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66-555: A neural Turing machine ( NTM ) is a recurrent neural network model of a Turing machine . The approach was published by Alex Graves et al. in 2014. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers . An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent . An NTM with

132-566: A Hebbian learning rule. Later, in Principles of Neurodynamics (1961), he described "closed-loop cross-coupled" and "back-coupled" perceptron networks, and made theoretical and experimental studies for Hebbian learning in these networks, and noted that a fully cross-coupled perceptron network is equivalent to an infinitely deep feedforward network. Similar networks were published by Kaoru Nakano in 1971 , Shun'ichi Amari in 1972, and William A. Little  [ de ] in 1974, who

198-542: A long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone. The authors of the original NTM paper did not publish their source code . The first stable open-source implementation was published in 2018 at the 27th International Conference on Artificial Neural Networks, receiving a best-paper award. Other open source implementations of NTMs exist but as of 2018 they are not sufficiently stable for production use. The developers either report that

264-582: A 'reverberating' circular feedback loop without any original 'firing' signal or any new additional incoming signals. McCulloch claimed this accounted for conscious phenomena in which individuals' world view, or the reaffirmation of their senses' perceived external stimulus, was cognitively distorted or all together missing as seen in individuals with phantom limb syndrome (claiming to feel an arm that has been amputated or lost) or hallucinations (perceived sensory stimulus without an original external signal). Lawrence Kubie, another attending conference member and

330-946: A Jordan network are also called the state layer. They have a recurrent connection to themselves. Elman and Jordan networks are also known as "Simple recurrent networks" (SRN). Variables and functions Long short-term memory (LSTM) is the most widely used RNN architecture. It was designed to solve the vanishing gradient problem . LSTM is normally augmented by recurrent gates called "forget gates". LSTM prevents backpropagated errors from vanishing or exploding. Instead, errors can flow backward through unlimited numbers of virtual layers unfolded in space. That is, LSTM can learn tasks that require memories of events that happened thousands or even millions of discrete time steps earlier. Problem-specific LSTM-like topologies can be evolved. LSTM works even given long delays between significant events and can handle signals that mix low and high-frequency components. Many applications use stacks of LSTMs, for which it

396-505: A complete working theory of the mind. The Macy Conferences were discontinued shortly after the ninth conference. Participants: (as members or guests) in at least one of the Cybernetics conferences: Harold Alexander Abramson , Ackerman, Vahe E. Amassian, William Ross Ashby , Yehoshua Bar-Hillel , Gregory Bateson , Alex Bavelas, Julian H. Bigelow, Herbert G. Birch, John R. Bowman, Henry W. Brosin, Yuen Ren Chao (who memorably recited

462-522: A conditionally generative model of sequences, aka autoregression . Concretely, let us consider the problem of machine translation, that is, given a sequence ( x 1 , x 2 , … , x n ) {\displaystyle (x_{1},x_{2},\dots ,x_{n})} of English words, the model is to produce a sequence ( y 1 , … , y m ) {\displaystyle (y_{1},\dots ,y_{m})} of French words. It

528-508: A data flow, and the data flow itself is the configuration. Each RNN itself may have any architecture, including LSTM, GRU, etc. RNNs come in many variants. Abstractly speaking, an RNN is a function f θ {\displaystyle f_{\theta }} of type ( x t , h t ) ↦ ( y t , h t + 1 ) {\displaystyle (x_{t},h_{t})\mapsto (y_{t},h_{t+1})} , where In words, it

594-724: A majority of the conferences was reflexivity . Claude Shannon , one of the attendees, had previously worked on information theory and laid one of the initial frameworks for the Cybernetic Conferences by postulating information as a probabilistic element which reduced the uncertainty from a set of choices (i.e. being told a statement is true, or even false, completely reduces the ambiguity of its message). Other conference members, especially Donald MacKay , sought to reconcile Shannon's view of information , which they called selective information, with theirs of 'structural' information which signified how selective information

660-437: A neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded in time. Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1995 and set accuracy records in multiple applications domains. It became the default choice for RNN architecture. Bidirectional recurrent neural networks (BRNN) uses two RNN that processes

726-471: A psychiatrist, noted how repetitive and obsessive behaviors manifesting themselves in neurotics bore a resemblance to the behavior enacted by McCulloch's 'reverberating' loops. Shannon had developed a maze-solving device which attendees of the Macy Conferences likened to a rat. Shannon's 'rat' was designed and programmed to find its marked goal when dropped at any point in a maze by giving it

