquisition could also use both pathways and be faster that initial acquisition (not shown). This type of hysteresis has been observed both for VOR gain adaptation [26,28,29] and eyeblink conditioning [30,31].…”

Section: Discussionmentioning

confidence: 91%

Adaptive-filter Models of the Cerebellum: Computational Analysis

Dean

Porrill

2008

Cerebellum

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Many current models of the cerebellar cortical microcircuit are equivalent to an adaptive filter using the covariance learning rule. The adaptive filter is a development of the original Marr-Albus framework that deals naturally with continuous time-varying signals, thus addressing the issue of 'timing' in cerebellar function, and it can be connected in a variety of ways to other parts of the system, consistent with the microzonal organization of cerebellar cortex. However, its computational capacities are not well understood. Here we summarise the results of recent work that has focused on two of its intrinsic properties. First, an adaptive filter seeks to decorrelate its (mossy fibre) inputs from a (climbing fibre) teaching signal. This procedure can be used both for sensory processing, e.g. removal of interference from sensory signals, and for learning accurate motor commands, by decorrelating an efference copy of those commands from a sensory signal of inaccuracy. As a model of the cerebellum the adaptive filter thus forms a natural link between events at the cellular level, such as forms of synaptic plasticity and the learning rules they embody, and intelligent behaviour at the system level. Secondly, it has been shown that the covariance learning rule enables the filter to handle input and intrinsic noise optimally. Such optimality may underlie the recently described role of the cerebellum in producing accurate smooth pursuit eye movements in the face of sensory noise. Moreover, it has the consequence of driving most input weights to very small values, consistent with experimental data that many parallel-fibre synapses are normally silent. The effectiveness of silent synapses can only be altered by LTP, so learning tasks depending on a reduction of Purkinje cell firing require the synapses to be embedded in a second, inhibitory pathway from parallel fibre to Purkinje cell. This pathway and the appropriate climbing-fibre related plasticity have been described experimentally, and its presence has implications for asymmetries and hysteresis in behavioural learning rates that are also consistent with experimental observations. These computational properties of the adaptive filter suggest that it is both powerful and realistic enough to be a suitable candidate model of the cerebellar cortical microcircuit.

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“…Again, the key comparisons are the rates of extinction on the postmuscimol session vs. the control 1 session and vs. the control 2 session. These within-animal comparisons are possible, because previous studies have shown that repeated rounds of extinction and reacquisition do not appreciably change the rate of trace response extinction (Kehoe 2006).…”

Section: Disrupting Cerebellar Mechanisms Of Extinction Prevents Extimentioning

confidence: 99%

Multiple sites of extinction for a single learned response

Kalmbach¹,

Mauk

2012

Journal of Neurophysiology

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Kalmbach BE, Mauk MD. Multiple sites of extinction for a single learned response. J Neurophysiol 107: 226 -238, 2012. First published September 21, 2011 doi:10.1152/jn.00381.2011.-Most learned responses can be diminished by extinction, a process that can be engaged when a conditioned stimulus (CS) is presented but not reinforced. We present evidence that plasticity in at least two brain regions can mediate extinction of responses produced by trace eyelid conditioning, where the CS and the reinforcing stimulus are separated by a stimulus-free interval. We observed individual differences in the effects of blocking extinction mechanisms in the cerebellum, the structure that, along with several forebrain structures, mediates acquisition of trace eyelid responses; in some rabbits extinction was prevented, whereas in others it was largely unaffected. We also show that cerebellar mechanisms can mediate extinction when noncerebellar mechanisms are bypassed. Together, these observations indicate that trace eyelid responses can be extinguished via processes operating at more than one site, one in the cerebellum and one upstream in forebrain. The relative contributions of these sites may vary from animal to animal and situation to situation.

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Repeated acquisitions and extinctions in classical conditioning of the rabbit nictitating membrane response

Cited by 17 publications

References 85 publications

Time course of the rabbit's conditioned nictitating membrane movements during acquisition, extinction, and reacquisition

Time course of the rabbit's conditioned nictitating membrane movements during acquisition, extinction, and reacquisition

Adaptive-filter Models of the Cerebellum: Computational Analysis

Multiple sites of extinction for a single learned response

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