A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a “chart ” contains a twodimensional attractor set called an “attractor map ” that can be used to represent coordinates in any arbitrary environment, once associative binding has occurred between chart locations and sensory inputs. In hippocampus, there are different spatial relations among place fields in different environments and behavioral contexts. Thus, the same units may participate in many charts, and it is shown that the number of uncorrelated charts that can be encoded in the same recurrent network is potentially quite large. According to this theory, the firing of a given place cell is primarily a cooperative effect of the activity of its

such as the orientation of a line in the visual field or the location of Two main goals for reconstruction are approached in this the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which paper. The first goal is technical and is exemplified by the the physical variables are estimated from observed neural activity. population vector method applied to motor cortical activities Reconstruction is useful first in quantifying how much information during various reaching tasks (Georgopoulos et al. 1986, 1989; about the physical variables is present in the population and, second, Schwartz 1994) and the template matching method applied to in providing insight into how the brain might use distributed represen- disparity selective cells in the visual cortex (Lehky and Sejnowtations in solving related computational problems such as visual ob- ski 1990) and hippocampal place cells during rapid learning of ject recognition and spatial navigation. Two classes of reconstruction place fields in a novel environment (Wilson and McNaughton methods, namely, probabilistic or Bayesian methods and basis func- 1993). In these examples, reconstruction extracts information tion methods, are discussed. They include important existing methods from noisy neuronal population activity and transforms it to a

We report a computer simulation of the visuospatial delayed-response experiments of Funahashi et al. (1989), using a firing-rate model that combines intrinsic cellular bistability with the recurrent local network architecture of the neocortex. In our model, the visuospatial working memory is stored in the form of a continuum of network activity profiles that coexist with a spontaneous activity state. These neuronal firing patterns provide a population code for the cue position in a graded manner. We show that neuronal persistent activity and tuning curves of delay-period activity (memory fields) can be generated by an excitatory feedback circuit and recurrent synaptic inhibition. However, if the memory fields are constructed solely by network mechanisms, noise may induce a random drift over time in the encoded cue position, so that the working memory storage becomes unreliable. Furthermore, a "distraction" stimulus presented during the delay period produces a systematic shift in the encoded cue position. We found that the working memory performance can be rendered robust against noise and distraction stimuli if single neurons are endowed with cellular bistability (presumably due to intrinsic ion channel mechanisms) that is conditional and realized only with sustained synaptic inputs from the recurrent network. We discuss how cellular bistability at the single cell level may be detected by analysis of spike trains recorded during delay-period activity and how local modulation of intrinsic cell properties and/or synaptic transmission can alter the memory fields of individual neurons in the prefrontal cortex.

A simple but powerful modification of the standard Gaussian distribution is studied. The variables of the rectified Gaussian are constrained to be nonnegative, enabling the use of nonconvex energy functions. Two multimodal examples, the competitive and cooperative distributions, illustrate the representational power of the rectified Gaussian. Since the cooperative distribution can represent the translations of a pattern, it demonstrates the potential of the rectified Gaussian for modeling pattern manifolds. 1 INTRODUCTION The rectified Gaussian distribution is a modification of the standard Gaussian in which the variables are constrained to be nonnegative. This simple modification brings increased representational power, as illustrated by two multimodal examples of the rectified Gaussian, the competitive and the cooperative distributions. The modes of the competitive distribution are well-separated by regions of low probability. The modes of the cooperative distribution are closely sp...

Introduction Neuronal activity due to recurrent excitations in the form of a spatially localized pulse or bump has been proposed as a mechanism for feature selectivity in models of the visual system (Somers, Nelson, & Sur, 1995; Hansel & Sompolinsky, 1998), the head direction system (Skaggs, Knieram, Kudrimoti, & McNaughton, 1995; Zhang, 1996; Redish, Elga, & Touretzky, 1996), and working memory (Wilson & Cowan, 1973; Amit & Brunel, 1997; Camperi & Wang, 1998). Many of the previous mathematical formulations of such structures have employedpopulation rate models (Wilson &Cowan, 1972, 1973; Amari, 1977; Kishimoto & Amari, 1979; Hansel & Sompolinsky, 1998). (See Ermentrout, 1998, for a recent review.) Here, we consider a network of spiking neurons that shows such structures and investigate their properties. In our network we #nd localized time-stationary states