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792-409: A separate individual, MacKay turned the second individual into an additional observer which could be elicited to react just how the initial observer did, a reaction that could then further be observed by a nested doll of observers ad infinitum . Reflexive feedback loops continued to come up during the Macy Conferences and became a prominent issue during its later discussions as well, most notably in

858-471: A sequence of hidden vectors, and the decoder RNN processes the sequence of hidden vectors to an output sequence, with an optional attention mechanism . This was used to construct state of the art neural machine translators during the 2014–2017 period. This was an instrumental step towards the development of Transformers . An RNN may process data with more than one dimension. PixelRNN processes two-dimensional data, with many possible directions. For example,

924-541: A set of meetings of scholars from various academic disciplines held in New York under the direction of Frank Fremont-Smith at the Josiah Macy Jr. Foundation starting in 1941 and ending in 1960. The explicit aim of the conferences was to promote meaningful communication across scientific disciplines , and restore unity to science. There were different sets of conferences designed to cover specific topics, for

990-968: A skipped year in 1958. While the conferences have developed a reputation as being primarily about LSD , the drug was discussed extensively at the second conference and was not the primary focus of most of the sessions. In the first conference, for instance, reference to LSD appears only one time, as a side comment during discussion. Participants: Hudson Hoagland (Chairman), Harold A. Abramson (Secretary), Philip Bard (absent), Henry K. Beecher (absent), Mary A. B. Brazier, G. L. Cantoni, Ralph W. Gerard, Roy R. Grinker, Seymour S. Kety, Chauncey D. Leake (absent), Horace W. Magoun, Amedeo S. Marrazzi, I. Arthur Mirsky, J. H. Quastel (absent), Orr E. Reynolds, Curt P. Richter (absent), Ernst A. Scharrer, David Shakow (absent) Guests: Charles D. Aring, William Borberg , Enoch Callaway III, Conan Kornetsky, Joost A. M. Meerloo, John I. Nurnberger, Carl C. Pfeiffer, Anatol Rapoport, Maurice H. Seevers, Richard Trumbull Topics: "Considerations of

1056-405: A stand-alone RNN, and each layer's output sequence is used as the input sequence to the layer above. There is no conceptual limit to the depth of stacked RNN. A bidirectional RNN (biRNN) is composed of two RNNs, one processing the input sequence in one direction, and another in the opposite direction. Abstractly, it is structured as follows: The two output sequences are then concatenated to give

1122-993: A total of 160 conferences over the 19 years this program was active; the phrase "Macy conference" does not apply only to those on cybernetics , although it is sometimes used that way informally by those familiar only with that set of events. Disciplinary isolation within medicine was viewed as particularly problematic by the Macy Foundation, and given that their mandate was to aid medical research, they decided to do something about it. Thus other topics covered in different sets of conferences included: aging , adrenal cortex , biological antioxidants , blood clotting , blood pressure , connective tissues , infancy and childhood, liver injury , metabolic interrelations, nerve impulse , problems of consciousness , and renal function . The Josiah Macy, Jr. Foundation developed two innovations specifically designed to encourage and facilitate interdisciplinary and multidisciplinary exchanges; one

1188-772: A wide range of fields. Casual recollections of several participants as well as published comments in the Transactions volumes stress the communicative difficulties in the beginning of each set of conferences, giving way to the gradual establishment of a common language powerful enough to communicate the intricacies of the various fields of expertise present. Participants were deliberately chosen for their willingness to engage in interdisciplinary conversations, or for having formal training in multiple disciplines, and many brought relevant past experiences (gained either from earlier Macy conferences or other venues). As participants became more secure in their ability to understand one another over

1254-449: Is a first-order iterative optimization algorithm for finding the minimum of a function. In neural networks, it can be used to minimize the error term by changing each weight in proportion to the derivative of the error with respect to that weight, provided the non-linear activation functions are differentiable . The standard method for training RNN by gradient descent is the " backpropagation through time " (BPTT) algorithm, which

1320-493: Is a neural network that maps an input x t {\displaystyle x_{t}} into an output y t {\displaystyle y_{t}} , with the hidden vector h t {\displaystyle h_{t}} playing the role of "memory", a partial record of all previous input-output pairs. At each step, it transforms input to an output, and modifies its "memory" to help it to better perform future processing. The illustration to

1386-470: Is a sampling of the topics discussed each year. Some of the researchers present at the cybernetics conferences later went on to do extensive government-funded research on the psychological effects of LSD, and its potential as a tool for interrogation and psychological manipulation in such projects as the CIA 's MKULTRA program. Five annual Neuropharmacological Conferences took place from 1954 to 1959 with

