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We present a neural model that addresses the capacity of a frontal lobe system to hold up information for short periods of time and to perform response selection. In the model, reverberation states are sustained after stimulus offset, due to loops of recurrent excitation in neural cell assemblies and lateral inhibition is necessary to block an uncontrolled spread of activation. At high levels of inhibition the system performs response selection, and at lower levels it retains a number of units in active states, after stimulus offset. It is shown formally that such a system has capacity limitations: only a limited number of cell assemblies can be retained. Sequential presentation of a list of items is simulated and serial position curves characterised by recency are obtained. The model explains recency, list-length and presentation rate effects in immediate cued recall, as well as semantic effects and patterns of forgetting in Brown-Peterson type of experiments. A reduction in the strength of recurrent excitations explains the absence of lexical effects in tests of immediate memory for frontal lobe patients [1] and more extreme reductions result in impairments of response selection in dynamic aphasia [2].

We present the case for a role of biologically plausible neural network modelling in bridging the gap between physiology and behavior. We argue that spiking level networks can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity (Study 1) to ‘whole system’ behavior in human visual search (Study 2), while also capturing effects at ‘intermediate level’, including performance break down after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data.

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.

We present the case for a role of biologically plausible neural network modelling in bridging the gap between physiology and behavior. We argue that spiking level networks can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity (Study 1) to ‘whole system’ behavior in human visual search (Study 2), while also capturing effects at ‘intermediate level’, including performance break down after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data.

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.

A major challenge to understanding cortical function is the complexity found at both the single-cell and microcircuit levels. Here we outline what is known about the microcircuitry of the CA1 region of the mammalian hippocampus. We then explore the possible functional roles of the variety of neuronal types within this microcircuit during dynamic information processing. This is considered within the framework of CA1 acting as an associative storage device during encoding and retrieval of episodic memories

We present the case for a role of biologically plausible neural network modelling in bridging the gap between physiology and behavior. We argue that spiking level networks can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity (Study 1) to ‘whole system’ behavior in human visual search (Study 2), while also capturing effects at ‘intermediate level’, including performance break down after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data.

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.

We present the case for a role of biologically plausible neural network modelling in bridging the gap between physiology and behavior. We argue that spiking level networks can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity (Study 1) to ‘whole system’ behavior in human visual search (Study 2), while also capturing effects at ‘intermediate level’, including performance break down after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data.

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.

We present the case for a role of biologically plausible neural network modelling in bridging the gap between physiology and behavior. We argue that spiking level networks can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity (Study 1) to ‘whole system’ behavior in human visual search (Study 2), while also capturing effects at ‘intermediate level’, including performance break down after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data.

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.