this article.

A connectionist model of human short-term memory is presented that extends the 'phonological loop' (A.D. Baddeley, 1986) to encompass serial order and learning. Psychological and neuropsychological data motivate separate layers of lexical, timing and input and output phonemic information. Connection weights between layers show Hebbian learning and decay over short and long time scales. At recall, the timing signal is rerun, phonemic information feeds back from output to input and lexical nodes compete to be selected. The selected node then receives decaying inhibition. The model provides an explanatory mechanism for the phonological loop, and for the effects of serial position, presentation modality, lexicality, grouping and Hebb repetition. It makes new psychological and neuropsychological predictions and is a starting point for understanding the role of the phonological loop in vocabulary acquisition and for interpreting data from functional neuroimaging.

This article describes a neural network model of speech motor skill acquisition and speech production that explains a wide range of data on variability, motor equivalence, coarticulation, and rate effects. Model parameters are learned during a babbling phase. To explain how infants learn language-specific variability limits, speech sound targets take the form of convex regions, rather than points, in orosensory coordinates. Reducing target size for better accuracy during slower speech leads to differential effects for vowels and consonants, as seen in experiments previously used as evidence for separate control processes for the 2 sound types. Anticipatory coarticulation arises when targets are reduced in size on the basis of context; this generalizes the well-known look-ahead model of coarticulation. Computer simulations verify the model's properties. The primary goal of the modeling work described in this article is to provide a coherent theoretical framework that provides explanations for a wide range of data concerning the articulator movements used by humans to produce speech sounds. This is carried out by formulating a model that transforms strings of phonemes into continuous articulator movements for

Address correspondence to T.P. The usual description of visual processing in cortex is an extension of the simple to complex hi-erarchy postulated by Hubel and Wiesel — a feedforward sequence of more and more complex and invariant features. The capability of this class of models to perform higher level visual processing such as viewpoint-invariant object recognition in cluttered scenes has been questioned in recent years by several researchers, who in turn proposed an alternative class of models based on the synchro-nization of large assemblies of cells, within and across cortical areas. The main implicit argument for this novel and controversial view was the assumption that hierarchical models cannot deal with the computational requirements of high level vision and suffer from the so-called “binding problem”. We review the present situation and discuss theoretical and experimental evidence showing that the perceived weaknesses of hierarchical models are not true. In particular, we show that recognition of multiple objects in cluttered scenes, arguably among the most difficult tasks in vision, can be done in a hierarchical feedforward model. 1

We report four experiments investigating the effects of repeated and transposed letters in orthographic processing. Orthographically related primes were formed by removing one letter from the target word, by transposing two adjacent letters, or by replacing two adjacent letters with different letters. Robust masked priming in a lexical decision task was found for primes formed by removing a single letter (e.g., mircle-MIRACLE), and this was not influenced by whether or not the prime contained a letter repetition (e.g., balace vs. balnce as a prime for BALANCE). Target words containing a repeated letter tended to be harder to respond to than words without a letter repetition, but the nonwords formed by removing a repeated letter (e.g., BALNCE) were no harder to reject than nonwords formed by removing a non-repeated letter (e.g., MIRCLE, BALACE). Significant transposition priming effects were found for 7-letter words (e.g., sevrice-SERVICE), and these priming effects did not vary as a function of the position of the transposition (initial, final, or inner letter pair). Priming effects disappeared when primes were formed by replacing the two transposed letters with different letters (e.g., sedlice-SERVICE), and fiveletter words only showed priming effects with inner letter transpositions (e.g., Correspondence should be addressed to Jonathan Grainger, Laboratoire de Psychologie

this article, we describe our work to test a simple analytical model that captures several trade-offs governing the performance of visual recognition systems based on spatially invariant conjunctive features. In addition, we introduce a supervised greedy algorithm for feature learning that grows a visual representation in such a way as to minimize false-positive recognition errors. Finally, we consider some of the surprising properties of "good" representations and the implications of our results for more realistic visual recognition problems.

: In this work we discuss Hebb's old ideas about cell assemblies in the light of recent results concerning temporal structure and correlations in neural signals. We want to give a conceptual, necessarily only rough picture, how ideas about `binding by synchronisation', `synfire chains', `local and global assemblies', `short and long term memory' and `behaviour' might be integrated into a coherent model of brain functioning based on neuronal assemblies. Keywords: cell assemblies, synchronization, gamma-oscillations, synfire chains, memory, behaviour 1 ASSEMBLIES AND ASSOCIATIVE MEMORIES 1.1 Cell Assemblies Cell assemblies have been introduced by Donald Hebb with the intention of providing a functional and at the same time structural model for cortical processes and neuronal representations of external events (Hebb, 1949). According to Hebb's ideas, stimuli, objects, things, but also more abstract entities like concepts, contextual relations, ideas, and so on are thought of being repre...

In 1951, Lashley highlighted the importance of serial order for the brain and behavioural sciences. He considered the response chaining account untenable and proposed an alternative employing parallel response activation and "schemata for action". Subsequently, much has been learned about sequential behaviour, particularly in the linguistic domain. We argue that these developments support Lashley's picture, and recent computational models compatible with it are described. The models are developed in a series of steps, beginning with the basic problem of parallel response competition and its possible resolution into serial action. At each stage, important limitations of the previous models are identified and simple additions proposed to overcome them, including the provision of learning mechanisms. Each type of model is compared with relevant data, and the importance of error data is emphasized. Taken together, the models constitute a unified approach to serial order which has achieved considerable explanatory success across disparate domains.

This thesis describes HABLAR, a computational model of the sensorimotor foundations of early childhood phonological development. HABLAR (an acronym for "Hierarchical Articulatory Based Language Acquisition by Reinforcement learning" and Spanish for "to speak") is intended to replicate the major milestones of emerging speech and demonstrate key characteristics of normal development, including the phonetic characteristics of babble, systematic and context-sensitive patterns of sound substitutions and deletions, overgeneralization errors, and the emergence of adult phonemic organization. It should also mimic abnormal phonological development under certain conditions of damage or degradation. HABLAR simulates a complete sensorimotor system consisting of an auditory system that detects and categorizes speech sounds using only acoustic cues drawn from its linguistic environment, an articulatory system that generates synthetic speech based on a realistic computer model of the vocal tract, an...

The interplay between experiments and theoretical approaches can support the exploration of the function of neuronal circuits in the cortex. In this review we exemplify such a proceeding with a study on the functional role of spike timing and gamma-oscillations, and their relation to associative activity feedback through cortex-intrinsical synaptic connections. We first discern the theoretical approaches in general that have been most important in brain research, in particular, those approaches focusing on the biophysical, the functional, and the computational aspect. It is demonstrated how results from computational model studies on different levels of abstraction can constrain the functionality of associative memory expected in real cortical neuronal circuits. These constraints will be used to implement a computational model of associative memory on the base of biophysically elaborated compartmental neurons developed by Pinsky and Rinzel [43]. We run simulation experiments...