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1452-407: Is a special case of the general algorithm of backpropagation . A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL, which is an instance of automatic differentiation in the forward accumulation mode with stacked tangent vectors. Unlike BPTT, this algorithm is local in time but not local in space. Macy conferences The Macy conferences were

1518-414: Is a three-layer network (arranged horizontally as x , y , and z in the illustration) with the addition of a set of context units ( u in the illustration). The middle (hidden) layer is connected to these context units fixed with a weight of one. At each time step, the input is fed forward and a learning rule is applied. The fixed back-connections save a copy of the previous values of the hidden units in

1584-473: Is an RNN in which all connections across layers are equally sized. It requires stationary inputs and is thus not a general RNN, as it does not process sequences of patterns. However, it guarantees that it will converge. If the connections are trained using Hebbian learning , then the Hopfield network can perform as robust content-addressable memory , resistant to connection alteration. An Elman network

1650-476: Is called "deep LSTM". LSTM can learn to recognize context-sensitive languages unlike previous models based on hidden Markov models (HMM) and similar concepts. Gated recurrent unit (GRU), introduced in 2014, was designed as a simplification of LSTM. They are used in the full form and several further simplified variants. They have fewer parameters than LSTM, as they lack an output gate. Their performance on polyphonic music modeling and speech signal modeling

1716-458: Is preferred to binary encoding of the associative pairs. Recently, stochastic BAM models using Markov stepping were optimized for increased network stability and relevance to real-world applications. A BAM network has two layers, either of which can be driven as an input to recall an association and produce an output on the other layer. Echo state networks (ESN) have a sparsely connected random hidden layer. The weights of output neurons are

1782-496: Is that if the model makes a mistake early on, say at y ^ 2 {\displaystyle {\hat {y}}_{2}} , then subsequent tokens are likely to also be mistakes. This makes it inefficient for the model to obtain a learning signal, since the model would mostly learn to shift y ^ 2 {\displaystyle {\hat {y}}_{2}} towards y 2 {\displaystyle y_{2}} , but not

1848-431: Is the recurrent unit . This unit maintains a hidden state, essentially a form of memory, which is updated at each time step based on the current input and the previous hidden state. This feedback loop allows the network to learn from past inputs, and incorporate that knowledge into its current processing. Early RNNs suffered from the vanishing gradient problem , limiting their ability to learn long-range dependencies. This

1914-458: Is the Hopfield network with random initialization. Sherrington and Kirkpatrick found that it is highly likely for the energy function of the SK model to have many local minima. In the 1982 paper, Hopfield applied this recently developed theory to study the Hopfield network with binary activation functions. In a 1984 paper he extended this to continuous activation functions. It became a standard model for

1980-598: Is to be solved by a seq2seq model. Now, during training, the encoder half of the model would first ingest ( x 1 , x 2 , … , x n ) {\displaystyle (x_{1},x_{2},\dots ,x_{n})} , then the decoder half would start generating a sequence ( y ^ 1 , y ^ 2 , … , y ^ l ) {\displaystyle ({\hat {y}}_{1},{\hat {y}}_{2},\dots ,{\hat {y}}_{l})} . The problem

2046-526: Is to recall the paths which lead it to its goal, it would get stuck in an endless loop chasing its tail. Completely abandoning its goal-oriented design, Shannon's rat had seemingly become neurotic. The Macy Conferences failed to reconcile the subjectivity of information (its meaning) and that of the human mind but succeeding in showing how concepts such as that of the observer, reflexivity , black box systems , and neural networks would have to be approached in conjunction and eventually overcome in order to form

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2112-1065: The Lion-Eating Poet in the Stone Den ), Jan Droogleever-Fortuyn, M. Ericsson, Fitch, Lawrence K. Frank , Ralph Waldo Gerard , William Grey Walter , Molly Harrower , George Evelyn Hutchinson , Heinrich Klüver , Lawrence S. Kubie, Paul Lazarsfeld , Kurt Lewin , J. C. R. Licklider , Howard S. Liddell , Donald B. Lindsley , W. K. Livingston, David Lloyd , Rafael Lorente de Nó , R. Duncan Luce , Donald M. MacKay , Donald G. Marquis , Warren S. McCulloch , Turner McLardy, Margaret Mead , Frederick A. Mettier, Marcel Monnier, Oskar Morgenstern , F. S. C. Northrop , Walter Pitts , Henry Quastler , Antoine Remond, I. A. Richards , David McKenzie Rioch, Arturo Rosenblueth , Leonard J. Savage , T. C. Schneirla , Claude Shannon , John Stroud, Hans-Lukas Teuber , Mottram Torre, Gerhardt von Bonin, Heinz von Foerster , John von Neumann , Heinz Werner, Norbert Wiener , Jerome B. Wiesner , J. Z. Young This

