A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

Abstract

How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitive-emotional learning? Why do lesions of thalamus, amygdala, hippocampus, and cortex have differential effects depending on the phase of learning when they occur? In particular, why is the hippocampus typically needed for trace conditioning, but not delay conditioning, and what do the exceptions reveal? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later do not? Why do thalamic or sensory cortical lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions during trace conditioning experiments degrade recent but not temporally remote learning? Why do orbitofrontal cortical lesions degrade temporally remote but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of prefrontal cortex after memory consolidation? How are attention and consciousness linked during conditioning? How do neurotrophins, notably brain-derived neurotrophic factor (BDNF), influence memory formation and consolidation? Is there a common output path for learned performance? A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.

Comparison of different environmental conditions in the follow-up of subarachnoid hemorrhage: an experimental rat study

“…The prefrontal cortex involves representations of meaningful contexts in which related memories occur (20). Interactions between the hippocampus and prefrontal cortex are integral to the formation of consciousness and memory (9). In addition, the hippocampus plays an important Frontal lobe amyloid beta levels (ng/ml) in control and SAH group rats kept in different environments for 7 days (***p<0.001).…”

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

“…-visual retinogeniculate, thalamocortical, and corticocortical development, perception, attention, and categorization (Grossberg and Levine, 1975;Grossberg, 1975bGrossberg, , 1976aGrunewald and Grossberg, 1998;Olson and Grossberg, 1998;Grossberg and Kelly, 1999;Grossberg and Raizada, 2000;Kelly and Grossberg, 2000;Grossberg and Williamson, 2001; Grossberg, 2001, 2003;Grossberg and Grunewald, 2002;Grossberg and Seitz, 2003;Grossberg and Swaminathan, 2004;Grossberg, 2005, 2012;Markowitz et al, 2012); -development of entorhinal grid cells and hippocampal place cells to support spatial navigation (Gorchetchnikov and Grossberg, 2007;Pilly, 2012, 2014;Mhatre et al, 2012;Pilly and Grossberg, 2012, 2013a,b, 2014; -optic flow navigation by the dorsal, or Where. cortical stream (Cameron et al, 1998;Browning et al, 2009a,b;Elder et al, 2009); -time cells for adaptively timed learning by the hippocampus (Grossberg and Schmajuk, 1989;Fiala et al, 1996;Franklin and Grossberg, 2017); -analog and place-value numerical representations by the parietal and prefrontal cortices (Grossberg and Repin, 2003); -auditory streaming (Cohen et al, 1995;; -auditory scene analysis and speaker normalization by the auditory cortex (Cohen et al, 1999;Ames and Grossberg, 2008); -reinforcement learning by cognitive-emotional interactions within and between multiple brain regions (Grossberg, 1975a(Grossberg, , 2018(Grossberg, , 2019Fi...…”

“…The reason seems to be that, remarkably, these spatial and temporal representations seem to use variations of a single brain design that is characterized by similar equations. In particular, large time intervals can be bridged using a mechanism of spectral timing (Grossberg and Schmajuk, 1989;Merrill, 1992, 1996;Grossberg and Seidman, 2006;Franklin and Grossberg, 2017;Grossberg, 2017b) whereby a ''spectrum'' of time cells (MacDonald et al, 2011) along a dorsoventral gradient in the lateral entorhinal cortex, each with different reaction rates, can learn to match the statistical distribution of expected delays in reinforcement over hundreds of milliseconds, or even seconds.…”

Acetylcholine Neuromodulation in Normal and Abnormal Learning and Memory: Vigilance Control in Waking, Sleep, Autism, Amnesia and Alzheimer’s Disease

“…During this kind of consolidation, after learning trials end, early vs. late ablations of hippocampus, amygdala, orbitofrontal cortex and thalamus can have different effects on the memory consolidation process. Although this memory consolidation process is not an explanatory target of the current article, mechanistic explanations of this complex data pattern are provided in Franklin and Grossberg ( 2017 ), along with computer simulations of the main properties of these consolidation data. These explanations also suggest a role for neuromodulation by describing how brain-derived neurotrophic factor, or BDNF, abets the memory consolidation process.…”

“…Cerebellar dynamics are not simulated in nSTART. Key: arrowhead = excitatory synapse; hemidisc = adaptive weight; square = habituative transmitter gate; square followed by a hemidisc = habituative transmitter gate followed by an adaptive weight (reprinted with permission from Franklin and Grossberg, 2017 ).…”