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self-organising map
self-organising map

... •Let m be the dimension of the input space. A pattern chosen randomly from input space is denoted by: x=[x1, x2,…, xm]T •The synaptic weight of each neuron in the output layer has the same dimension as the input space. We denote the weight of neuron j as: wj=[wj1, wj2,…, wjm]T, j=1,2,…,l Where l is ...
Amygdala-Prefrontal Synchronization Underlies Resistance to
Amygdala-Prefrontal Synchronization Underlies Resistance to

... two-way ANOVA). In both the amygdala and the dACC, responsive cells were homogeneously distributed within our recording borders (Figure S2C, p > 0.2 for all, bootstrap analysis), suggesting that they represent an activity pattern common in wide parts of these two structures. In addition, there was n ...
Spiking Neurons - Computing Science and Mathematics
Spiking Neurons - Computing Science and Mathematics

... In other areas of the brain the wiring pattern looks different . In all areas, however, neurons of different sizes and shapes form the basic elements. A typical neuron has three parts, called dendritic tree, soma, and axon; see Figure 1.2. Roughly speaking, signals from other neurons arrive onto the ...
Cognon Neural Model Software Verification and
Cognon Neural Model Software Verification and

Parallel Processing of Appetitive Short- and Long
Parallel Processing of Appetitive Short- and Long

... neural circuits than just ab and g neurons. Blocking output from a0 b0 neurons for 1 hr after training affects both STM and LTM. Similarly, blocking output from dorsal paired medial (DPM) neurons, which project onto the MB lobes, for 1 hr after appetitive conditioning affects both STM and LTM [30, 3 ...
Matlin, Cognition, 7e, Chapter 8: General Knowledge
Matlin, Cognition, 7e, Chapter 8: General Knowledge

... which customers will qualify for a loan. Her decision is based on a completed application form that contains ten questions. The bank manager's experience allows her to use "Intuition” that will enable her to recognize certain patterns that her brain has become attuned to. If we had a large number of ...
Computation with Spikes in a Winner-Take-All Network
Computation with Spikes in a Winner-Take-All Network

... the excitatory connections from excitatory neurons to interneurons and the inhibitory connections from interneurons to excitatory neurons. In our model, we assume the forward connections between the excitatory and the inhibitory neurons to be strong, so that each spike of an excitatory neuron trigge ...
Learning place cells, grid cells and invariances: A unifying model
Learning place cells, grid cells and invariances: A unifying model

... remain small enough to preserve an overall head direction tuning of the cell, because individual grid fields tend to align their head direction ...
Dynamic Stochastic Synapses as Computational Units
Dynamic Stochastic Synapses as Computational Units

... output bit of this threshold gate after the presentation of the second part yE of the input as the output of the whole computation, this threshold gate with “dynamic synapses” computes the boolean function Fn : {0, 1}2n → {0, 1} x, yE) = 1 ⇐⇒ ∃i ∈ {1, . . . , n}(yi = 1 and xi = 0). One might defined ...
Cholinergic Deafferentation of the Entorhinal Cortex in Rats
Cholinergic Deafferentation of the Entorhinal Cortex in Rats

... given an overdose of sodium pentobartital and were transcardially perfused with saline followed by a 10% buffered formalin. Brains were allowed to sink in a 30% sucrose solution and then sliced at 50 ␮m. Alternate coronal sections were thionin stained for nissl bodies and acetylcholinesterase as opt ...
INSTANTANEOUSLY TRAINED NEURAL NETWORKS WITH
INSTANTANEOUSLY TRAINED NEURAL NETWORKS WITH

... regions, dividing a 16 by 16 area into a black spiral shaped region and another white region. A point in the black spiral region is represented as a binary “1” and a point in the white region is represented by a binary “0”. Any point in the region is represented by row and column coordinates. These ...
Document
Document

... sometimes four layers, including one or two hidden layers. Each layer can contain from 10 to 1000 neurons. Experimental neural networks may have five or even six layers, including three or four hidden layers, and utilize millions of neurons. ...
Multiplicative Gain Changes Are Induced by Excitation or Inhibition
Multiplicative Gain Changes Are Induced by Excitation or Inhibition

... Krukowski and Miller, 2001; Palmer and Miller, 2002) but with minor adjustments in the present work. In particular, the parameters were designed without reference to (and before obtaining) the results presented here. The values for the following parameters are the same in each simulation: g leak ⫽ 1 ...
Realistic synaptic inputs for model neural networks
Realistic synaptic inputs for model neural networks