2178-571: The Josiah Macy, Jr. Foundation , motivated by Lawrence K. Frank and Frank Fremont-Smith of the Foundation. As chair of this set of conferences, Warren McCulloch had responsibility to ensure that disciplinary boundaries were crossed. The Cybernetics were particularly complex as a result of bringing together the most diverse group of participants of any of the Macy conferences, so they were

2244-413: The cerebellar cortex formed by parallel fiber , Purkinje cells , and granule cells . In 1933, Lorente de Nó discovered "recurrent, reciprocal connections" by Golgi's method , and proposed that excitatory loops explain certain aspects of the vestibulo-ocular reflex . During 1940s, multiple people proposed the existence of feedback in the brain, which was a contrast to the previous understanding of

2310-453: The gradients of their implementation sometimes become NaN during training for unknown reasons and cause training to fail; report slow convergence; or do not report the speed of learning of their implementation. Differentiable neural computers are an outgrowth of Neural Turing machines, with attention mechanisms that control where the memory is active, and improve performance. Recurrent neural network The building block of RNNs

2376-723: The Effects of Pharmacological Agents on the Over-All Circulation and Metabolism of the Brain" (Seymour Kety) "Functional Organization of the Brain" (Ernest A. Scharrer) "Studies of Electrical Activity of the Brain in Relation to Anesthesia" (Mary A. B. Brazier) "Ascending Reticular System and Anesthesia (Horace W. Magoun) "Observations on New CNS Convulsants" (Carl C. Pfeiffer) The Group Processes Conferences were held between 1954 and 1960. They are of particular interest due to

2442-433: The Macy Conferences, McCulloch proposed that the firing of a neuron can be associated with an event or interaction taking place in the external world which provides sensory stimulus that is then picked up by the nervous system and processed by the neurons . But McCulloch also showed how a neural network's signal pathway could be set up reflexively with itself causing the neurons to continuously fire onto each other in

2508-418: The ability to recall on past experiences, previous paths it had taken around the maze, so as to help it reach its endpoint - which it did repeatedly. Though goal-oriented, Shannon showed how his rat's design was prone to erratic behavior that negated its original function entirely via reflexive feedback loops. If Shannon's rat encountered itself in a path in which its 'memory' failed to fire correctly, that

2574-425: The context of what came before it and what came after it. By stacking multiple bidirectional RNNs together, the model can process a token increasingly contextually. The ELMo model (2018) is a stacked bidirectional LSTM which takes character-level as inputs and produces word-level embeddings. Two RNNs can be run front-to-back in an encoder-decoder configuration. The encoder RNN processes an input sequence into

2640-418: The context units (since they propagate over the connections before the learning rule is applied). Thus the network can maintain a sort of state, allowing it to perform tasks such as sequence-prediction that are beyond the power of a standard multilayer perceptron . Jordan networks are similar to Elman networks. The context units are fed from the output layer instead of the hidden layer. The context units in

2706-846: The course of a set of conferences on a single topic, their willingness to think outside their own specializations meant that creativity increased. The Macy Cybernetics Conferences were preceded by the Cerebral Inhibition Meeting, organized by Frank Fremont-Smith and Lawrence K. Frank, and held on 13–15 May 1942. Those invited were Gregory Bateson , Frank Beach , Carl Binger , Felix Deutsch , Flanders Dunbar , Julie Eisenbud, Carlyla Jacobsen, Lawrence Kubie , Jules Masserman, Margaret Mead , Warren McCulloch , Bela Mittelmann, David Rapoport , Arturo Rosenblueth , Donald Sheehan, Georg Soule, Robert White, John Whitehorn, and Harold Wolff. There were two topics: The Cybernetics conferences were held between 1946 and 1953, organized by

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2772-593: The discussions regarding behavioral patterns of the human mind . Warren McCulloch and Walter Pitts , also attendees, had previously worked on designing the first mathematical schema of a neuron based on the idea that each neuron had a threshold level that was to be reached, via excitation signals from incoming neurons, before firing its own signal onto others. Similarly to how Shannon had previously proven with his work in relay and switch circuits , McCulloch and Pitts proved that neural networks were capable of carrying out any boolean algebra calculations. At

