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Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses.

Rigotti M, Ben Dayan Rubin D, Wang XJ, Fusi S - Front Comput Neurosci (2010)

Bottom Line: Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states.A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks.Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

View Article: PubMed Central - PubMed

Affiliation: Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University New York, NY, USA.

ABSTRACT
Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

No MeSH data available.


Related in: MedlinePlus

Probability of finding an RCN which implements mixed selectivity as a function of the RCN's firing threshold θ. Different curves correspond to different negative values of the overlap o of the input patterns representing the mental states and the external events.
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FA6: Probability of finding an RCN which implements mixed selectivity as a function of the RCN's firing threshold θ. Different curves correspond to different negative values of the overlap o of the input patterns representing the mental states and the external events.

Mentions: We now consider what happens for values of the overlap o which are even more negative than o < −1/3. This is illustrated in Figure A6.


Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses.

Rigotti M, Ben Dayan Rubin D, Wang XJ, Fusi S - Front Comput Neurosci (2010)

Probability of finding an RCN which implements mixed selectivity as a function of the RCN's firing threshold θ. Different curves correspond to different negative values of the overlap o of the input patterns representing the mental states and the external events.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967380&req=5

FA6: Probability of finding an RCN which implements mixed selectivity as a function of the RCN's firing threshold θ. Different curves correspond to different negative values of the overlap o of the input patterns representing the mental states and the external events.
Mentions: We now consider what happens for values of the overlap o which are even more negative than o < −1/3. This is illustrated in Figure A6.

Bottom Line: Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states.A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks.Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

View Article: PubMed Central - PubMed

Affiliation: Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University New York, NY, USA.

ABSTRACT
Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

No MeSH data available.


Related in: MedlinePlus