PSY 511.001 Spr 2026
:max_bytes(150000):strip_icc():format(webp)/2794861-classical-vs-operant-conditioning-5afc42a343a10300370da76f.png)
Fig. 3. Flattened surface representation. (A) Derivation of a flattened patch for a single subject, showing the corresponding area on the original surface. On the patch, gyri are represented with lighter shading and sulci with darker shading. (B) Hand drawn boundaries used for group alignment. (C) Left hemisphere tonotopy map for a single subject projected on a flattened surface and in volumetric space.
News story about Beatrice Gelber from the Tucson Daily Citizen October 19, 1960. © 1960, USA TODAY NETWORK. This image is not covered by the CC-BY 4.0 licence and may not be separated from the article.
In Experiment 1 (top), one group of Paramecia was exposed to intermittent training trials in which a wire was coated with bacteria (every 3rd trial during the training phase). This group acquired a conditioned response to the clean wire, as measured by adherence to the wire in final test trials. In contrast, an untrained group did not show a conditioned response. Experiment 2 (bottom) demonstrated that the wire by itself did not drive conditioned responding.
Blocks ion pore. See https://en.wikipedia.org/wiki/Ketamine
Recent studies have further characterized the mechanism and function of STDP in both in vitro and in vivo preparations, addressing the following questions: Which cellular mechanisms determine the STDP window, and how similar are they to the mechanisms underlying LTP and LTD induced by HFS and LFS, respectively? Does the window depend on the dendritic location of the input, and can it be regulated by neuromodulatory inputs? Does a similar learning rule apply to the inhibitory circuits? Can we observe the consequences of the asymmetric window in vivo, and can it account for the synaptic modifications induced by complex, naturalistic spike trains? In this review we summarize recent progress in these areas.
Balanced excitation and inhibition are crucial for normal brain functions (Shu et al. 2003) and for regulating experience-dependent developmental plasticity (Hensch 2005). Although the strength of excitatory synapses can be modified through STDP, an important question is whether and how correlated pre- and postsynaptic activity affects inhibitory circuits. Inhibition in a network depends on both the excitatory synapses onto inhibitory neurons and the inhibitory synapses themselves. Spike timing–dependent plasticity has been studied at both of these synapses.
Fig. 1. Place fields of eight pyramidal cells recorded at different longitudinal levels of CA3 during animals’ running on a linear 18-m track. (A) Nissl-stained sections showing recording locations in four animals. Individual rat numbers are indicated. (B) Place fields on the 18-m track. Each panel shows one cell. Rat numbers refer to (A). Percentages indicate location along the dorsoventral axis. For rat 11285, only the 87% location is shown in (A). (Top of each panel) Smoothed spike density function indicates firing rate as a function of position. Horizontal bar indicates estimated place field. Left runs, red; right runs, green. (Bottom of each panel) Raster plot showing density of spikes on individual laps. Each vertical tic indicates one spike and each horizontal line shows one lap (right side, blue-green; left, red). See fig. S5 for complete cell samples. Note 5- to 10-m-long place fields in ventral CA3 (colored horizontal bars; left runs, red; right runs, green).
a, PRF paradigm and modeling. In Exp. 1, participants underwent pRF mapping. Participants viewed visual scenes through a bar aperture that gradually traversed the visual field. Each visual-field traversal lasted 36 s (18 × 2 s positions), and the bar made eight traversals per run. The direction of motion varied between traversals. To estimate the pRF for each voxel, a synthetic time series is generated for 400 visual-field locations (200 x and y positions) and 100 sizes (sigma). This results in four million possible time series that are fit to each voxel’s activity. The fit results in four parameters describing each receptive field: x, y, sigma and amplitude. b, Negative-amplitude pRFs fall anterior to the cortex typically considered visual (beyond known retinotopic maps). Group average (n = 17) pRF amplitude map (threshold at explained variance R-squared (R2) > 0.08) is shown on partially inflated representations of the left hemisphere, alongside ROIs: SPAs (OPA, PPA), PMAs (LPMA, VPMA) (localized in an independent group of participants24) and the DMN22. The known retinotopic maps in the posterior cortex (black dotted outlines21) contain exclusively positive-amplitude pRFs (hot colors), as visual stimulation evokes positive retinotopically specific BOLD responses. Negative-amplitude pRFs (cool colors), where visual stimulation evokes a negative spatially specific BOLD response, arise anterior to these retinotopic maps in the PMAs and DMN (see Fig. 2a for example time series from a representative subject).
a, PRF modeling reveals posterior–anterior inversion of pRF amplitude in individual participants. Left, PRF amplitude for a representative participant overlaid onto a lateral view of the left hemisphere (threshold at R2 > 0.15; see Extended Data Fig. 1 for example ventral and lateral surface pRF amplitude maps from all participants and Extended Data Fig. 3 for amplitude maps with default mode parcellation overlaid). Posterior visual cortex is dominated by positive-amplitude pRFs (hot colors), while cortex anterior to regions classically considered visual exhibits a high concentration of negative-amplitude pRFs (cold colors). This individual’s OPA and LPMA are shown in white. Both the SPAs and PMAs contain pRFs (Extended Data Fig. 2). Right, time-series, model fits and reconstructed pRFs for two surface vertices in this subject. Top, example prototypical positive-amplitude pRF from the lateral SPA (OPA) in the left and right hemispheres (LH, RH). Bottom, example negative-amplitude population receptive field from the LPMA. b, Memory areas (PMAs) contain a larger percentage of negative pRFs compared to perceptual areas (SPAs) (repeated measures ANOVA, Bonferroni-corrected t-tests). Blue bars depict percentage of negative pRFs from individually localized SPAs and PMAs compared to total pRFs in the area (dotted outline). On the ventral and lateral surfaces, SPAs are dominated by positive pRFs, whereas a transition from positive to negative pRFs is evident within PMAs. Individual participant data points overlaid and connected in gray. P two-tailed < 0.05, **P two-tailed < 0.001. NS, not significant.
Henry G. Molaison was identified with his permission after his death in 2008.