2838-514: The early 2010s. The papers most commonly cited as the originators that produced seq2seq are two papers from 2014. A seq2seq architecture employs two RNN, typically LSTM, an "encoder" and a "decoder", for sequence transduction, such as machine translation. They became state of the art in machine translation, and was instrumental in the development of attention mechanism and Transformer . An RNN-based model can be factored into two parts: configuration and architecture. Multiple RNN can be combined in

2904-417: The element of reflexivity: participants were interested in their own functioning as a group, and made numerous comments about their understanding of how Macy conferences were designed to work. For example, there were a series of jokes made about the disease afflicting them all, interdisciplinitis, or how multidisciplinarian researchers were neither fish nor fowl. When Erving Goffman made a guest appearance at

2970-559: The exchange. The explicit goal was to let a wider audience hear the experts exchange ideas and think out loud about their own work. But even participants themselves found the transactions valuable, as a way to prompt memories, and to catch comments they might have missed. A few comments were made explicitly referring to later publication of the conference discussions, so clearly participants took this into account. However, Fremont-Smith explicitly stated that actual discussion should always take priority. Participants were leading scientists from

3036-403: The information does so relative to their preexisting internal state, consisting of what they already know and have experienced, and only then acts. MacKay further muddled the role of information and its meaning by introducing the idea of reflexivity and feedback loops into his thought experiment. By claiming that the influence of the original message on the initial observer could be perceived by

3102-400: The most difficult to organize and maintain. The principal purpose of these series of conferences was to set the foundations for a general science of the workings of the human mind . These were one of the first organized studies of interdisciplinarity , spawning breakthroughs in systems theory , cybernetics , and what later became known as cognitive science . One of the topics spanning

3168-556: The neural system as a purely feedforward structure. Hebb considered "reverberating circuit" as an explanation for short-term memory. The McCulloch and Pitts paper (1943), which proposed the McCulloch-Pitts neuron model, considered networks that contains cycles. The current activity of such networks can be affected by activity indefinitely far in the past. They were both interested in closed loops as possible explanations for e.g. epilepsy and causalgia . Recurrent inhibition

3234-416: The only part of the network that can change (be trained). ESNs are good at reproducing certain time series . A variant for spiking neurons is known as a liquid state machine . A recursive neural network is created by applying the same set of weights recursively over a differentiable graph-like structure by traversing the structure in topological order . Such networks are typically also trained by

3300-448: The others. Teacher forcing makes it so that the decoder uses the correct output sequence for generating the next entry in the sequence. So for example, it would see ( y 1 , … , y k ) {\displaystyle (y_{1},\dots ,y_{k})} in order to generate y ^ k + 1 {\displaystyle {\hat {y}}_{k+1}} . Gradient descent

3366-462: The reverse mode of automatic differentiation . They can process distributed representations of structure, such as logical terms . A special case of recursive neural networks is the RNN whose structure corresponds to a linear chain. Recursive neural networks have been applied to natural language processing . The Recursive Neural Tensor Network uses a tensor -based composition function for all nodes in

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3432-438: The right may be misleading to many because practical neural network topologies are frequently organized in "layers" and the drawing gives that appearance. However, what appears to be layers are, in fact, different steps in time, "unfolded" to produce the appearance of layers . A stacked RNN , or deep RNN , is composed of multiple RNNs stacked one above the other. Abstractly, it is structured as follows Each layer operates as

3498-667: The row-by-row direction processes an n × n {\displaystyle n\times n} grid of vectors x i , j {\displaystyle x_{i,j}} in the following order: x 1 , 1 , x 1 , 2 , … , x 1 , n , x 2 , 1 , x 2 , 2 , … , x 2 , n , … , x n , n {\displaystyle x_{1,1},x_{1,2},\dots ,x_{1,n},x_{2,1},x_{2,2},\dots ,x_{2,n},\dots ,x_{n,n}} The diagonal BiLSTM uses two LSTMs to process

3564-595: The same grid. One processes it from the top-left corner to the bottom-right, such that it processes x i , j {\displaystyle x_{i,j}} depending on its hidden state and cell state on the top and the left side: h i − 1 , j , c i − 1 , j {\displaystyle h_{i-1,j},c_{i-1,j}} and h i , j − 1 , c i , j − 1 {\displaystyle h_{i,j-1},c_{i,j-1}} . The other processes it from