Diverse theories of animal navigation aim at explaining how to determine and maintain a course from one place to another in the environment, although each presents a particular perspective with its own terminologies. These vocabularies sometimes overlap, but unfortunately with different meanings. This paper attempts to precisely define the existing concepts and terminologies, so as to comprehensively describe the different theories and models within the same unifying framework. We present navigation strategies within a 4 level hierarchical framework based upon levels of complexity of required processing (Guidance, Place recognition-triggered Response, Topological navigation, Metric navigation). This classification is based upon what information is perceived, represented and processed. It contrasts with common distinctions based upon availability of certain sensors or cues and rather stresses the information structure and content of central processors. We then review computat...

this article. Because there is no visible cue in the hidden-platform water maze task, it would not help the animal find the platform. 3. Route system. Routes stored in the hippocampus can be written out to the cortex, so that directions necessary to reach a goal are associated with local views. This is the system detailed in section 2.5 (see also section 4.3). This system requires training for each step the animal must take; it cannot learn to associate local views with directions to distant goals without hippocampal help (through route replay). The Role of the Hippocampus 97 If there were a way to show the animal the route to the goal, it might be possible to train the route system even without a hippocampus. Whishaw, Cassell, and Jarrard (1995) and Schallert, Day, Weisend, and Sutherland (1996) both showed ways to train the route system directly and found that animals could learn to solve the water maze even with hippocampal lesions. Whishaw et al. (1995) trained animals with fimbria/fornix lesions to find a visible platform and then removed the visible platform. These animals concentrated their search where the platform had been. Schallert et al. (1996) used animals with kainate-colchicine hippocampal lesions. The animals were first trained with a large platform that filled almost the entire maze. Once the animals could reach that platform reliably, it was shrunk trial by trial until it was the same size as a typical platform in a water maze task. Again, the animals could learn to solve the water maze without a hippocampus. 4.3 Where Is the Route System? Although the data are not yet conclusive, we suggest that the most likely candidate for anatomical instantiation of the route system is from posterior parietal to posterior cingulate cortex. There is a lot of evide...

Strong constraints on the neural mechanisms underlying the formation of place fields in the rodent hippocampus come from the systematic changes in spatial activity patterns that are consequent on systematic environmental manipulations. We describe an attractor network model of area CA3 in which local, recurrent, excitatory, and inhibitory interactions generate appropriate place cell representations from location- and directionspecific activity in the entorhinal cortex. In the model, familiarity with the environment, as reflected by activity in neuromodulatory systems, influences the efficacy and plasticity of the recurrent and feedforward inputs to CA3. In unfamiliar, novel, environments, mossy fiber inputs impose activity patterns on CA3, and the recurrent collaterals and the perforant path inputs are subject to graded Hebbian plasticity. The hippocampus is known to be involved in spatial learning and memory in rodents. Some of the most convincing evidence for this is the presence of place cells in areas CA3 and CA1 of the hippocampus (O’Keefe and Dostrovsky, 1971; O’Keefe, 1976) and of many other types of spatially selective cells in neighboring areas

.<F3.733e+05> Head direction (HD) cells, abundant in the rat postsubiculum and anterior thalamic nuclei, fire maximally when the rat's head is facing a particular direction. The activity of a population of these cells forms a distributed representation of the animal's current heading. We describe a neural network model that creates a stable, distributed representation of head direction and updates that representation in response to angular velocity information. In contrast to earlier models, our model of the head direction system accurately tracks a series of actual rat head rotations, and, using biologically plausible neurons, it fits the single-cell tuning curves of real HD cells recorded from rats executing those same rotations. The model makes neurophysiological predictions that can be tested using current technologies.<F3.74e+05> Introduction<F3.733e+05> Head direction cells in the postsubiculum (PoS, also known as dorsal presubiculum) were first described by Ranck<F3.967e+05> et...

In this paper we describe a model that applies Marr's theory of hippocampal function to the problem of map based navigation. Like many others we attribute a spatial memory function to the hippocampus, but we suggest that the additional functional components required for map based navigation are located elsewhere in the brain. One of the key functional components in this model is an egocentric map of space, located in the neocortex, that is continuously updated using ideothetic (self motion) information. The hippocampus stores snapshots of this egocentric map. The modelled activity pattern of head direction cells is used to set the best egocentric map rotation to match the snapshots stored in the hippocampus, resulting in place cells with a non-directional firing pattern. We describe an evaluation of this model using a mobile robot, and demonstrate that with this model the robot can recognise an environment and find a hidden goal. This model is discussed in the context of prior experime...