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The paper discusses the contributions of Donald Olding Hebb to psychology, particularly the shift from behavioral psychology to a biological understanding of mental processes. Key concepts include the 'Hebb rule' which highlights the association between neuron activity and memory formation, distinguishing between transient and permanent memories. It also reviews Hebb's work on fear responses and sensory deprivation, emphasizing the critical role of sensory stimulation in psychological development.
Proceedings of the National Academy of Sciences, 2014
A long-standing hypothesis termed "Hebbian plasticity" suggests that memories are formed through strengthening of synaptic connections between neurons with correlated activity. In contrast, other theories propose that coactivation of Hebbian and neuromodulatory processes produce the synaptic strengthening that underlies memory formation. Using optogenetics we directly tested whether Hebbian plasticity alone is both necessary and sufficient to produce physiological changes mediating actual memory formation in behaving animals. Our previous work with this method suggested that Hebbian mechanisms are sufficient to produce aversive associative learning under artificial conditions involving strong, iterative training. Here we systematically tested whether Hebbian mechanisms are necessary and sufficient to produce associative learning under more moderate training conditions that are similar to those that occur in daily life. We measured neural plasticity in the lateral amygdala, a brain region important for associative memory storage about danger. Our findings provide evidence that Hebbian mechanisms are necessary to produce neural plasticity in the lateral amygdala and behavioral memory formation. However, under these conditions Hebbian mechanisms alone were not sufficient to produce these physiological and behavioral effects unless neuromodulatory systems were coactivated. These results provide insight into how aversive experiences trigger memories and suggest that combined Hebbian and neuromodulatory processes interact to engage associative aversive learning.
Neural Networks, 2001
Dynamic Neurons, Santiago Ramón y Cajal, 2019
Santiago Ramón y Cajal. Discoveries in the neurosciences made possible by technical advances in the late 19 th century had great influence in the field of psychology. The idea that one can manipulate the very structure of the brain by what one experiences has its roots in the research that led to the discovery of the synapse. Scientists of the late 19 th century diverged from hundreds of years of assumptions about the structure and function of the nervous system. Traditional views were bitterly guarded even as evidence against them mounted. In the end, strong observational research and exciting speculation about the nature of the nervous system laid the groundwork for work now being done in the fields of neuroplasticity and neurogenesis. The field of psychology was to be changed dramatically by the discovery of the dynamic nature of neurons.
Neural Networks, 2001
Here, we develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel [Pinsky, P. F., & Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. Journal of Computational Neuroscience, 1, 39±60]. To study associative memory, we connect a pool of cells by a structured connectivity matrix. The connection weights are shaped by simple Hebbian coincidence learning using a set of spatially sparse patterns. We study the neuronal activity processes following an external stimulation of a stored memory. In two series of simulation experiments, we explore the effect of different classes of external input, tonic and¯ashed stimulation. With tonic stimulation, the addressed memory is an attractor of the network dynamics. The memory is displayed rhythmically, coded by phase-locked bursts or regular spikes. The participating neurons have rhythmic activity in the gamma-frequency range (30±80 Hz). If the input is switched from one memory to another, the network activity can follow this change within one or two gamma cycles. Unlike similar models in the literature, we studied the range of high memory capacity (in the order of 0.1 bit/synapse), comparable to optimally tuned formal associative networks. We explored the robustness of ef®cient retrieval varying the memory load, the excitation/inhibition parameters, and background activity. A stimulation pulse applied to the identical simulation network can push away ongoing network activity and trigger a phase-locked association event within one gamma period. Unlike as under tonic stimulation, the memories are not attractors. After one association process, the network activity moves to other states. Applying in close succession pulses addressing different memories, one can switch through the space of memory patterns. The readout speed can be increased up to the point where in every gamma cycle another pattern is displayed. With pulsed stimulation, bursts become relevant for coding, their occurrence can be used to discriminate relevant processes from background activity.
Physical Review E, 1999
A correlation-based ͑''Hebbian''͒ learning rule at a spike level with millisecond resolution is formulated, mathematically analyzed, and compared with learning in a firing-rate description. The relative timing of presynaptic and postsynaptic spikes influences synaptic weights via an asymmetric ''learning window.'' A differential equation for the learning dynamics is derived under the assumption that the time scales of learning and neuronal spike dynamics can be separated. The differential equation is solved for a Poissonian neuron model with stochastic spike arrival. It is shown that correlations between input and output spikes tend to stabilize structure formation. With an appropriate choice of parameters, learning leads to an intrinsic normalization of the average weight and the output firing rate. Noise generates diffusion-like spreading of synaptic weights.
Biological Cybernetics, 1998
Biological memories have a number of unique features, including (1) hierarchical, reciprocally interacting layers, (2) lateral inhibitory interactions within layers, and (3) Hebbian synaptic modi®cations. We incorporate these key features into a mathematical and computational model in which we derive and study Hebbian learning dynamics and recall dynamics. Introducing the construct of a feasible memory (a memory that formally responds correctly to a speci®ed collection of noisy cues that are known in advance), we study stability and convergence of the two kinds of dynamics by both analytical and computational methods. A conservation law for memory feasibility under Hebbian dynamics is derived. An infomax net is one where the synaptic weights resolve the most uncertainty about a neural input based on knowledge of the output. The infomax notion is described and is used to grade memories and memory performance. We characterize the recall dynamics of the most favorable solutions from an infomax perspective. This characterization includes the dynamical behavior when the net is presented with external stimuli (noisy cues) and a description of the accuracy of recall. The observed richness of dynamical behavior, such as its initial state sensitivity, provides some hints for possible biological parallels to this model.
The Yale journal of biology and medicine, 2008
The neuropsychological concepts found in Donald Hebb's The Organization of Behavior have greatly influenced many aspects of neuroscience research over the last half century. Hebb's ideas arose from a rich tradition of research. An underappreciated contribution came from pioneering studies at Yale University. Here, we wish to reconsider these developments, placing particular emphasis on the roles of the neurophysiologists John Fulton, J.J. Dusser de Barenne, and Warren McCulloch and the psychologists Donald Marquis and Ernest Hilgard. These neuroscientists all contributed significantly to the intellectual climate that gave rise to Hebb's remarkable synthesis.
Progress in Neurobiology, 1984
Contents I. Introduction 89 2. What are Hebb synapses? 91 3. Is visual cortical development explained by hebbian synapses? 92 3.1. Synaptic competition 92 3.2. Selective vs instructive mechanisms 93 4. Hebb plasticity in neural systems 94 4.1. Cerebellar learning 94 4.2. Hippocampal LTP 95 5. Cellular associative learning: The evidence in identified synapses 96 6. Conclusions 97 Acknowledgements 98 References 98
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