... Reahsttc synfiplrc tnpuls for model neural networks ...
Fading memory and kernel properties of generic cortical microcircuit
Fading memory and kernel properties of generic cortical microcircuit

... This computational model is universal (for deterministic offline digital computation) in the sense that every deterministic digital function that is computable at all (according to a well-established mathematical definition, see [41]) can be computed by some Turing machine. Before a Turing machine give ...
Activity of Defined Mushroom Body Output Neurons
Activity of Defined Mushroom Body Output Neurons

... neurons might be glutamatergic. DVGlut labeling perfectly overlapped with the GFP-marked presynaptic field of the M4/6 neurons (Figure 1E). This is most evident at higher resolution where, in addition, individual M4/6 presynaptic boutons can be seen to be large and spherical (Figure 1E, inserts). We ...
From spike frequency to free recall:
From spike frequency to free recall:

... Researchers have described numerous hypotheses of hippocampal function based on lesion data, attempting to present these hypotheses entirely in verbal terms -- using terms such as “interference”, “response inhibition”, “context”, “temporal contiguity”, “snapshot memory” etc. This method of hypothesi ...
More is Better: The Effects of Multiple Repetitions on Implicit Memory
More is Better: The Effects of Multiple Repetitions on Implicit Memory

... might not appear until a substantial delay after encoding. The intuition behind this speculation is that, depending on how memory is tested, all the words presented in an experimental setting whether presented one time or many might be near the ceiling of availability (where availability is some fun ...
Dr. Abeer Mahmoud - PNU-CS-AI
Dr. Abeer Mahmoud - PNU-CS-AI

... would still recognize it, as long as the total sum was greater than 1.5. So the neuron is “Noise Tolerant” or able to “Generalize” which means it will still successfully recognize the picture, even if it isn’t perfect - this is one of the most important attributes of Neural Networks. Dr.Abeer Mahmou ...
Nerve Cell Communication - URMC
Nerve Cell Communication - URMC

... branches. Hint: As the impulse travels along the axon, you should arrange the impulse card as shown in the diagram on the right. ...
Nerve Cell Communication - URMC
Nerve Cell Communication - URMC

... branches. Hint: As the impulse travels along the axon, you should arrange the impulse card as shown in the diagram on the right. ...
Neuronal Regulation Implements Efficient Synaptic Pruning
Neuronal Regulation Implements Efficient Synaptic Pruning

... synaptic efficacy at the metastable state as a function of the initial synaptic efficacy, for different values of the degradation dimension a. As observed, a sigmoidal dependency is obtained, where the slope of the sigmoid s.trongly depends on the degradatiori dimension. In the two limit cases, addi ...
Optimal Recall from Bounded Metaplastic Synapses: Predicting
Optimal Recall from Bounded Metaplastic Synapses: Predicting

... from more realistic synapses that suffer from a bounded dynamical range. Second, at the level of retrieval, there are also several aspects of hippocampal circuit dynamics of which we lack a theoretical account. For example, experimental work has long shown that synaptic plasticity is accompanied by ...
Emotional Arousal and Memory Binding
Emotional Arousal and Memory Binding

... using an object-based framework. According to this framework, emotionally arousing objects attract attention that enhances binding of their constituent features. In contrast, the emotional arousal associated with one object either impairs or has no effect on the associations between that object and ...
Mental Processes -- How the Mind Arises from the Brain Roger Ellman
Mental Processes -- How the Mind Arises from the Brain Roger Ellman

... Humans perceive, recognize, a very large number of universals, of course. Some examples, in order to clarify the concept, are: - recognition of the letter E, whether capital or lower case, hand written or mechanically produced, large or small, alone or among other symbols, even though the particular ...
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Sparse distributed memory

Sparse distributed memory (SDM) is a mathematical model of human long-term memory introduced by Pentti Kanerva in 1988 while he was at NASA Ames Research Center. It is a generalized random-access memory (RAM) for long (e.g., 1,000 bit) binary words. These words serve as both addresses to and data for the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving one close to it, as measured by the number of mismatched bits (i.e., the Hamming distance between memory addresses).SDM implements transformation from logical space to physical space using distributed data storing. A value corresponding to a logical address is stored into many physical addresses. This way of storing is robust and not deterministic. A memory cell is not addressed directly. If input data (logical addresses) are partially damaged at all, we can still get correct output data.The theory of the memory is mathematically complete and has been verified by computer simulation. It arose from the observation that the distances between points of a high-dimensional space resemble the proximity relations between concepts in human memory. The theory is also practical in that memories based on it can be implemented with conventional RAM-memory elements.
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