3630-772: The same input in opposite directions. These two are often combined, giving the bidirectional LSTM architecture. Around 2006, bidirectional LSTM started to revolutionize speech recognition , outperforming traditional models in certain speech applications. They also improved large-vocabulary speech recognition and text-to-speech synthesis and was used in Google voice search , and dictation on Android devices . They broke records for improved machine translation , language modeling and Multilingual Language Processing. Also, LSTM combined with convolutional neural networks (CNNs) improved automatic image captioning . The idea of encoder-decoder sequence transduction had been developed in

3696-531: The study of neural networks through statistical mechanics. Modern RNN networks are mainly based on two architectures: LSTM and BRNN. At the resurgence of neural networks in the 1980s, recurrent networks were studied again. They were sometimes called "iterated nets". Two early influential works were the Jordan network (1986) and the Elman network (1990), which applied RNN to study cognitive psychology . In 1993,

3762-424: The top-right corner to the bottom-left. Fully recurrent neural networks (FRNN) connect the outputs of all neurons to the inputs of all neurons. In other words, it is a fully connected network . This is the most general neural network topology, because all other topologies can be represented by setting some connection weights to zero to simulate the lack of connections between those neurons. The Hopfield network

3828-402: The total output: ( ( y 0 , y 0 ′ ) , ( y 1 , y 1 ′ ) , … , ( y N , y N ′ ) ) {\displaystyle ((y_{0},y_{0}'),(y_{1},y_{1}'),\dots ,(y_{N},y_{N}'))} . Bidirectional RNN allows the model to process a token both in

3894-453: The tree. Neural Turing machines (NTMs) are a method of extending recurrent neural networks by coupling them to external memory resources with which they interact. The combined system is analogous to a Turing machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent . Differentiable neural computers (DNCs) are an extension of Neural Turing machines, allowing for

3960-467: The usage of fuzzy amounts of each memory address and a record of chronology. Neural network pushdown automata (NNPDA) are similar to NTMs, but tapes are replaced by analog stacks that are differentiable and trained. In this way, they are similar in complexity to recognizers of context free grammars (CFGs). Recurrent neural networks are Turing complete and can run arbitrary programs to process arbitrary sequences of inputs. An RNN can be trained into

4026-525: Was acknowledged by Hopfield in his 1982 paper. Another origin of RNN was statistical mechanics . The Ising model was developed by Wilhelm Lenz and Ernst Ising in the 1920s as a simple statistical mechanical model of magnets at equilibrium. Glauber in 1963 studied the Ising model evolving in time, as a process towards equilibrium ( Glauber dynamics ), adding in the component of time. The Sherrington–Kirkpatrick model of spin glass, published in 1975,

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4092-418: Was found to be similar to that of long short-term memory. There does not appear to be particular performance difference between LSTM and GRU. Introduced by Bart Kosko, a bidirectional associative memory (BAM) network is a variant of a Hopfield network that stores associative data as a vector. The bidirectionality comes from passing information through a matrix and its transpose . Typically, bipolar encoding

4158-679: Was oral: the Macy conferences, and one was written: the Macy transactions (published transcriptions of the conferences). Macy conferences were essentially conversations held in a conference setting, with participants presenting research while it was still in process (rather than after it had been completed). These were more formal than conversations (papers were prepared ahead of time and circulated) but less formal than conferences. Macy transactions were transcriptions widely circulated to those who could not attend. These were far more informal than typical proceedings, which publish revised versions of conference papers, and served to invite additional scholars into

4224-472: Was proposed in 1946 as a negative feedback mechanism in motor control. Neural feedback loops were a common topic of discussion at the Macy conferences . See for an extensive review of recurrent neural network models in neuroscience. Frank Rosenblatt in 1960 published "close-loop cross-coupled perceptrons", which are 3-layered perceptron networks whose middle layer contains recurrent connections that change by

4290-466: Was solved by the long short-term memory (LSTM) variant in 1997, thus making it the standard architecture for RNN. RNNs have been applied to tasks such as unsegmented, connected handwriting recognition , speech recognition , natural language processing , and neural machine translation . One origin of RNN was neuroscience. The word "recurrent" is used to describe loop-like structures in anatomy. In 1901, Cajal observed "recurrent semicircles" in

4356-480: Was to be understood  (i.e. a true statement might acquire additional meanings in varied settings though the information exchanged itself has not changed). The addition of meaning into the concept of information necessarily brought the role of the observer into the Macy Conferences. MacKay argued that by receiving and interpreting a message, the observer and the information they perceived ceased to exist independently of one another. The individual reading and processing

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